Construe is a knowledge-based abductive framework for time series interpretation. It provides a knowledge representation model and a set of algorithms for the interpretation of temporal information, implementing a hypothesize-and-test cycle guided by an attentional mechanism. The framework is fully described in the following paper:
[1]: T. Teijeiro and P. Félix: On the adoption of abductive reasoning for time series interpretation, 2016, arXiv:1609.05632.
In this repository you will find the complete implementation of the data model and the algorithms, as well as a knowledge base for the interpretation of multi-lead electrocardiogram (ECG) signals, from the basic waveforms (P, QRS, T) to complex rhythm patterns (Atrial fibrillation, Bigeminy, Trigeminy, Ventricular flutter/fibrillation, etc.). In addition, we provide some utility scripts to reproduce the interpretation of all the ECG strips shown in paper [1], and to allow the interpretation of any ECG record in the MIT-BIH format with a command-line interface very similar to that of the WFDB applications.
Additionally, the repository includes an algorithm for automatic heartbeat classification on ECG signals, described in the paper:
[2]: T. Teijeiro, P. Félix, J.Presedo and D. Castro: Heartbeat classification using abstract features from the abductive interpretation of the ECG
The Construe algorithm is also the basis for the method described in the paper Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records, by T. Teijeiro, C.A. García, D. Castro and P. Félix. This method won First Prize in the Physionet/Computing in Cardiology Challenge 2017, providing the best results in Atrial Fibrillation detection among the 75 participating teams.
This project is implemented in pure python, so no installation is required. However, the core algorithms have strong dependencies with the following python packages:
In addition, the knowledge base for ECG interpretation depends on the following packages:
To support visualization of the interpretation results and the interpretations tree and run the usage examples, the following packages are also needed:
Finally, to read ECG signal records it is necessary to have access to a proper installation of the WFDB software package.
To make easier the installation of Python dependencies, we recommend the Anaconda Python distribution. Alternatively, you can install them using pip with the following command:
~$ pip install -r requirements.txt
Once all the dependencies are satisfied, it is enough to download the project sources and execute the proper python or bash scripts, as explained below. Please note that all our tests are performed on Linux environments, so unexpected issues may arise on Windows or OS-X environments. Please let us know if this is the case.
Along with the general data model for knowledge description and the interpretation algorithms, a comprehensive knowledge base for ECG signal interpretation is provided with the framework, so the software can be directly used as a tool for ECG analysis in multiple abstraction levels.
All signal strips in [1] are included as interactive examples to make it easier to understand how the interpretation algorithms work. For this, use the run_example.sh script, selecting the figure for which you want to reproduce the interpretation process:
./run_example.sh fig4
Once the interpretation is finished, the resulting observations are printed to the terminal, and two interactive figures are shown. One plots the ECG signal with all the observations organized into abstraction levels (deflections, waves, and rhythms), and the other shows the interpretations tree explored to find the result. Each node in the tree can be selected to show the observations at a given time point during the interpretation, allowing to reproduce the abduce, deduce, subsume and predict reasoning steps [1].
Any ECG record in MIT-BIH format can be interpreted with the Construe algorithm. For this, we provide two convenient python modules that may be used as command-line tools. The first one (fragment_processing.py) is intended to visually show the result of the interpretation of a (small) ECG fragment, allowing to inspect and reproduce the interpretation process by navigating through the interpretations tree. But the main one is the (construe_ecg.py) script, which is intended to be used as a production tool that performs background interpretations of full ECG records (or sections). The result is a set of annotations in the MIT format. Both tools try to follow the WFDB Applications command-line interface. The usage of the construe-ecg tool is as follows:
usage: construe_ecg.py [-h] -r record [-a ann] [-o oann]
[--level {conduction,rhythm}] [--exclude-pwaves]
[--exclude-twaves] [-f init] [-t stop] [-l length]
[--overl OVERL] [--tfactor TFACTOR] [-d min_delay]
[-D max_delay] [--time-limit TIME_LIMIT] [-k K] [-v]
[--no-merge]
Interprets a MIT-BIH ECG record in multiple abstraction levels, generating as
a result a set of annotations encoding the observation hypotheses.
optional arguments:
-h, --help show this help message and exit
-r record Name of the record to be processed
-a ann Annotator containing the initial evidence. If not
provided, the gqrs application is used.
-o oann Save annotations as annotator oann (default: iqrs)
--level {conduction,rhythm}
Highest abstraction level used in the interpretation.
Using the "conduction" level produces just a wave
delineation for each QRS annotation in the initial
evidence, while the "rhythm" level also includes a
rhythm interpretation of the full signal, but at the
expense of a higher computational cost in several
orders of magnitude.
--exclude-pwaves Avoids searching for P-waves. Default:False
--exclude-twaves Avoids searching for T-waves. It also implies
--exclude-pwaves Default:False
-f init Begin the interpretation at the "init" time, in
samples
-t stop Stop the interpretation at the "stop" time, in samples
-l length Length in samples of each independently interpreted
fragment. It has to be multiple of 256. Default:23040
if the abstraction level is "rhythm", and 640000 if
the abstraction level is "conduction".
--overl OVERL Length in samples of the overlapping between
consecutive fragments, to prevent loss of information.
If the selected abstraction level is "conduction",
this parameter is ignored. Default: 1080.
--tfactor TFACTOR Time factor to control the speed of the input signal.
For example, if tfactor = 2.0 two seconds of new
signal are added to the signal buffer each real
second. A value of 1.0 simulates real-time online
interpretation. If the selected abstraction level is
"conduction", this parameter is ignored. Default: 1e20
-d min_delay Minimum delay in samples between the acquisition time
and the last interpretation time. If the selected
abstraction level is "conduction", this parameter is
ignored. Default: 2560
-D max_delay Maximum delay in seconds that the interpretation can
be without moving forward. If this threshold is
exceeded, the searching process is pruned. If the
selected abstraction level is "conduction", this
parameter is ignored. Default: 20.0
--time-limit TIME_LIMIT
Interpretation time limit *for each fragment*.If the
interpretation time exceeds this number of seconds,
the interpretation finishes immediately, moving to the
next fragment. If the selected abstraction level is
"conduction", this parameter is ignored. Default:
Infinity
-k K Exploration factor. It is the number of
interpretations expanded in each searching cycle.
Default: 12. If the selected abstraction level is
"conduction", this parameter is ignored.
-v Verbose mode. The algorithm will print to standard
output the fragment being interpreted.
--no-merge Avoids the use of a branch-merging strategy for
interpretation exploration. If the selected
abstraction level is "conduction", this parameter is
Perform a full interpretation of record 100 from the MIT-BIH Arrhythmia Database (the output will be stored in the 100.iqrs annotations file):
$ python construe_ecg.py -r 100
Perform a delineation of the selected heartbeats in the .man annotations file for the record sel30 from the QT database, and storing the result in the sel30.pqt file.
$ python construe_ecg.py -r sel30 -a man -o pqt --level conduction
The same than before, but avoiding P-Wave delineation (only includes QRS complexes and T-waves):
$ python construe_ecg.py -r sel30 -a man -o pqt --level conduction --exclude-pwaves
We will be glad if you want to use Construe to solve problems different from ECG interpretation, and we will help you to do so. The first step is to understand what is under the hood, and the best reference is [1]. After this, you will have to define the Abstraction Model for your problem, based on the Observable and Abstraction Pattern formalisms. As an example, a high-level description of the ECG abstraction model is available in [2], and its implementation is in the knowledge subdirectory. A tutorial is also available in the project wiki.
Once the domain-specific knowledge base has been defined, the fragment_processing.py module should serve as a basis for the execution of the full hypothesize-and-test cycle with different time series and the new abstraction model.
The source code is structured in the following main modules:
acquisition: Modules for the acquisition of the raw time series data. Currently it is highly oriented to ECG data in the MIT-BIH format.inference: Definition of the interpretation algorithms, including the construe algorithm and the reasoning modes (abduce, deduce, subsume, predict and advance) [1].knowledge: Definition of the ECG abstraction model, including observables and abstraction patterns.model: General data model of the framework, including the base class for all observables and classes to implement abstraction grammars as finite automata.utils: Miscellaneous utility modules, including signal processing and plotting routines.
- On windows and OS-X systems, the Dynamic Time Warping utilities included in the
construe.utils.signal_processing.dtwpackage probably won't work. These sources are from the discontinued mlpy project, and should be compiled using cython. The fastest solution is probably to install the mlpy package and change thedtw_stdimport in theconstrue/knowledge/abstraction_patterns/segmentation/QRS.pymodule. - Abductive interpretation of time-series is NP-Hard [1]. This implementation includes several optimizations to make computations feasible, but still the running times are probably longer than you expect if the selected abstraction level is
rhythm. Parameter tuning also help to increase the interpretation speed (usually at the cost of worse-quality results). Also try the-vflag to get feedback and make the wait less painful ;-).
This project is licensed under the terms of the GPL v3 license.