Measurements of pulse rate using facial video cameras from smart devices in patients diagnosed with atrial fibrillation.

Clinical applications of passive long-term heart rate (HR) monitoring in patients with cardiac arrhythmias include adequate drug titration of atrioventricular (AV) nodal drugs and assessment of medical compliance with treatment. A majority of patients treated with beta-blockers, especially patients with atrial fibrillation (AF), require some degree of drug titration during the first 6 months of treatment to ensure that adequate HR control and medicine compliance has been achieved. Failing to achieve adequate rate control in patients with AF can lead to worsening symptoms, heart failure exacerbations, and potentially tachycardia-induced cardiomyopathy. Enabling video-based monitoring during telehealth patient visits could facilitate providers to measure heart rate (HR) without the need for a dedicated home device (smartwatch, SPO2 device, or others). Videoplethysmography (VPG) is a monitoring technology that measures pulse rate by utilizing front-facing cameras embedded in smart devices. VPG provides a remote and contactless cardiac monitoring solution. We conducted a clinical experiment to evaluate the accuracy of VPG in measuring HR while running on two portable devices: Samsung S10 smartphones and S3 tablets. We used a single­lead ECG to measure the heart rate at the time of the VPG recordings in AF patients. We employed the Bland-Altman method to measure the level of agreement between videoplethysmography and ECG-based measurements of HR. The findings reveal that the mean difference in videoplethysmography and ECG-based heart rate was inferior to 1 bpm across the 2 devices with confidence intervals ranging from 3 to 12 BPM. Our facial video-based HR monitoring solution could assist providers in measuring heart rates in their patients with AF during remote telehealth visits.

The use of beat-to-beat electrocardiogram analysis to distinguish QT/QTc interval changes caused by moxifloxacin from those caused by vardenafil.

QT correction factors (QTc) can cause errors in the interpretation of drug effects on cardiac repolarization because they do not adequately differentiate changes when heart rate or autonomic state deviates from the baseline QT/RR interval relationship. The purpose of our study was to determine whether the new method of QT interval dynamic beat-to-beat (QTbtb) analysis could better discriminate between impaired repolarization caused by moxifloxacin and normal autonomic changes induced by subtle reflex tachycardia after vardenafil. Moxifloxacin produced maximum mean increases of 13-14 ms in QTbtb, QTcF, and QTcI after 4 h. After vardenafil administration, a 10-ms effect could be excluded at all time points with QTbtb but not with QTcF or QTcI. Subset analysis of the vardenafil upper pharmacokinetic quartile showed that the upper bound of QTcF and QTcI was >10 ms, whereas that of QTbtb was <8 ms. This study demonstrated that newer methods of electrocardiogram (ECG) analysis can differentiate changes in the QT interval to improve identification of proarrhythmia risk.

Static and Dynamic Electrocardiographic Patterns Preceding Torsades de Pointes in the Acquired and Congenital Long QT Syndrome.

The arrhythmogenic mechanisms involved in the triggering of the polymorphic ventricular tachycardia called torsades de pointes (TdPs) remains to be elucidated. In this work, we investigated the static and dynamic profiles of the repolarization interval from the surface electrocardiogram recorded in healthy individuals and in cardiac patients with TdPs. We implemented this analysis just prior to the arrhythmia onset and we computed the delta values based on baseline periods (1 hour prior to event). We measured QT/QTc prolongation, QT variability, ventricular ectopic beats (VPBs) frequency, T-wave amplitude, T-peak to T-end interval, and T-wave complexcity. The analysis of these parameters in reference to baseline revealed 1) an increased QTc variability, 2) the presence of VPCs, and 3) the profound changes in T-loop morphology in patients developing TdPs.

Analysis of T-wave Amplitude Adaptation to Heart Rate Using RR-binning of Long-Term ECG Recordings.

The prognosis of patients with coronary artery disease at the early stage of the disease is a challenge of modern cardiology. There is an urgent need to risk stratify these patients. Holter technology is a cheap and cost effective tool to evaluate electrical abnormalities in the heart. We propose to investigate T-amplitude adaptation to heart rate (HR) using RR-binning. We used daytime recordings from healthy subjects and subjects with acute myocardial infarction (AMI) from the Telemetric and Holter ECG Warehouse. The AMI subjects were divided into two groups based on location of their infarction (group A: anterior or anterior lateral, group B: inferior or inferior lateral). Both AMI groups had acute and stable phase recordings. Population-based T-adaptation to HR was observed for healthy subjects (R2 = 0.92) but was less pronounced for AMI subjects: [Formula: see text].

A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG.

This study compares the ability to preserve information and reduce noise contaminants on the ECG for five wavelet filters and three IIR filters. Two 3-lead Holter ECGs were used. White Gaussian Noise was added to the first ECG in increments of 10% coverage. The second ECG contained alternating muscle transients and noise-free segments. Computation times and SNR improvements for different noise coverages were calculated and compared. RMS errors were calculated from noise-free segments on the ECG with transient muscle noise. Wavelet filters improved SNR more than IIR filters when the signal coverage was more than 50% noise. In contrast, the computation times were shorter for IIR filters (6 s) than for wavelet filters (88 s). On the ECG with transient muscle noise there was a trade-off in performance between wavelet and IIR filtering. In a clinical setting where the amount of noise is unknown, using IIR filters appears to be preferred for consistent performance.

A Wavelet-Based Algorithm for Delineation and Classification of Wave Patterns in Continuous Holter ECG Recordings.

Quantitative analysis of the electrocardiogram (ECG) requires delineation and classification of the individual ECG wave patterns. We propose a wavelet-based waveform classifier that uses the fiducial points identified by a delineation algorithm. For validation of the algorithm, manually annotated ECG records from the QT database (Physionet) were used. ECG waveform classification accuracies were: 85.6% (P-wave), 89.7% (QRS complex), 92.8% (T-wave) and 76.9% (U-wave). The proposed classification method shows that it is possible to classify waveforms based on the points obtained during delineation. This approach can be used to automatically classify wave patterns in long-term ECG recordings such as 24-hour Holter recordings.