Accuracy and usability of single-lead ECG from smartphones - A clinical study

Abstract

Background Several wireless ECG devices are commercially available for possible screening, monitoring and diagnosis of rhythms. The field is rapidly expanding, and some devices have demonstrated acceptable qualities. The objective was to evaluate the accuracy, usability and diagnostic capabilities of smartphone ECG in both patients and healthy controls. Methods We used a commercially available smartphone ECG device, connected wirelessly to a tablet, to record a 30-s lead I ECG in 144 subjects—20 of whom repeated the test after vigorous exercise. The subjects included 94 patients under standard calculated 12-lead ECG surveillance; transcripts were obtained shortly after the smartphone ECG was acquired. Results No significant differences were found in the QRS, frequency and QT intervals between the two modalities. Smartphone ECG recordings separated pathologic rhythms (atrial fibrillation (AF)/flutter, atrioventricular block, regular supraventricular rhythm, and pacing) from sinus rhythms with a sensitivity of 0.75 and a specificity of 0.97. The specific diagnosis of AF appeared in 11 patients and was detected with a sensitivity of 1 and a specificity of 0.94. There was a marginal decrease in the interpretability of the smartphone ECG after exercise. Inter- and intraobserver variability was low. Conclusions Smartphone ECG accurately measures most baseline intervals and has acceptable sensitivity and specificity for pathological rhythms, especially for AF. Vigorous activity has a minor influence on the readability of the PR interval. Elderly patients may face challenges in recording a smartphone ECG correctly without assistance. According to our findings, the smartphone ECG would be applicable as a screening device for pathological rhythms.

Evaluation of the QT interval in patients with drug‐induced QT prolongation and torsades de pointes

“…Moreover, two other studies in 381 and 94 subjects without known QT interval prolongation, respectively, showed relevant discrepancies in the QT interval between single lead mobile ECG devices and 12-lead ECGs. 16,17 Malone et al 13 furthermore reported that the QT interval was not measurable in 9% of their study subjects due to low T wave amplitudes. Our results expand the current knowledge and suggest, that the interpretability of the limb leads further decreases with even longer QT intervals in patients experiencing TdP.…”

Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion

“…For single-lead ECG, one of these IoT-based wearable devices has shown many applications, such as the ECG-derived respiration (EDR) rate over past years, using different techniques on features, such as R-amplitude, QRS complex, RS slope, etc., which are sensitive to noise or artifacts during continuous ambulatory monitoring [ 2 ]. On the other hand, a single-lead ECG signal is one of the major candidates for promoting the opportunities of telemonitoring by measuring the heart rate, cardiac rhythm, and even detection of atrial fibrillation (AF) in chronic disease [ 3 , 4 , 5 ]. However, these devices still face some issues, such as power consumption, and a certain degree of inaccuracies, etc.…”

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

“…The most recent advances in portable and wearable medical devices may become highly useful in this premature screening of silent AF, because they are able to significantly increase the monitoring time window where the arrhythmia can be detected. Thus, fresh improvements in low-power embedded systems, communication protocols, and cloud computing technologies have allowed the development of numerous wearable systems with the ability for ECG monitoring over several weeks and even months, while the subject continues a normal active life [ 15 , 16 , 17 , 18 ]. However, these devices will usually work in highly dynamic and changing environments, thus providing ECG signals that are especially prone to be corrupted with different kinds of noises, such as motion artifacts, powerline interference, baseline wander, and high-frequency electromyography disturbances, among others [ 19 ].…”

“…While a broad variety of techniques for ECG quality assessment have been proposed to date [ 20 ], most of them cannot deal with signals acquired via portable or wearable systems. On the one hand, many algorithms have been designed to simultaneously analyze the 12 leads found in the standard ECG, but portable and wearable systems often present a more reduced number of signals, commonly between one and three [ 15 , 16 , 17 , 18 ]. On the other hand, numerous methods raised to assess single-lead ECG quality are based on detecting fiducial points and morphological events in the signal, and then computing parameters such as mean RR interval, ratio of maximum to minimum RR interval, time consistency of PQRST waveforms, coherence of QRS complexes, etc.…”