Health & Environmental Research Online (HERO)


Print Feedback Export to File
7149196 
Journal Article 
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection 
Alfaras, M; Soriano, MC; Ortin, S; , 
2019 
FRONTIERS MEDIA SA 
LAUSANNE 
7 
WOS:000475910600001 
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. 
Echo State Networks; reservoir computing; arrhythmia classification; GPU; ECG