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

Abstract

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.

Figures

Fig. 1

The workflow of this study

Fig. 2

The results after signal preprocessing and character points detection. From left to right, there are P wave origin, P wave peak, P wave end, Q wave peak, R wave peak, S wave peak, T wave origin, T wave peak, T wave end. This part of ECG signal was from No.1 subject, which appeared from 5 h 40 min 11 s 505 ms to 5 h 40 min 13 s 355 ms

Fig. 3

QRS complex area and T wave area calculation

Fig. 4

ECG waveform in 3 lying positions, all from the No.1 subject in the database. Left: from 4 h 3 min 26 s 305 ms to 4 h 3 min 28 s 155 ms. Supine: from 6 h 31 min 47 s 405 ms to 6 h 31 min 49 s 255 ms. Right: from 2 h 18 min 33 s 655 ms to 2 h 18 min 35 s 505 ms

Fig. 5

the workflow of classification method in 3 classification schemes

Fig. 6

The classifier performance of three schemes. Graphs (a–c) are the ROC curves of three kinds of lying position. The red line represents subject specific scheme, green line represents subject independent scheme without features normalization and blue line represents subject independent scheme with features normalization. Bar charts (d–f) present the mean value of AUC, Sensitivity, Specificity and F1-scores of 10 experiments

Fig. 7

In order to verify the absence of overfitting, the learning curve are shown above. The blue line and red line represent the accuracy and Cohen’ K, respectively, of the classification result based on random forest with different proportion of training data

Fig. 8

The comparison of the classification performance between RF, SVM and ANN. We can see that RM and ANN perform better than SVM, and the accuracies of RF and ANN are close. The Cohen’ k of ANN is slightly higher than RF. However, the calculation of RF is much faster. Consequently, RF performs best in general

Fig. 9

The formation of VCG (vectorcardiogram)

Fig. 10

The relation between VCG and ECG