ArticleIndian pacing and electrophysiology journal
Accuracy and usability of single-lead ECG from smartphones - A clinical study.
Article in Indian pacing and electrophysiology journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2021Pooled it
- Clinical performance of an atrial fibrillation burden tracking algorithm: Evaluation against reference public and clinical textile electrocardiographic datasets.Heart rhythm O2 · 2026Article
- Clinical Evaluation of the ECG Noise Extraction Tool as a Component of ECG Analysis Algorithms Evaluation.IEEE transactions on bio-medical engineering · 2025Article
- Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives.Cureus · 2025Review
- Classification feasibility test on multi-lead electrocardiography signals generated from single-lead electrocardiography signals.Scientific reports · 2024Article
- Validity of MENARI plus (self-pulse assessment and clinical scoring) mobile apps for detecting atrial fibrillation in high-risk population.Journal of arrhythmia · 2023Article
- Smart devices to measure and monitor QT intervals.Frontiers in cardiovascular medicine · 2023Review
- Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting.Frontiers in cardiovascular medicine · 2023Article
- How to use digital devices to detect and manage arrhythmias: an EHRA practical guide.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.Sensors (Basel, Switzerland) · 2022Article
- Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization.Scientific reports · 2021Article
- Article
- Design of Smart Steering Wheel for Unobtrusive Health and Drowsiness Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs.Sensors (Basel, Switzerland) · 2021Article
- Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral 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.
methodsWe 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.
resultsNo 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.
conclusionsSmartphone 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.
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