Accuracy of Wearable ECG Parameter Calculation Method for Long QT and First-Degree A-V Block Detection: A Multi-Center Real-World Study with External Validations Compared to Standard ECG Machines and Cardiologist Assessments

2025-02-21

AI Summary

This study addresses the accuracy challenges in computing key electrocardiographic parameters—PR, QRS, and QT/QTc intervals—and detecting long QT syndrome (LQT) and first-degree atrioventricular block (AVB I) from single-lead wearable ECG signals during real-time, non-invasive monitoring. We propose FeatureDB, an end-to-end parameter estimation algorithm that integrates robust waveform detection with discriminative feature modeling. To our knowledge, this is the first study to conduct external clinical validation of a wearable ECG algorithm on multicenter real-world data, with concurrent benchmarking against standard 12-lead ECG machines and consensus readings by two cardiologists. Results demonstrate excellent agreement with the reference standard for QTc (r > 0.92); LQT detection achieves an AUC of 0.836 (accuracy: 85.6%), and AVB I detection attains an AUC of 0.861 (sensitivity: 87.7%). These performance metrics meet clinical requirements for auxiliary diagnosis, establishing a new benchmark for clinical credibility of wearable ECG-derived parameter estimation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Search and Optimization: Algorithm Configuration

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

In recent years, wearable devices have revolutionized cardiac monitoring by enabling continuous, non-invasive ECG recording in real-world settings. Despite these advances, the accuracy of ECG parameter calculations (PR interval, QRS interval, QT interval, etc.) from wearables remains to be rigorously validated against conventional ECG machines and expert clinician assessments. In this large-scale, multicenter study, we evaluated FeatureDB, a novel algorithm for automated computation of ECG parameters from wearable single-lead signals Three diverse datasets were employed: the AHMU-FH dataset (n=88,874), the CSE dataset (n=106), and the HeartVoice-ECG-lite dataset (n=369) with annotations provided by two experienced cardiologists. FeatureDB demonstrates a statistically significant correlation with key parameters (PR interval, QRS duration, QT interval, and QTc) calculated by standard ECG machines and annotated by clinical doctors. Bland-Altman analysis confirms a high level of agreement.Moreover,FeatureDB exhibited robust diagnostic performance in detecting Long QT syndrome (LQT) and atrioventricular block interval abnormalities (AVBI),with excellent area under the ROC curve (LQT: 0.836, AVBI: 0.861),accuracy (LQT: 0.856, AVBI: 0.845),sensitivity (LQT: 0.815, AVBI: 0.877),and specificity (LQT: 0.856, AVBI: 0.845).This further validates its clinical reliability. These results validate the clinical applicability of FeatureDB for wearable ECG analysis and highlight its potential to bridge the gap between traditional diagnostic methods and emerging wearable technologies.Ultimately,this study supports integrating wearable ECG devices into large-scale cardiovascular disease management and early intervention strategies,and it highlights the potential of wearable ECG technologies to deliver accurate,clinically relevant cardiac monitoring while advancing broader applications in cardiovascular care.

Problem

• Validates wearable ECG parameter calculation accuracy
• Compares wearable ECG with standard ECG machines
• Assesses detection of Long QT and AV block

Innovation

• FeatureDB algorithm for ECG analysis
• Wearable single-lead signal processing
• Multi-center validation with cardiologists