Lying position classification based on ECG waveform and random forest during sleep in healthy people

Biomedical Engineering Online
Hongze PanYu Zhang

Abstract

Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved. By means of de-noising the overnight sleep ECG data from ISRUC website dataset, as well as extracting the waveform features, we calculated a total of 30 ECG waveform features, including 2 newly proposed features, S/R and ∠QSR. The means and significant difference level of these features within different lying positions were calculated, respectively. Then 12 features were selected for three kinds of classification schemes. The lying positions had comparatively less effect on time-limit features. QT interval and RR interval were significantly lower than that in supine ([Formula: see text]). Significant differences appeared in most of the amplitude and double-direction features. When lying on the left side, the height of P wave and T wave, QRS area and T area, the QR potential difference and ∠QSR were significantly lower than those in supine ([Formula: see ...Continue Reading

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Citations

Sep 29, 2020·JAMA Internal Medicine·Hoang H NguyenHenry D Huang
May 13, 2020·Biomedical Engineering Online·Jinzhong SongJingsong Li

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Methods Mentioned

BETA
features extraction

Software Mentioned

MATLAB
mathop
TreeBagger

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