4.ecg.block.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import neurokit2 as nk\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.latex.preamble'] = r'''\\usepackage{libertine}\\usepackage{fontawesome5}\\usepackage[dvipsnames]{xcolor}'''\n",
    "plt.rcParams['ps.usedistiller'] = 'xpdf'\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_ecg(pid):\n",
    "\n",
    "    dfECG = pd.read_csv(f\"./data/ID{pid}-ECG.csv\")\n",
    "    dfState = pd.read_csv(f\"./data/ID{pid}-state.csv\")\n",
    "    dfECG.drop(\"TimeLsl\", axis =1, inplace=True)\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][0]) & (dfECG.Time < dfState['Time'][0+1])].index, 'BlockNumber']   = dfState['BlockNumber'][0]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][2]) & (dfECG.Time < dfState['Time'][2+1])].index, 'BlockNumber']   = dfState['BlockNumber'][2]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][4]) & (dfECG.Time < dfState['Time'][4+1])].index, 'BlockNumber']   = dfState['BlockNumber'][4]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][6]) & (dfECG.Time < dfState['Time'][6+1])].index, 'BlockNumber']   = dfState['BlockNumber'][6]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][8]) & (dfECG.Time < dfState['Time'][8+1])].index, 'BlockNumber']  = dfState['BlockNumber'][8]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][10]) & (dfECG.Time < dfState['Time'][10+1])].index, 'BlockNumber'] = dfState['BlockNumber'][10]\n",
    "    dfECG.loc[dfECG[(dfECG.Time > dfState['Time'][12]) & (dfECG.Time < dfState['Time'][12+1])].index, 'BlockNumber'] = dfState['BlockNumber'][12]\n",
    "    dfECG = dfECG.dropna()\n",
    "    dfECG = dfECG.sort_values(by=\"Time\")\n",
    "    return dfECG\n",
    "\n",
    "def get_block(df, block_number):\n",
    "    return df[df.BlockNumber == block_number]\n",
    "\n",
    "df_all = {}\n",
    "\n",
    "def extract_ecg_features(pid, df):\n",
    "    df_features = pd.DataFrame()\n",
    "    df_current = df\n",
    "\n",
    "    # # H10 Technical Specification\n",
    "    # # Sample rate = 130 Hz ± 2 % (Tamb = +20 … +40 °C)\n",
    "    # #               130 Hz ± 5 % (Tamb = -20 … +70 °C)\n",
    "    # # https://github.com/polarofficial/polar-ble-sdk/blob/master/technical_documentation/H10_ECG_Explained.docx\n",
    "    s, _ = nk.ecg_process(df_current['Value'], sampling_rate=130)\n",
    "    peaks, _ = nk.ecg_peaks(nk.ecg_clean(df_current['Value'], sampling_rate=130),\n",
    "                            sampling_rate=130,\n",
    "                            correct_artifacts=True)\n",
    "\n",
    "    try:\n",
    "        hrv_rmssd = np.mean(nk.hrv(peaks, sampling_rate=130)['HRV_RMSSD'])\n",
    "    except Exception:\n",
    "        hrv_rmssd = np.NaN\n",
    "\n",
    "\n",
    "    df_features = df_features.append(pd.DataFrame.from_dict([{\n",
    "        'pid': pid,\n",
    "        'block_number': df_current['BlockNumber'].iloc[0],\n",
    "        # https://neuropsychology.github.io/NeuroKit/functions/ecg.html?highlight=ecg_rate\n",
    "        'ecg_rate_mean': np.mean(s['ECG_Rate']),\n",
    "        'ecg_rate_var': np.var(s['ECG_Rate']),\n",
    "        # https://neuropsychology.github.io/NeuroKit/examples/ecg_hrv/ecg_hrv.html?highlight=rmssd\n",
    "        'hrv_rmssd': hrv_rmssd,\n",
    "    }]))\n",
    "    return df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23/23 [00:02<00:00,  7.95it/s]\n"
     ]
    }
   ],
   "source": [
    "pid_valid = list(range(1, 24))\n",
    "df_all = {}\n",
    "\n",
    "for pid in tqdm.tqdm(pid_valid):\n",
    "    if pid == 4 or pid == 8 or pid == 10 or pid == 15 or pid == 16 or pid == 17 or pid == 21:\n",
    "        continue\n",
    "    df = load_ecg(pid)\n",
    "    df_all[pid] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current pid:  1\n",
      "current pid:  2\n",
      "current pid:  3\n",
      "current pid:  4\n",
      "current pid:  5\n",
      "current pid:  6\n",
      "current pid:  7\n",
      "current pid:  8\n",
      "current pid:  9\n",
      "current pid:  10\n",
      "current pid:  11\n",
      "current pid:  12\n",
      "current pid:  13\n",
      "current pid:  14\n",
      "current pid:  15\n",
      "current pid:  16\n",
      "current pid:  17\n",
      "current pid:  18\n",
      "current pid:  19\n",
      "current pid:  20\n",
      "current pid:  21\n",
      "current pid:  22\n",
      "current pid:  23\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>block_number</th>\n",
       "      <th>ecg_rate_mean</th>\n",
       "      <th>ecg_rate_var</th>\n",
       "      <th>hrv_rmssd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>817.543733</td>\n",
       "      <td>317.432443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    pid  block_number  ecg_rate_mean  ecg_rate_var   hrv_rmssd\n",
       "0     1           1.0      95.345525    659.755498  268.963986\n",
       "0     1           6.0      95.228705    718.533571  288.091628\n",
       "0     1           4.0      94.828516    760.207311  303.001170\n",
       "0     1           2.0      94.174818    692.298862  287.803725\n",
       "0     1           7.0      92.419487    833.728590  331.050488\n",
       "..  ...           ...            ...           ...         ...\n",
       "0    23           4.0      95.306382    890.665676  316.952605\n",
       "0    23           5.0      93.902097    803.595971  307.356819\n",
       "0    23           7.0      96.710182    850.241299  297.374839\n",
       "0    23           2.0      95.077711    905.353914  350.297737\n",
       "0    23           1.0      96.981867    817.543733  317.432443\n",
       "\n",
       "[112 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "for pid in pid_valid:\n",
    "    print('current pid: ', pid)\n",
    "    if pid == 4 or pid == 8 or pid == 10 or pid == 15 or pid == 16 or pid == 17 or pid == 21:\n",
    "        continue\n",
    "    blocknumbers = df_all[pid].BlockNumber.unique()\n",
    "    for block_number in blocknumbers:\n",
    "        df_current = get_block(df_all[pid], block_number)\n",
    "        df_features = extract_ecg_features(pid, df_current)\n",
    "        df = df.append(df_features)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ecg_rate_mean_norm'] = (df['ecg_rate_mean'] - df['ecg_rate_mean'].min()) / (df['ecg_rate_mean'].max() - df['ecg_rate_mean'].min())\n",
    "df['hrv_rmssd_norm'] = (df['hrv_rmssd'] - df['hrv_rmssd'].min()) / (df['hrv_rmssd'].max() - df['hrv_rmssd'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./processed/ecg_block.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./processed/ecg_block.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
    "colors = sns.color_palette('vlag', n_colors=5)\n",
    "# colors = ['#d81b60', '#1e88e5', '#ffc107', '#26A08C', '#5945c5']\n",
    "sns.boxplot(x='block_number', y='ecg_rate_mean', data=df[(df.block_number != 6)&(df.block_number != 7)],  ax=axes[0], palette=colors, showfliers=False, saturation=1)\n",
    "sns.boxplot(x='block_number', y='hrv_rmssd', data=df[(df.block_number != 6)&(df.block_number != 7)], ax=axes[1], palette=colors, showfliers=False, saturation=1)\n",
    "\n",
    "axes[0].set_ylabel('HR (bpm)',fontsize=18)\n",
    "axes[1].set_ylabel('HRV (ms)',fontsize=18)\n",
    "\n",
    "axes[0].set_xticklabels(['24', '110', '191', '270', '347'], fontsize=18)\n",
    "axes[1].set_xticklabels(['24', '110', '191', '270', '347'], fontsize=18)\n",
    "\n",
    "\n",
    "axes[0].legend().set_visible(False)\n",
    "axes[1].legend().set_visible(False)\n",
    "handles, labels = axes[1].get_legend_handles_labels()\n",
    "\n",
    "axes[0].set_xlabel('Stream (NPCs)', fontsize=18)\n",
    "axes[1].set_xlabel('Stream (NPCs)', fontsize=18)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/ecg_block.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mdpi-data",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "65502a3f73cc00e7b77636c6552b1eb663fb3bdd00b203193cf636158de71b39"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}