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

Biomed Eng Online. 2018 Aug 30;17(1):116. doi: 10.1186/s12938-018-0548-7.


Background: 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.

Methods: 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.

Results: 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 text]). However, S/R was significantly greater on left than those in supine ([Formula: see text]) and on right ([Formula: see text]). The height of T wave and area under T wave were significantly higher in supine than those on right ([Formula: see text]). For the subject specific classifier, a mean accuracy of 97.17% with Cohen's kappa statistic κ of 0.91, and AUC > 0.97 were achieved. While the accuracy and κ dropped to 63.87% and 0.32, AUC > 0.66, respectively when the subject independent classifier was considered.

Conclusions: When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the vectorcardiogram of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom was poor, and the ECG waveform was almost unchanged. The proposed method could be used as a technique for convenient lying position classification.

Keywords: Classification; ECG waveform; Lying position; Random forest; Sleep.

MeSH terms

  • Algorithms*
  • Electrocardiography*
  • Healthy Volunteers*
  • Humans
  • Posture*
  • Signal Processing, Computer-Assisted*
  • Sleep / physiology*