Robust detection of heartbeats using association models from blood pressure and EEG signals

Biomedical Engineering Online
Taegyun JeonByeongcheol Lee

Abstract

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect cha...Continue Reading

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Citations

Apr 19, 2017·International Journal of Audiology·Guy Lightfoot
Aug 6, 2021·Biomedical Engineering Letters·Miguel Altuve, Nelson F Monroy

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Software Mentioned

wqrs
PhysioNet
gqrs

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