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Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle

Biomedical Engineering Frontiers 2022年第1期3卷 159-170页

作者： Alina Dubatovka joachim m.buhmann Department of Computer Science ETH ZurichZurichSwitzerland

Objective and Impact *** fibrillation(AF)is a serious medical condition that requires effective and timely treatment to prevent *** explore deep neural networks(DNNs)for learning cardiac cycles and reliably detecting AF from single-lead electrocardiogram(ECG)*** are widely used for diagnosis of various cardiac dysfunctions including *** huge amount of collected ECGs and recent algorithmic advances to process time-series data with DNNs substantially improve the accuracy of the AF ***,however,are often designed as general purpose black-box models and lack interpretability of their *** design a three-step pipeline for AF detection from ***,a recording is split into a sequence of individual heartbeats based on R-peak *** heartbeats are then encoded using a DNN that extracts interpretable features of a heartbeat by disentangling the duration of a heartbeat from its ***,the sequence of heartbeat codes is passed to a DNN to combine a signal-level representation capturing heart ***,the signal representations are passed to a DNN for detecting *** approach demonstrates a superior performance to existing ECG analysis methods on AF ***,the method provides interpretations of the features extracted from heartbeats by DNNs and enables cardiologists to study ECGs in terms of the shapes of individual heartbeats and rhythm of the whole *** considering ECGs on two levels and employing DNNs for modelling of cardiac cycles,this work presents a method for reliable detection of AF from single-lead ECGs.

关键词： recording Deep enable

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