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

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 approaches.This work was partially funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) and the European Social Fund through project TEC2016-80063-C3-3-R (MINECO/AEI/FEDER/UE). MA was supported by the Beca de colaboración 012/2016 UIB fellowship on Information processing in neural and photonic systems. MS was supported by the Spanish Ministerio de Economía, Industria y Competitividad through a Ramón y Cajal Fellowship (RYC-2015-18140). SO was supported by the Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund.Peer reviewe