Construe

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:

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:

The Construe algorithm is also the basis for the arrhythmia classification method described in the following papers:

This method won the First Prize in the Physionet/Computing in Cardiology Challenge 2017, providing the best results in Atrial Fibrillation detection among the 75 participating teams.

Interactive demo examples

All signal strips in [1] are included as interactive examples to make it easier to understand how the interpretation algorithms work. For this, and after installing the optional dependencies described in the [installation](## Installation) section, 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].

In order to support this kind of interactive analysis in other arbitrary (short) ECG fragments, the fragment_processing.py script is provided. Please note that this tool is conceived just to give insights into the abductive interpretation algorithms and to illustrate the adopted reasoning paradigm, and not as a production tool.

Using Construe in other problems and domains

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.