Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms.
In this paper, a novel method to detect atrial fibrillation (AFib) 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 artifact removal, in total 119 min of AFib data and 126 min of sinus rhythm data were considered for automated AFib 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 the SCG and needs no complementary electrocardiography to be functional. For the considered data, the detection method performs well even on relatively low quality SCG signals. Using a majority voting scheme that takes five randomly selected segments from a signal and classifies these segments using the proposed algorithm, we obtained an average true positive rate of [Formula: see text] and an average true negative rate of [Formula: see text] for detecting AFib in leave-one-out cross-validation. This paper facilitates adoption of microelectromechanical sensor based heart monitoring devices for arrhythmia detection.
Tero
T
Hurnanen
Eero
E
Lehtonen
Mojtaba Jafari
MJ
Tadi
Tom
T
Kuusela
Tuomas
T
Kiviniemi
Antti
A
Saraste
Tuija
T
Vasankari
Juhani
J
Airaksinen
Tero
T
Koivisto
Mikko
M
Pankaala
D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D007702:Kinetocardiography / Q000379:methods*; D008297:Male; D012815:Signal Processing, Computer-Assisted