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Classification of falling asleep states using HRV analysis

Piotrowski Z., Szypulska M.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 2

290--301

The article presents the results of studies on drowsiness and drowsiness detection performed using heart rate variability analysis (HRV). The results of those studies indicate that the most significant parameters, from the standpoint of classification of drowsiness are the following parameters of the HRV analysis: the low and high frequency band the ratio of the tachogram power in the LF and HF bands, and the total power distribution. The best detection results were obtained for the following methods, in the following order: the nearest neighborhood with metrics: standardized Euclides and Mahalanobis, the square discriminant analysis, and the Bayesian classifier. In order to classify drowsiness periods, a neural network was also used; it consisted of four inputs, six neurons in the hidden layer, and three outputs, one of which was assigned to one of the accepted classes. In order to obtain the most effective learning, a linear feed forward network was designed using back propagation of errors and the RPROP algorithm. In the case of this type of networks, the achieved accuracy of the individual classes was on the level of 98.7%.

Prediction of fatigue and sleep onset using HRV analysis

Szypulska M., Piotrowski Z.

Elektronika : konstrukcje, technologie, zastosowania

2012

Vol. 53, nr 12

16-19

In the paper objective syndromes associated with sleep onset and fatigue based on the analysis of heart rate variability (HRV) have been presented. Temporal and frequency parameters have been given particular attention. An algorithm for detection of the moment of sleep onset and fatigue has been described. It is based on the determination of the LF/HF ratio on the basis of an RR tachogram and assigning its value to three basic states: activity, drowsiness and sleep. Results of experiments conducted on people without dysfunctions in electrocardiogram (ECG) waveforms have been presented and discussed.

W artykule przedstawiono obiektywne syndromy towarzyszące zasypianiu i zmęczeniu na podstawie analizy Zmienności Rytmu Serca (HRV). Wyszczególniono parametry analizy czasowej i częstotliwościowej. Opisano algorytm detekcji momentu zasypiania i zmęczenia bazujący na wyznaczeniu wartości współczynnika LF/HF na podstawie tachogramu RR i przypisaniu jego wartości do trzech podstawowych stanów: aktywności, senności oraz snu. Przedstawiono i omówiono wyniki eksperymentów przeprowadzonych na osobach bez dysfunkcji przebiegów elektrokardiogramu (EKG).