Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Krzysztof Gajowniczek,Michał Gostkowski,Iga Grzegorczyk,Tomasz Ząbkowski

doi:10.3390/electronics9030425

Krzysztof Gajowniczek, Michał Gostkowski + Show 2 more

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https://doi.org/10.3390/electronics9030425

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Abstract

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

Highlights

Cardiac arrhythmia refers to a condition in which the heart muscle does not contract in a regular way
In this paper, using the PhysioNet Challenge 2015 dataset gathered for subjects with five different arrhythmia types, we aimed to improve the classification of arrhythmias using blind source separation (BSS) methods applied to neural networks (BSS artificial neural networks (ANNs))
The problem of false arrhythmia alarms in intensive care units (ICUs) is still a demanding task for the algorithms, as there could be a number of potential triggers for false alarms, including noises and device malfunctions that highly influence the analyzed signals and, the model performance

Summary

Introduction

Cardiac arrhythmia refers to a condition in which the heart muscle does not contract in a regular way. One is asystole, where a patient’s heart stops contracting and is inactive for at least 4 s Another is bradycardia, where the heart rate is unnaturally slow—even lower than 40 beats per minute (bpm). The shape of QRS complexes (the combination of three of the graphical deflections seen on a typical electrocardiogram), which represent the depolarization of ventricles, remains mostly unchanged, but the pace of heart contractions in this arrhythmia might exceed 140 bpm. Another type of tachycardia (ventricular tachycardia) occurs when additional electrical impulses are created in ventricles, which causes the heart rate to accelerate

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