Publications - PulseAI | Peer-Reviewed AI ECG Research

Clinical Evidence

Our technology is backed by 28 peer-reviewed publications in leading cardiology journals including Heart Rhythm, Circulation, European Heart Journal, and Europace, spanning AI-enabled ECG interpretation, arrhythmia detection, wearable diagnostics, and clinical validation.

28 Peer-Reviewed Papers

10+ Journals & Conferences

Journal Article

Accounting for Inconclusive Results and Repeated Testing: A Framework for Evaluating Wearable ECG Diagnostic Performance with Application to Apple Watch and AI-Enhanced Algorithms

P Doggart, C Fisher, P Biglarbeigi, R Bond, D Finlay, D Burke, A Kennedy et al.

Heart Rhythm

Clinical Evidence

Accounting for Inconclusive Results and Repeated Testing: A Framework for Evaluating Wearable ECG Diagnostic Performance with Application to Apple Watch and AI-Enhanced Algorithms

Are single-lead consumer ECG devices enough for accurate QTc monitoring? A calibrated uncertainty AI approach

Uncertainty-Aware AI for QTc Interval Measurement: Validation Using Withings ScanWatch Single-Lead ECGs

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Which estimates can we trust when estimating body surface potential maps from the standard 12-lead ECG? A modeling approach

Clinical Validation of an AI-enabled ECG Holter Analysis Platform

Detecting Atrial and Ventricular Ectopy from the Apple Watch with a Novel Deep Learning Algorithm

Prognostic value of synthetized right-side precordial leads in arrhythmogenic right ventricular cardiomyopathy: data from Nordic, Dutch, and Canadian ARVC registers

Uncertainty Calibrated Deep Regression for QT Interval Measurement in Reduced Lead Set ECGs

Development of an AI-enabled 12-lead ECG interpretation algorithm

Comparison of electrocardiogram (ECG) waveforms and centralized ECG measurements between a wearable mobile ECG device and a standard 12-lead ECG

Clinical validation of an artificial intelligence algorithm offering automated corrected QT-interval measurements from a single lead electrocardiogram

Precordial electrocardiographic recording and QT measurement from a novel wearable ring device

Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm

On the Estimation Performance of Patient-Specific Linear ECG-Lead Transformations in the Presence of Cardiac Conduction Changes

Clinical Validation of a Chest-Worn Personal ECG Device for the Detection of Atrial Fibrillation

Reducing the burden of inconclusive smart-device single-lead ECG tracings via a novel artificial intelligence algorithm

A two-staged classifier to reduce false positives: On device detection of atrial fibrillation using phase-based distribution of Poincaré plots and deep learning

Novel AI algorithm improves the automated detection of atrial arrhythmias from the Apple Watch

Comparison of reduced lead sets in detection of common ECG abnormalities

AI-Enabled ECG Combined with Dry Electrode Sensors for Population-Based Screening of Atrial Fibrillation

Transforming Polysomnography: Time-Frequency Transforms to Visualise and Classify Polysomnography Data

Identifying Noisy ECG Signals in Large Datasets Using a Temporal Convolutional Neural Network Trained to Estimate Pseudo-SNR

Influence of the Training Set Composition on the Estimation Performance of Linear ECG-Lead Transformations

Device agnostic AI-based analysis of ambulatory ECG recordings

Automated measurement of the heart-rate corrected QT-interval using deep learning

Automated identification of label errors in large electrocardiogram datasets

Influence of the Training Set Size on the Subject-to-Subject Variability of the Estimation Performance of Linear ECG-Lead Transformations