Time-frequency distribution of heart rate variability below 0.05 Hz by Wigner-Ville spectral analysis in congestive heart failure patients

Electrocardiographic features for the measurement of drivers' mental workload

This study examines the effect of mental workload on the electrocardiogram (ECG) of participants driving the Lane Change Task (LCT). Different levels of mental workload were induced by a secondary task (n-back task) with three levels of difficulty. Subjective data showed a significant increase of the experienced workload over all three levels. An exploratory approach was chosen to extract a large number of rhythmical and morphological features from the ECG signal thereby identifying those which differentiated best between the levels of mental workload. No single rhythmical or morphological feature was able to differentiate between all three levels. A group of parameters were extracted which were at least able to discriminate between two levels. For future research, a combination of features is recommended to achieve best diagnosticity for different levels of mental workload.

The analysis of mental stress using time-frequency distribution of heart rate variability signal

Conventional power spectrum methods based on fast Fourier transform (FFT), autoregressive(AR) model are not appropriate for analyzing biomedical signals whose spectral characteristics change rapidly. On the other hand, time-frequency analysis has more desirable characteristics of a time-varying spectrum. In this study, we investigated the spectral components of heart rate variability (HRV) in a time-frequency domain. Then, from the instantaneous frequency, obtained from time-frequency distribution, the method extracting frequency components of HRV was proposed. The subjects were 17 healthy young men. A coin-stacking task was used to induce mental stress. In the results, the emotional stress of subjects produced an increase in sympathetic activity. Sympathetic activation was responsible for the significant increase in the LF/HF ratio. The subjects were divided into two groups with task ability. The subject who had higher mental stress had a lack of task ability.

Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability

We have developed an automatic arrhythmia detection system, which is based on heart rate features only. Initially, the RR interval duration signal is extracted from ECG recordings and segmented into small intervals. The analysis is based on both time and time-frequency (t-f) features. Time domain measurements are extracted and several combinations between the obtained features are used for the training of a set of neural networks. Short time Fourier transform and several time-frequency distributions (TFD) are used in the t-f analysis. The features obtained are used for the training of a set of neural networks, one for each distribution. The proposed approach is tested using the MIT-BIH arrhythmia database and satisfactory results are obtained for both sensitivity and specificity (87.5 and 89.5%, respectively, for time domain analysis and 90 and 93%, respectively, for t-f domain analysis).