ecg_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwE7IDlkth3f",
        "outputId": "54b30c27-d02e-490e-bbe6-e0092407568b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#Importing data from Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_PATH='/content/drive/MyDrive/velozity/'  "
      ],
      "metadata": {
        "id": "sp8DZ87IuZr0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wfdb &> /dev/null\n",
        "!pip install tqdm\n",
        "!pip install biosppy\n",
        "!pip install matplotlib==3.1.3 &> /dev/null"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvXxENbYulya",
        "outputId": "0ecb58a4-f3c0-424d-ad14-65d896ddd4a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (4.64.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting biosppy\n",
            "  Downloading biosppy-1.0.0-py2.py3-none-any.whl (106 kB)\n",
            "\u001b[K     |████████████████████████████████| 106 kB 37.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from biosppy) (1.21.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from biosppy) (3.2.2)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from biosppy) (3.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from biosppy) (1.15.0)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from biosppy) (4.6.0.66)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from biosppy) (1.7.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from biosppy) (1.2.0)\n",
            "Collecting shortuuid\n",
            "  Downloading shortuuid-1.0.9-py3-none-any.whl (9.4 kB)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from biosppy) (1.0.2)\n",
            "Collecting bidict\n",
            "  Downloading bidict-0.22.0-py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->biosppy) (1.5.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->biosppy) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->biosppy) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->biosppy) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->biosppy) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->biosppy) (4.1.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->biosppy) (3.1.0)\n",
            "Installing collected packages: shortuuid, bidict, biosppy\n",
            "Successfully installed bidict-0.22.0 biosppy-1.0.0 shortuuid-1.0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyedflib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8JrzFadv-5F",
        "outputId": "922ac2a5-8faf-4c4e-b68f-0df039383a89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyedflib\n",
            "  Downloading pyEDFlib-0.1.30-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.4 MB 29.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from pyedflib) (1.21.6)\n",
            "Installing collected packages: pyedflib\n",
            "Successfully installed pyedflib-0.1.30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import math\n",
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import wfdb as wf\n",
        "import pyedflib\n",
        "\n",
        "from scipy import signal\n",
        "from biosppy.signals import ecg"
      ],
      "metadata": {
        "id": "1JVLJI3fux-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.utils import resample, class_weight\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout, Input, Activation, Add, Softmax\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from keras.utils.np_utils import to_categorical"
      ],
      "metadata": {
        "id": "ERVTA3Yvu0Wb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(DATA_PATH + 'mitbih_train.csv',header=None)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "jE4qJmRau3xL",
        "outputId": "2e62feb1-be3a-42b3-b938-c041cbd10147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            0         1         2         3         4         5         6    \\\n",
              "0      0.977941  0.926471  0.681373  0.245098  0.154412  0.191176  0.151961   \n",
              "1      0.960114  0.863248  0.461538  0.196581  0.094017  0.125356  0.099715   \n",
              "2      1.000000  0.659459  0.186486  0.070270  0.070270  0.059459  0.056757   \n",
              "3      0.925414  0.665746  0.541436  0.276243  0.196133  0.077348  0.071823   \n",
              "4      0.967136  1.000000  0.830986  0.586854  0.356808  0.248826  0.145540   \n",
              "...         ...       ...       ...       ...       ...       ...       ...   \n",
              "87549  0.807018  0.494737  0.536842  0.529825  0.491228  0.484211  0.456140   \n",
              "87550  0.718333  0.605000  0.486667  0.361667  0.231667  0.120000  0.051667   \n",
              "87551  0.906122  0.624490  0.595918  0.575510  0.530612  0.481633  0.444898   \n",
              "87552  0.858228  0.645570  0.845570  0.248101  0.167089  0.131646  0.121519   \n",
              "87553  0.901506  0.845886  0.800695  0.748552  0.687138  0.599073  0.512167   \n",
              "\n",
              "            7         8         9    ...  178  179  180  181  182  183  184  \\\n",
              "0      0.085784  0.058824  0.049020  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "1      0.088319  0.074074  0.082621  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "2      0.043243  0.054054  0.045946  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "3      0.060773  0.066298  0.058011  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "4      0.089202  0.117371  0.150235  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "...         ...       ...       ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "87549  0.396491  0.284211  0.136842  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "87550  0.001667  0.000000  0.013333  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "87551  0.387755  0.322449  0.191837  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "87552  0.121519  0.118987  0.103797  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "87553  0.427578  0.395133  0.402086  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "\n",
              "       185  186  187  \n",
              "0      0.0  0.0  0.0  \n",
              "1      0.0  0.0  0.0  \n",
              "2      0.0  0.0  0.0  \n",
              "3      0.0  0.0  0.0  \n",
              "4      0.0  0.0  0.0  \n",
              "...    ...  ...  ...  \n",
              "87549  0.0  0.0  4.0  \n",
              "87550  0.0  0.0  4.0  \n",
              "87551  0.0  0.0  4.0  \n",
              "87552  0.0  0.0  4.0  \n",
              "87553  0.0  0.0  4.0  \n",
              "\n",
              "[87554 rows x 188 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6bf94c09-8892-47a9-b3c1-cec42c580522\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>178</th>\n",
              "      <th>179</th>\n",
              "      <th>180</th>\n",
              "      <th>181</th>\n",
              "      <th>182</th>\n",
              "      <th>183</th>\n",
              "      <th>184</th>\n",
              "      <th>185</th>\n",
              "      <th>186</th>\n",
              "      <th>187</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.977941</td>\n",
              "      <td>0.926471</td>\n",
              "      <td>0.681373</td>\n",
              "      <td>0.245098</td>\n",
              "      <td>0.154412</td>\n",
              "      <td>0.191176</td>\n",
              "      <td>0.151961</td>\n",
              "      <td>0.085784</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.049020</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.960114</td>\n",
              "      <td>0.863248</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>0.196581</td>\n",
              "      <td>0.094017</td>\n",
              "      <td>0.125356</td>\n",
              "      <td>0.099715</td>\n",
              "      <td>0.088319</td>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.082621</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659459</td>\n",
              "      <td>0.186486</td>\n",
              "      <td>0.070270</td>\n",
              "      <td>0.070270</td>\n",
              "      <td>0.059459</td>\n",
              "      <td>0.056757</td>\n",
              "      <td>0.043243</td>\n",
              "      <td>0.054054</td>\n",
              "      <td>0.045946</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.925414</td>\n",
              "      <td>0.665746</td>\n",
              "      <td>0.541436</td>\n",
              "      <td>0.276243</td>\n",
              "      <td>0.196133</td>\n",
              "      <td>0.077348</td>\n",
              "      <td>0.071823</td>\n",
              "      <td>0.060773</td>\n",
              "      <td>0.066298</td>\n",
              "      <td>0.058011</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.967136</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.830986</td>\n",
              "      <td>0.586854</td>\n",
              "      <td>0.356808</td>\n",
              "      <td>0.248826</td>\n",
              "      <td>0.145540</td>\n",
              "      <td>0.089202</td>\n",
              "      <td>0.117371</td>\n",
              "      <td>0.150235</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87549</th>\n",
              "      <td>0.807018</td>\n",
              "      <td>0.494737</td>\n",
              "      <td>0.536842</td>\n",
              "      <td>0.529825</td>\n",
              "      <td>0.491228</td>\n",
              "      <td>0.484211</td>\n",
              "      <td>0.456140</td>\n",
              "      <td>0.396491</td>\n",
              "      <td>0.284211</td>\n",
              "      <td>0.136842</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87550</th>\n",
              "      <td>0.718333</td>\n",
              "      <td>0.605000</td>\n",
              "      <td>0.486667</td>\n",
              "      <td>0.361667</td>\n",
              "      <td>0.231667</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.051667</td>\n",
              "      <td>0.001667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87551</th>\n",
              "      <td>0.906122</td>\n",
              "      <td>0.624490</td>\n",
              "      <td>0.595918</td>\n",
              "      <td>0.575510</td>\n",
              "      <td>0.530612</td>\n",
              "      <td>0.481633</td>\n",
              "      <td>0.444898</td>\n",
              "      <td>0.387755</td>\n",
              "      <td>0.322449</td>\n",
              "      <td>0.191837</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87552</th>\n",
              "      <td>0.858228</td>\n",
              "      <td>0.645570</td>\n",
              "      <td>0.845570</td>\n",
              "      <td>0.248101</td>\n",
              "      <td>0.167089</td>\n",
              "      <td>0.131646</td>\n",
              "      <td>0.121519</td>\n",
              "      <td>0.121519</td>\n",
              "      <td>0.118987</td>\n",
              "      <td>0.103797</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87553</th>\n",
              "      <td>0.901506</td>\n",
              "      <td>0.845886</td>\n",
              "      <td>0.800695</td>\n",
              "      <td>0.748552</td>\n",
              "      <td>0.687138</td>\n",
              "      <td>0.599073</td>\n",
              "      <td>0.512167</td>\n",
              "      <td>0.427578</td>\n",
              "      <td>0.395133</td>\n",
              "      <td>0.402086</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>87554 rows × 188 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bf94c09-8892-47a9-b3c1-cec42c580522')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6bf94c09-8892-47a9-b3c1-cec42c580522 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6bf94c09-8892-47a9-b3c1-cec42c580522');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "# normal\n",
        "plt.subplot(2, 3, 1)  \n",
        "plt.plot(data.iloc[0,:186])\n",
        "plt.title('normal beat')\n",
        "# unknown\n",
        "plt.subplot(2, 3, 2)  \n",
        "plt.plot(data.iloc[8310,:186])\n",
        "plt.title(data.iloc[8310,187])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "thlLqqA-vRpT",
        "outputId": "0aea151b-5b0e-432f-9b6e-b9befbe79b67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '0.0')"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[187].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tki50E4GqTl-",
        "outputId": "b1770fa9-3e4a-49fd-e1b1-8daa7fb4ca26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    72471\n",
              "4.0     6431\n",
              "2.0     5788\n",
              "1.0     2223\n",
              "3.0      641\n",
              "Name: 187, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_1 = data[data[187]==1.0]\n",
        "class_2 = data[data[187]==2.0]\n",
        "class_3 = data[data[187]==3.0]\n",
        "class_4 = data[data[187]==4.0]\n",
        "class_0 = data[data[187]==0.0].sample(n = 8000)\n",
        "data = pd.concat([class_1, class_2, class_3, class_4, class_0]).sample(frac=1)"
      ],
      "metadata": {
        "id": "G489BOZeqTig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[187].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6PuRSXBqTgS",
        "outputId": "c26e6ce1-b891-4e04-dbf6-e52bb56e54dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    8000\n",
              "4.0    6431\n",
              "2.0    5788\n",
              "1.0    2223\n",
              "3.0     641\n",
              "Name: 187, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "nq-kA0CFqbwC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset \n",
        "xtrain, xtest, ytrain, ytest = train_test_split(data.drop([187], axis=1), data[187], test_size = 0.1)"
      ],
      "metadata": {
        "id": "NgABqUxcqbsc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain = np.array(xtrain).reshape(xtrain.shape[0], xtrain.shape[1], 1)\n",
        "xtest = np.array(xtest).reshape(xtest.shape[0], xtest.shape[1], 1)"
      ],
      "metadata": {
        "id": "tm36Gj4OqbqT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import Sequential,utils\n",
        "from tensorflow.keras.layers import Flatten, Dense, Conv1D, MaxPool1D, Dropout"
      ],
      "metadata": {
        "id": "8HDwtgobqj43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(filters=32, kernel_size=(3,), padding='same', activation='relu', input_shape = (xtrain.shape[1],1)))\n",
        "model.add(Conv1D(filters=64, kernel_size=(3,), padding='same', activation='relu')) \n",
        "model.add(Conv1D(filters=128, kernel_size=(5,), padding='same', activation='relu')) \n",
        "model.add(Conv1D(filters=256, kernel_size=(5,), padding='same', activation='relu'))    \n",
        "\n",
        "model.add(MaxPool1D(pool_size=(3,), strides=2, padding='same'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units = 512, activation='relu'))\n",
        "model.add(Dense(units = 1024, activation='relu'))\n",
        "model.add(Dense(units = 5, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "LkPfhwpNqbn5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fitted_model = model.fit(xtrain, ytrain, epochs = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVUbdAmHqrmD",
        "outputId": "6caf82db-e96f-4abb-9178-68cc2322ca0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "650/650 [==============================] - 20s 11ms/step - loss: 0.3832 - accuracy: 0.8651\n",
            "Epoch 2/10\n",
            "650/650 [==============================] - 6s 8ms/step - loss: 0.1989 - accuracy: 0.9345\n",
            "Epoch 3/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.1485 - accuracy: 0.9491\n",
            "Epoch 4/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.1177 - accuracy: 0.9595\n",
            "Epoch 5/10\n",
            "650/650 [==============================] - 6s 8ms/step - loss: 0.0975 - accuracy: 0.9669\n",
            "Epoch 6/10\n",
            "650/650 [==============================] - 6s 8ms/step - loss: 0.0811 - accuracy: 0.9708\n",
            "Epoch 7/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.0756 - accuracy: 0.9727\n",
            "Epoch 8/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.0560 - accuracy: 0.9796\n",
            "Epoch 9/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.0555 - accuracy: 0.9801\n",
            "Epoch 10/10\n",
            "650/650 [==============================] - 6s 9ms/step - loss: 0.0474 - accuracy: 0.9820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = model.predict(xtest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4PNd13gqrio",
        "outputId": "52c08efb-8c0f-4f1f-a871-bfe8ac317fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73/73 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1o7SKJ6O5Cc4",
        "outputId": "53621fb5-f45d-4d91-84ab-4b41244045a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.1735840e-01, 4.5592319e-03, 7.2085863e-06, 7.8073680e-02,\n",
              "        1.4685776e-06],\n",
              "       [6.4976713e-13, 8.0360105e-19, 3.5060541e-20, 2.0080961e-27,\n",
              "        1.0000000e+00],\n",
              "       [9.2852218e-15, 3.2198216e-13, 1.0000000e+00, 3.5510184e-12,\n",
              "        1.3018718e-12],\n",
              "       ...,\n",
              "       [4.9428490e-07, 5.7171019e-09, 9.9999738e-01, 1.1292819e-10,\n",
              "        2.1289493e-06],\n",
              "       [9.1679376e-01, 2.0534021e-03, 7.7941105e-02, 1.8546721e-03,\n",
              "        1.3569525e-03],\n",
              "       [9.9928445e-01, 6.6860468e-04, 2.2840523e-09, 2.3528003e-06,\n",
              "        4.4582888e-05]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "metadata": {
        "id": "JbY0cmp3qrf8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = [np.where(i == np.max(i))[0][0] for i in ypred]\n",
        "mat = confusion_matrix(ytest, y)\n",
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "sns.heatmap(mat, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "YiNALqVSqTd-",
        "outputId": "cde8dfb3-93f5-4946-9555-4ddb610ee247"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb77a205c90>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score of the predictions: {0}\".format(accuracy_score(y, ytest)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnELKtbXrrcM",
        "outputId": "25713157-3738-4df5-fac4-8899e12140e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the predictions: 0.9623213512343005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting from an edf file**"
      ],
      "metadata": {
        "id": "z3TWaSnBeQkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = pyedflib.EdfReader(DATA_PATH + \"10-12-27.EDF\")"
      ],
      "metadata": {
        "id": "Fm6R5p2bvRl0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dISwmsCwVgS",
        "outputId": "78cb7cc3-f5c4-4fda-871f-ff50f2b1a88c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pyedflib.edfreader.EdfReader object at 0x8f8f8020>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "signal_labels = f.getSignalLabels() \n",
        "sigbufs = np.zeros((1, f.getNSamples()[0]))\n",
        "for i in np.arange(1):\n",
        "    sigbufs[i, :] = f.readSignal(i)   # Numpy array of the signal values"
      ],
      "metadata": {
        "id": "kYBoKPoawYBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sigbufs)\n",
        "print(sigbufs.shape)\n",
        "print(signal_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gos9UCJNwgv7",
        "outputId": "8dc4c43f-50ee-4844-fbf5-528222ae5227"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   0.   24.   45. ...  -93. -115. -134.]]\n",
            "(1, 68678375)\n",
            "['ECG', 'Marker']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30,8))\n",
        "#fig = plt.figure()\n",
        "ax = plt.axes()\n",
        "for i in np.arange(1):\n",
        "  ax.plot(f.readSignal(i))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "WLJ1Af8BxiA0",
        "outputId": "cafdf834-337e-45ed-f10b-dbdc58cf5f7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f.readSignal(0).shape)\n",
        "print(type(f.readSignal(0)))\n",
        "print(f.getSampleFrequency(0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxRxtMg6yBCB",
        "outputId": "415418b2-84fd-4092-fb78-74719b2d01b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(68678375,)\n",
            "<class 'numpy.ndarray'>\n",
            "125.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = sigbufs\n",
        "data = data[0,0:40000]\n",
        "#data = data[0]\n",
        "data = data.reshape(1,len(data))\n",
        "print(data,\"\\n\", data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvPtZt7FyGfM",
        "outputId": "a6986ad1-2e13-40b2-c1bb-92d103d34841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   0.   24.   45. ... -410. -484. -592.]] \n",
            " (1, 40000)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for channelid, channel in enumerate(data):\n",
        "\n",
        "        out = ecg.ecg(signal=channel, sampling_rate=360, show=False)\n",
        "        rpeaks = np.zeros_like(channel, dtype='float')\n",
        "        rpeaks[out['rpeaks']] = 1.0\n",
        "\n",
        "        print(\"Rpeaks: \", rpeaks)\n",
        "\n",
        "        beatstoremove = np.array([0])\n",
        "        beats = np.split(channel, out['rpeaks'])\n",
        "\n",
        "        for idx, idxval in enumerate(out['rpeaks']):\n",
        "            firstround = idx == 0\n",
        "            lastround = idx == len(beats) - 1\n",
        "\n",
        "            # Skip first and last beat.\n",
        "            if (firstround or lastround):\n",
        "                continue\n",
        "\n",
        "            # Get the classification value that is on\n",
        "            # or near the position of the rpeak index.\n",
        "            fromidx = 0 if idxval < 10 else idxval - 10\n",
        "            toidx = idxval + 10\n",
        "\n",
        "            # Append some extra readings from next beat.\n",
        "            beats[idx] = np.append(beats[idx], beats[idx+1][:40])\n",
        "\n",
        "            # Normalize the readings to a 0-1 range for ML purposes.\n",
        "            beats[idx] = (beats[idx] - beats[idx].min()) / beats[idx].ptp()\n",
        "\n",
        "            # Resample from 360Hz to 125Hz\n",
        "            newsize = int((beats[idx].size * 125 / 360) + 0.5)\n",
        "            beats[idx] = signal.resample(beats[idx], newsize)\n",
        "\n",
        "            # Skipping records that are too long.\n",
        "            if (beats[idx].size > 187):\n",
        "                beatstoremove = np.append(beatstoremove, idx)\n",
        "                continue\n",
        "\n",
        "            # Pad with zeroes.\n",
        "            zerocount = 187 - beats[idx].size\n",
        "            beats[idx] = np.pad(beats[idx], (0, zerocount), 'constant', constant_values=(0.0, 0.0))\n",
        "\n",
        "            # Append the classification to the beat data.\n",
        "            beats[idx] = np.append(beats[idx], 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhRd5_t7yPn4",
        "outputId": "2c59cb92-8c8c-4894-e6d7-cb882710d3f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rpeaks:  [0. 0. 0. ... 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(beats), type(beats))\n",
        "for i in range(10):\n",
        "  print(len(beats[i]), end=\" \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaUrJJEuyjYa",
        "outputId": "44e2c9ee-3dc0-47bb-b744-74a939cb1ad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "312 <class 'list'>\n",
            "212 188 188 188 188 188 188 188 188 188 "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(beatstoremove), type(beatstoremove))\n",
        "print(beatstoremove.shape)\n",
        "print(beatstoremove)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZR8MT687yj_x",
        "outputId": "0ef6df6d-dbab-4a1d-8e7d-0cf69255f5e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 <class 'numpy.ndarray'>\n",
            "(1,)\n",
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "beatstoremove = np.append(beatstoremove, len(beats)-1) #Removing unnecessary beats\n",
        "\n",
        "# Remove first and last beats and the ones without classification.\n",
        "beats = np.delete(beats, beatstoremove)"
      ],
      "metadata": {
        "id": "8mFmWNU4ymo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "savedata = np.array(list(beats[:]), dtype=np.float)\n",
        "outfn = DATA_PATH + 'SampleECG' +'.csv'\n",
        "print('Generating ', outfn)\n",
        "with open(outfn, \"wb\") as fin:\n",
        "    np.savetxt(fin, savedata, delimiter=\",\", fmt='%f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mimaM7y-yrFF",
        "outputId": "9bc211bb-016c-4320-c330-3651f4023d5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating  /content/drive/MyDrive/velozity/SampleECG.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "temp_df = pd.read_csv(DATA_PATH + 'SampleECG.csv', header=None)\n",
        "temp_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "dRGNZLhvVbeo",
        "outputId": "d8a0a6aa-6ced-4722-80e4-3e3a2b56feb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        0         1         2         3         4         5         6    \\\n",
              "0  0.835591  0.582913  0.283711  0.100614  0.191445  0.417653  0.508231   \n",
              "1  0.836627  0.685339  0.364871  0.161041  0.197243  0.438128  0.526883   \n",
              "2  0.540616  0.318634  0.123649  0.005064  0.098388  0.261648  0.303558   \n",
              "3  0.819559  0.691087  0.290314  0.291852  0.250639  0.275365  0.230091   \n",
              "4  0.795307  0.638503  0.323387  0.159662  0.314495  0.518204  0.528231   \n",
              "\n",
              "        7         8         9    ...  178  179  180  181  182  183  184  185  \\\n",
              "0  0.509257  0.544151  0.551948  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "1  0.543419  0.529010  0.511581  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "2  0.297008  0.302816  0.318593  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "3  0.230027  0.202066  0.200728  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "4  0.526996  0.584367  0.587264  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
              "\n",
              "   186  187  \n",
              "0  0.0  0.0  \n",
              "1  0.0  0.0  \n",
              "2  0.0  0.0  \n",
              "3  0.0  0.0  \n",
              "4  0.0  0.0  \n",
              "\n",
              "[5 rows x 188 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20132536-5e2e-4c83-96c7-d18cbdaecf60\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>178</th>\n",
              "      <th>179</th>\n",
              "      <th>180</th>\n",
              "      <th>181</th>\n",
              "      <th>182</th>\n",
              "      <th>183</th>\n",
              "      <th>184</th>\n",
              "      <th>185</th>\n",
              "      <th>186</th>\n",
              "      <th>187</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.835591</td>\n",
              "      <td>0.582913</td>\n",
              "      <td>0.283711</td>\n",
              "      <td>0.100614</td>\n",
              "      <td>0.191445</td>\n",
              "      <td>0.417653</td>\n",
              "      <td>0.508231</td>\n",
              "      <td>0.509257</td>\n",
              "      <td>0.544151</td>\n",
              "      <td>0.551948</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.836627</td>\n",
              "      <td>0.685339</td>\n",
              "      <td>0.364871</td>\n",
              "      <td>0.161041</td>\n",
              "      <td>0.197243</td>\n",
              "      <td>0.438128</td>\n",
              "      <td>0.526883</td>\n",
              "      <td>0.543419</td>\n",
              "      <td>0.529010</td>\n",
              "      <td>0.511581</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.540616</td>\n",
              "      <td>0.318634</td>\n",
              "      <td>0.123649</td>\n",
              "      <td>0.005064</td>\n",
              "      <td>0.098388</td>\n",
              "      <td>0.261648</td>\n",
              "      <td>0.303558</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.302816</td>\n",
              "      <td>0.318593</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.819559</td>\n",
              "      <td>0.691087</td>\n",
              "      <td>0.290314</td>\n",
              "      <td>0.291852</td>\n",
              "      <td>0.250639</td>\n",
              "      <td>0.275365</td>\n",
              "      <td>0.230091</td>\n",
              "      <td>0.230027</td>\n",
              "      <td>0.202066</td>\n",
              "      <td>0.200728</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.795307</td>\n",
              "      <td>0.638503</td>\n",
              "      <td>0.323387</td>\n",
              "      <td>0.159662</td>\n",
              "      <td>0.314495</td>\n",
              "      <td>0.518204</td>\n",
              "      <td>0.528231</td>\n",
              "      <td>0.526996</td>\n",
              "      <td>0.584367</td>\n",
              "      <td>0.587264</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 188 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20132536-5e2e-4c83-96c7-d18cbdaecf60')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20132536-5e2e-4c83-96c7-d18cbdaecf60 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-20132536-5e2e-4c83-96c7-d18cbdaecf60');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df.iloc[0,40]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGH5EURbuZJz",
        "outputId": "1cf2bda4-f4ae-40e3-ca9e-daae99270ff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.629021"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(temp_df[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "T0m9iuBTViOZ",
        "outputId": "607b9555-7c96-4615-ebae-eca9cce9c28b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb764e0e410>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=temp_df.iloc[:,:187]\n",
        "y=temp_df.iloc[:,187]"
      ],
      "metadata": {
        "id": "TAKa_0XguWbA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array(x).reshape(x.shape[0], x.shape[1], 1)\n",
        "prediction = model.predict(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hS-873R3YdK",
        "outputId": "4ac25acb-2724-456f-ca7b-50b6161c38b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 23ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8m8ReDJd49Z7",
        "outputId": "d4180a77-e5a6-4f59-89cd-6d4e4954b1b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.8882931e-01, 5.9034964e-03, 5.0635779e-05, 9.8972869e-06,\n",
              "        5.2065444e-03],\n",
              "       [1.0000000e+00, 3.6425167e-08, 3.9312191e-08, 1.1977735e-10,\n",
              "        1.0637345e-09],\n",
              "       [6.6619956e-01, 5.9354785e-03, 3.2348621e-01, 4.2417315e-03,\n",
              "        1.3698824e-04],\n",
              "       ...,\n",
              "       [9.9737751e-01, 2.1154901e-03, 1.7639823e-04, 3.2787217e-04,\n",
              "        2.7336962e-06],\n",
              "       [1.0000000e+00, 4.3491315e-08, 3.4814956e-10, 3.6574668e-10,\n",
              "        8.1007739e-10],\n",
              "       [9.8559004e-01, 2.6055183e-03, 8.8724289e-03, 2.8967324e-03,\n",
              "        3.5417306e-05]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = [np.where(i == np.max(i))[0][0] for i in prediction]\n",
        "mat = confusion_matrix(y, a)\n",
        "fig, ax = plt.subplots(figsize=(7,7))\n",
        "sns.heatmap(mat, annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "ydLMKmYV4gbg",
        "outputId": "a5c97df9-479e-47c9-bb78-d5479f2198d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb70819cf90>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Gv15SsFF6guP",
        "outputId": "ac12c007-a143-4553-ed99-f0f4b35c07ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb4e2537cd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "# normal\n",
        "plt.subplot(2, 3, 1)  \n",
        "plt.plot(x[1])\n",
        "plt.title('normal beat')\n",
        "# unknown\n",
        "plt.subplot(2, 3, 2)  \n",
        "plt.plot(x[3])\n",
        "plt.title('abnormal beat')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "hSNz66iY6skJ",
        "outputId": "00f979b1-b4a3-44cd-c3e4-338b69541e3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'abnormal beat')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}