8,000 Holter 24-Hour Ambulatory ECG Recordings for Arrhythmia Burden Quantification

Overview

Our research group is developing an automated arrhythmia-burden analysis pipeline for long-duration ambulatory ECG data. We require a dataset of continuous 24-hour (or longer) Holter recordings collected from adult patients referred for ambulatory cardiac monitoring, covering a broad spectrum of rhythm disturbances including paroxysmal atrial fibrillation, premature ventricular contractions (PVCs), supraventricular ectopy, second- and third-degree AV block, and ventricular tachycardia runs. Technical requirements: recordings must include at minimum two channels (Lead II and a modified V5 derivation); three-channel recordings are preferred. Sampling rate must be ≥200 Hz with amplitude resolution ≥2.5 μV. The preferred file format is EDF or WFDB multi-segment; raw binary exports accompanied by a full header describing gain, offset, and channel labels are acceptable. Beat-level annotations following the AAMI EC57 annotation scheme (N, S, V, F, Q classes) produced by a certified cardiac physiologist and confirmed by a supervising cardiologist are mandatory. Episode-level labels indicating AF burden (percentage of recording time in AF), total PVC count, longest VT run duration, and overall arrhythmia classification are also required. The annotation workflow must enforce dual-reader review: an initial beat-by-beat annotation generated by a validated automated Holter analysis system (GE MARS, Spacelabs Oxford, or equivalent) must be manually reviewed and corrected by a credentialed cardiac physiologist, with a supervising electrophysiologist adjudicating all rhythm episodes exceeding 30 seconds. Inter-annotator reliability metrics, including percentage agreement for N, V, and S class beats across a 5% random re-annotation subset, must be reported and provided alongside the dataset. Label taxonomy must align with AAMI EC57 and EC38 standards to ensure compatibility with benchmark evaluations. De-identification must comply with applicable HIPAA or GDPR requirements. Patient age expressed as completed years at time of recording, sex, body-mass index, primary indication for monitoring (palpitations, syncope, breathlessness, post-ablation follow-up, or hypertension surveillance), and structural heart disease status must be preserved as structured metadata. Free-text diary entries or patient event logs must be reviewed and redacted before delivery; only event timing and general symptom category (palpitation, dizziness, presyncope, chest pain) should be retained. QA exclusion criteria: any 24-hour recording with more than 2 hours of uninterpretable signal due to electrode detachment or severe motion artefact must be flagged; recordings with total annotatable signal below 18 hours are excluded from the primary count but may be included as a supplementary low-quality subset. All data must be de-identified per applicable regulation; patient age, sex, and primary indication for monitoring should be preserved as structured metadata. We have a strong preference for recordings that include patient-activated event markers aligned to symptoms, as these allow supervised training of symptom-correlated arrhythmia models. Institutions contributing ≥500 recordings with complete beat-level annotation will receive priority payment processing. The target use case is a commercial-grade Holter analysis SaaS product currently in FDA Breakthrough Device evaluation, with a secondary research application targeting AF-burden-guided anticoagulation decision support integrated into cardiology electronic health record systems.

Progress

Data Specifications

Use Cases

• Training and validating Sensor / device data AI/ML models
• Benchmarking Sensor / device data detection and segmentation algorithms
• Building de-identified Sensor / device data research datasets for academic studies
• Augmenting existing Sensor / device data datasets to reduce class imbalance