Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals

Abstract

Different from the traditional methods of assessing the cardiac activities through heart rhythm statistics or P-QRS-T complexes separately, this study demonstrates their interactive effects on the power density spectrum (PDS) of ECG signal with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases. Firstly, a mathematical model of the PDS of ECG signal with a random pacing pulse train (PPT) mimicking S-A node firings was derived. Secondly, an experimental PDS analysis was performed on clinical ECG signals from 49 STEMI patients and 42 healthy subjects in PTB Diagnostic Database. It was found that besides the interactive effects which are consistent between theoretical and experimental results, the ECG PDSs of STEMI patients exhibited consistently significant power shift towards lower frequency range in ST-elevated leads in comparison with those of reference leads and leads of health subjects with the highest median frequency shift ratios at 51.39 ± 12.94% found in anterior MI. Thirdly, the results of ECG simulation with systematic changes in PPT firing statistics over various lengths of ECG data ranging from 10s to 60 mins revealed that the mean and median frequency parameters were less affected by the heart rhythm statistics and the data length but more depended on the alterations of P-QRS-T complexes, which were further confirmed on 33 more STEMI patients in European ST-T Database, demonstrating that the frequency indexes could be potentially used as alternative indicators for STEMI diagnosis even with ultrashort-term ECG recordings suitable for wearable and mobile health applications in living-free environments.

Refined Matrix Completion for Spectrum Estimation of Heart Rate Variability

“…Next, the estimated matrix is replaced with H(X) and the iteration is repeated starting from Eq. (10). The estimation is evaluated by ϵ(X) ≜ ||A Ω,H (X) − A Ω,H (H)|| F , and missing entries in matrix H can be approximated until ϵ(X) converges.…”

“…HRV data can be evaluated using a variety of signal analysis methods. For example, HRV measures can be calculated in the time domain, which typically computes statistical indices of HRV data, including the mean value of normal-to-normal RR intervals (NN), root mean square of successive difference of intervals (RMSSD), and standard deviation of NN intervals (SDNN) [10], [11]. Compared to time domain analysis, HRV indices in the frequency domain describe absolute or relative power distribution with different frequency bands of the HRV data.…”

“…This can be due to a number of factors; ( i ) either the ECG or PPG signal is sensitive to measurement noise and uncertainties, e.g., missing values, which would have subsequent effects on the HRV data [17]; ( ii ) the analytical approach and computational procedures present complexities when deriving HRV measures using these signals, such as R peak identification using QRS detectors, RR interval interpolation and resampling to generate the tachogram. These processes could introduce additional estimation errors on HRV data analysis [15]; and ( iii ) the ECG signal is a composition of multiple frequency components that are produced by a variety of physiological processes, and abnormalities in these processes can easily affect the frequency spectrum of HRV data [10].…”

Cardiac–Brain Dynamics Depend on Context Familiarity and Their Interaction Predicts Experience of Emotional Arousal

“…ECG signal is mainly comprised of P-QRS-T complexes and can be considered a composite signal with many frequency components that arise from various physiological processes. Thus, subtle changes in these physiological processes reflect changes in the P-QRS-T complexes during each cardiac cycle and also reflect power at different frequency components of the ECG signal [ 5 ]. The spectral content of the T wave, P wave, and QRS complex is generally within a range from 1 to 10 Hz, 5 to 25 Hz, and 8 to 50 Hz [ 6 ], respectively.…”

“…A recent study linked the power spectrum of ECG with cardiac activity such that the magnitude of frequencies in the power spectrum of ECG depends on HRV. The lower the HRV, the larger the magnitudes of peaks, resulting in more power along with the increasing number of distinct frequency peaks [ 5 ]. Hence, it is likely that subtle changes in cardiac dynamics during the emotional experience could be reflected in the power spectrum of the cardiac signal in the midF range.…”