Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology.

Components of heart rate variability have attracted considerable attention in psychology and medicine and have become important dependent measures in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability, however, remain complex issues and are fraught with pitfalls. The present report (a) examines the physiological origins and mechanisms of heart rate variability, (b) considers quantitative approaches to measurement, and (c) highlights important caveats in the interpretation of heart rate variability. Summary guidelines for research in this area are outlined, and suggestions and prospects for future developments are considered.

https://www.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1469-8986.1997.tb02140.x

Heart rate variability: Origins, methods, and interpretive caveats

In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

A wavelet-based ECG delineator: evaluation on standard databases

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

/pdf/the-principles-of-software-qrs-detection-1vgts9f7g6.pdf

The principles of software QRS detection

Chronic sleep loss as a consequence of voluntary bedtime restriction is an endemic condition in modern society. Although sleep exerts marked modulatory effects on glucose metabolism, and molecular mechanisms for the interaction between sleeping and feeding have been documented, the potential impact of recurrent sleep curtailment on the risk for diabetes and obesity has only recently been investigated. In laboratory studies of healthy young adults submitted to recurrent partial sleep restriction, marked alterations in glucose metabolism including decreased glucose tolerance and insulin sensitivity have been demonstrated. The neuroendocrine regulation of appetite was also affected as the levels of the anorexigenic hormone leptin were decreased, whereas the levels of the orexigenic factor ghrelin were increased. Importantly, these neuroendocrine abnormalities were correlated with increased hunger and appetite, which may lead to overeating and weight gain. Consistent with these laboratory findings, a growing body of epidemiological evidence supports an association between short sleep duration and the risk for obesity and diabetes. Chronic sleep loss may also be the consequence of pathological conditions such as sleep-disordered breathing. In this increasingly prevalent syndrome, a feedforward cascade of negative events generated by sleep loss, sleep fragmentation, and hypoxia are likely to exacerbate the severity of metabolic disturbances. In conclusion, chronic sleep loss, behavioral or sleep disorder related, may represent a novel risk factor for weight gain, insulin resistance, and Type 2 diabetes.

http://sites.oxy.edu/clint/physio/article/sleeplossanovelriskfactorforinsulinresistance.pdf

Sleep loss: A novel risk factor for insulin resistance and Type 2 diabetes

The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >

A comparison of the noise sensitivity of nine QRS detection algorithms

Heart period variability in sleep

Vagal nerve stimulation is an emerging therapy for epilepsy, yet little is known regarding the effects of this stimulation on heart period variability. We selected 10 patients (two female, eight male) who were receiving high-frequency, high-intensity left vagal nerve stimulation for intractable epilepsy. Electrocardiogram data were recorded for a 7 min baseline, 2.5 min of stimulation and a 7 min post-stimulation period. We found no significant changes in average heart period, instantaneous changes of successive R-to-R intervals greater than 50 ms or fractal dimension. We also found no significant changes in the total power in the 0.0–0.04 Hz, 0.04–0.12 Hz and 0.2–0.4 Hz bands with stimulation of the left vagus nerve. This study suggests that left vagal nerve stimulation has little acute effect on the cardiac rhythm or heart period variability.

Heart period variability during vagal nerve stimulation

Heart rate variability during interictal epileptiform discharges

Production of complete (i.e. continuous and transmural) cardiac lesions by radiofrequency (RF) ablation can cure certain cardiac arrhythmias. However, a predictor of lesion completeness that is reliable and can be measured intraoperatively is needed in order to maximize effectiveness of ablation therapy. Predictors that require membrane excitation or response to stimulation are not always practical. This study tested whether changes of myocardial impedance across the lesion can predict completeness. RF energy was applied epicardially on perfused rabbit ventricles to produce linear lesions that were complete (n = 25) or incomplete (noncontinuous or nontransmural, n = 25). Before and after creation of each lesion, the magnitude and phase of impedance at 1 kHz were measured with a four-electrode epicardial array across the lesion. For 16 of the lesions, the translesion stimulus-excitation delay was also measured. Lesion completeness was evaluated with 2,3,5-triphenyltetrazolium chloride stain. Complete lesions increased resistivity by 26 Omega cm (21% of the preablation value, p = 0.0007, n = 17) when the inactive RF electrode remained on the epicardium during impedance measurements. When the RF electrode was removed during measurements, the rise of resistivity by complete lesions increased to 58 Omega cm (30% of the preablation value, p = 0.022, n = 8). For incomplete lesions, resistivity did not change significantly. Ablation did not significantly alter the phase of impedance. Accuracies of predictions of lesion completeness by the change in resistivity or the change in translesion stimulus-excitation delay were comparable (Youden's index 0.75 and 0.625, respectively, n = 16). Thus, RF ablation increases myocardial resistivity. The resistivity can predict lesion completeness and may provide an alternative to predictors based on excitation.

https://iopscience.iop.org/article/10.1088/0967-3334/29/10/004/pdf

Stephen R. Quint

Papers

A comparison of the noise sensitivity of nine QRS detection algorithms

Heart period variability in sleep

Heart period variability during vagal nerve stimulation

Heart rate variability during interictal epileptiform discharges

Myocardial electrical impedance as a predictor of the quality of RF-induced linear lesions.