Accurate Identification of Actionable Single-Lead ECG Data Using a Signal Quality Assessment Algorithm
Year: 2024
Type: conference-paper
Abstract: Accurate identification of high-quality ECG data is crucial for reliable cardiac rhythm assessment, particularly in ambulatory settings.This study presents a machine learning-based signal quality assessment algorithm designed to address the challenges of ECG signal classification in diverse and real-world environments.The algorithm prioritises sensitivity, effectively identifying actionable ECG segments while minimising the risk of discarding critical clinical data.Trained and validated on a wide range of public and proprietary ECG datasets, the algorithm maintains a consistently high sensitivity (>98%) across various device types, patient demographics, and recording environments.Our results highlight the algorithm's utility as a generalisable signal quality triage tool, with the potential to reduce burden on clinicians and enhance the efficiency of ECG analysis in clinical practice.
Source: Computing in cardiology
Authors: Michael Jennings, Jordan Diven, Rebecca Hand, Matthew P. Fitzpatrick, Jonathan Francey, Holly Easlea, Andrew Miller, David Burke
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405435181