Download Report IEEE DataPort : A Comprehensive Dataset for Arrhythmia Detection Using Wrist-Worn Photoplethysmography (PPG) - 2026
MAT, MD, XLSX by Mahmoud Muhanad Fadhel, Khalida Azudin
Information
Format: MAT, MD, XLSX Publisher: IEEE DataPort Publication Date of the Electronic Edition: 02/24/2026
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ISBN: 10.21227/8rqt-dn89
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Description
This dataset presents a comprehensive collection of wrist-worn photoplethysmography (PPG) signals specifically designed for arrhythmia detection research. The dataset comprises 274 recordings (.mat files) from multiple subjects, featuring simultaneous electrocardiogram (ECG) and wrist PPG measurements acquired using reflectance-mode sensors.Each recording captures approximately 60 seconds of synchronized cardiovascular signals sampled at 256 Hz, providing high-resolution data for detailed analysis of cardiac rhythms. The dataset includes synchronized ECG signals as ground truth references for validating PPG-based arrhythmia detection algorithms, making it particularly valuable for comparative studies between traditional ECG and wearable PPG technology.The data structure consists of matrices containing dual-channel recordings with ECG signals in the first column and wrist PPG signals in the second column, facilitating direct correlation analysis between electrical and optical cardiac measurements. Clinical labels distinguishing normal sinus rhythm from arrhythmic episodes are provided in associated metadata, enabling supervised learning applications for automated arrhythmia classification.This resource addresses the growing need for validated datasets in wearable health monitoring technology, offering researchers an opportunity to develop and benchmark algorithms for continuous cardiac monitoring using consumer-grade wrist-worn devices. The dataset supports various applications including heart rate variability analysis, PPG signal quality assessment, wearable device validation, and machine learning model development for arrhythmia detection.The wrist-based PPG modality represents a practical approach to long-term cardiac monitoring, though it presents unique challenges compared to traditional finger-based measurements due to the anatomical differences and potential susceptibility to motion artifacts. This dataset enables systematic investigation of these challenges and the development of robust signal processing techniques for wrist-based cardiac monitoring.The dataset has undergone quality assurance procedures including visual inspection for artifacts, verification of consistent sampling rates, and validation of temporal synchronization between ECG and PPG channels. It is provided in MATLAB .mat format for compatibility with standard scientific computing environments and includes preprocessing recommendations to facilitate immediate utilization in research applications.
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