ECG signal classification using machine learning techniques
Keywords:electrocardiogram, ECG, ECG classification, one-dimensional convolutional neural networks, 1D CNN
The importance of electrocardiogram (ECG) analysis is difficult to overestimate. Rhythm of life, stress and other factors affect the frequency of diseases and their early appearance. At the same time, the technologization (digitalization) of life and hardware-software complexes, such as mobile electronic cardiographs and wearable devices in general, which are rapidly developing, open new opportunities for rapid analysis of human state by certain indicators, as well as allow to diagnose on the new higher level in almost real time.
There are many methods for analyzing cardiograms. In this paper, the authors propose a new approach based on an ensemble of individual classifiers, which effectively solves the problem of ECG analysis. The study is based on the PhysioNet Computing in Cardiology Challenge 2017 and the MIT-BIH Arrhythmia Database. The algorithm consists of the following stages: data filtering using moving average and Butterworth filters, R-peak localization via threshold and grouping method, ECG resampling for the better comparability, “Noisy” vs “NotNoisy” classification as the most hard-to-identify class, final classification as “Normal”, “Atrial Fibrillation”, “Other” using an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine (SVM).
The proposed method shows high accuracy by the metric F1, so it gives the background for further research, optimization and implementation. This way this algorithm could help to save human’s life by in-time detection of problems with cardiovascular system (CVS) at early stage.
Pages of the article in the issue: 70 - 77
Language of the article: Ukrainian
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