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 multiple arrhythmia dataset evaluation database (M.A.D.A.E.).

The convergence between wearable and medical device technologies is a natural progression. Miniaturization has allowed the design of small, compact monitoring systems that can record physiological signals over longer periods of time. Thus, the potential for these devices to expand the understanding of disease progression and patients' clinical status is very high. The accuracy of these devices, however, is dependent upon the computer algorithms utilized in the analysis of the large volume of physiological data monitored and/or recorded by the devices. Automated interpretation of the data by these new technologies, therefore, necessitates closer examination by regulatory organizations. The current requirements for the validation of novel Ambulatory ECG (A-ECG) annotation algorithms are based on the AAMI/ANSI-EC57 and IEC60601-2-47 Standard. These standards are being updated, but they rely on a very limited set of digitized ECG recordings from a couple of ECG databases built in the first half of the 70's. These reference signals are obsolete. We are developing a validation tool for computerized methods designed to detect and monitor cardiac activities based on body-surface ECGs. We will rely on a set of existing digital high-resolution 12­lead A-ECG recordings acquired in cardiac patients and healthy individuals. These ECG signals include a large and unique set of electrocardiographic events. This tool is being qualified by the Center for Devices and Radiological Health of the United States Food and Drug Administration (FDA) as a Medical Device Development Tool (MDDT). This document provides insights into the design of the M.A.D.A.E. database, its functionalities, and its ultimate role in enabling the next generations of automatic interpretation of ECG signals.

Semi-automated QRS score as a predictor of survival in CRT treated patients with strict left bundle branch block.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is widely used for treating selected heart failure patients, but patients with myocardial scar respond worse to treatment. The Selvester QRS scoring system estimates myocardial scar burden using 12-lead ECG. This study's objective was to investigate the scores correlation to mortality in a CRT population. METHODS AND RESULTS: Data on consecutive CRT patients was collected. 401 patients with LBBB and available ECG data were included in the study. QuAReSS software was used to perform Selvester scoring. Mean Selvester score was 6.4, corresponding to 19% scar burden. The endpoint was death or heart transplant; outcome was analyzed using Cox proportional hazards models. A Selvester score >8 was significantly associated with higher risk of the combined endpoint (HR 1.59, p=.014, CI 1.09-2.3). CONCLUSION: Higher Selvester scores correlate to mortality in CRT patients with strict LBBB and might be of value in prognosticating survival.

T-wave morphology can distinguish healthy controls from LQTS patients.

Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400-450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400-450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.

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.

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.

Electrocardiographic method for identifying drug-induced repolarization abnormalities associated with a reduction of the rapidly activating delayed rectifier potassium current.

Several important non-cardiac drugs have been removed from the market after revealing harmful effect that was not identified during prior safety-assessment studies. We developed a new technique for the measurements of repolarization abnormalities from surface ECGs; this method improves sensitivity and specificity of the current technique used to identify the presence of abnormal ion current kinetics in the myocardial cells namely a prolongation of the QT interval on the surface ECG signal. We described in this paper the method and preliminary results, revealing the superiority of our technique that may play a role in the future of drug-safety assessment.