A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

Miquel Alfaras,Silvia Ortín,Miguel C Soriano

doi:10.3389/fphy.2019.00103

Miquel Alfaras, Silvia Ortín + Show 1 more

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https://doi.org/10.3389/fphy.2019.00103

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Abstract

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection appr

Highlights

Electrocardiogram (ECG) analysis has been established at the core of cardiovascular pathology diagnosis since its development in the twentieth century
By loosening the requirements for feature extraction, we propose an implementation fundamentally based on raw signals, single lead information and heart rates that aims at reducing computation time while achieving low error classification results
Besides providing a detailed characterization of the arrhythmia heartbeat classifier based on Echo State Networks (ESNs), our study aims at achieving computational times that allow for real-time processing of ECG data

Summary

Introduction

Electrocardiogram (ECG) analysis has been established at the core of cardiovascular pathology diagnosis since its development in the twentieth century. The ECG signals reflect the electrical activity of the heart. Heart rhythm disorders or alterations in the ECG waveform are evidences of underlying cardiovascular problems, such as arrhythmias. Non-invasive arrhythmia diagnosis is based on the standard 12-lead electrocardiogram, which measures electric potentials from 10 electrodes placed at different parts of the body surface, six in the chest and four in the limbs. In order to provide an effective treatment for arrhythmias, an early diagnosis is important. Detection of certain types of transient, short-term or infrequent arrhythmias requires long-term monitoring (more than 24 h) of the electrical activity of the heart. The fast development of the digital industry has allowed for improvements in devices, data acquisition and computer-aided diagnosis methods

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