A1 Journal article – refereed
Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms




List of Authors: Tero Hurnanen, Eero Lehtonen, Mojtaba Jafari Tadi, Tom Kuusela, Tuomas Kiviniemi, Antti Saraste, Tuija Vasankari, Juhani Airaksinen, Tero Koivisto, Mikko Pänkäälä
Publisher: IEEE
Publication year: 2017
Journal: IEEE Journal of Biomedical and Health Informatics
Volume number: 21
Issue number: 99
eISSN: 2168-2194

Abstract

In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artefact removal, in total 119 minutes of AFib data and 126 minutes of sinus rhythm data were considered for automated atrial fibrillation detection. No other arrhythmias were considered in this study. The proposed algorithm requires no direct heartbeat peak detection from the SCG data, which makes it tolerant against interpersonal variations in the SCG morphology, and noise. Furthermore, the proposed method relies solely on SCG and needs no complementary electrocardiography (ECG) to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme which takes 5 randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of 99.9% and an average true negative rate of 96.4% for detecting atrial fibrillation in leave-one-out cross-validation. The presented work facilitates adoption of MEMS-based heart monitoring devices for arrhythmia detection.


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Last updated on 2019-21-08 at 22:33