Synchronised ECG and PPG Data Collection for AI-based QT/QTc Reconstruction (ILECS-ECGPPG)

Collection of Synchronised ECG and PPG Recordings in Healthy Adult Volunteers to Develop and Evaluate AI Methods for ECG- Parameter Reconstruction From PPG (Focus: QT/QTc).

This observational study will collect synchronized photoplethysmography (PPG) and electrocardiogram (ECG) recordings from approximately 50 healthy adult volunteers. Participants will wear the Corsano CardioWatch 297-2 and a reference ECG device simultaneously for approximately 24 hours, including standardized activities and free-lining conditions. The objective is to generate a high-quality paired ECG-PPG dataset to support the development and evaluation of artificial intelligence methods fro reconstructing ECG parameters, with a particular focus on QT/QTc, from PPG signals. The study will also evaluate the feasibility and usability of the wearable monitoring system.

Wearable devices have the potential to support continuous physiological monitoring in both clinical and home settings. Although photoplethysmography (PPG) enables comfortable long-term monitoring, it does not directly provide electrocardiographic (ECG) information. This study aims to establish a synchronized dataset of ECG and PPG recordings that can be used to develop and evaluate artificial intelligence algorithms for reconstructing ECG parameters, particularly QT/QTc, from PPG signals.

Approximately 50 healthy adult volunteers will be enrolled in this single-center, single-arm observational feasibility study. Participants will wear the Corsano CardioWatch 287-2 together with a reference continuous ECG device for approximately 24 hours. During the study, synchronized recordings will be collected during standardized activities (rest, standing, and light walking) as well as during normal daily activities.

The primary endpoints include completeness of paired ECG-PPG recordings, synchronization quality, and the proportion of recordings suitable for QT/QTc annotation. Secondary endpoints include participant comfort and usability of the wearable monitoring system, together with technical performance measures such as signal quality, device connectivity, and data completeness.

The resulting dataset will support future development and validation of AI-based methods for ECG parameter reconstruction from wearable PPG signals.

Inclusion Criteria:

• Able to provide written informed consent.
• Willing and able to comply with all study procedures and attend the required study visits.
• Age ≥18 years.
• Able to read or understand Dutch or English.

Exclusion Criteria:

• Children/minors or vulnerable individuals.
• Skin conditions or allergies preventing safe wear of the CardioWatch or ECG electrodes.
• Any condition that, in the investigator's opinion, may interfere with study completion or data quality.
• Compromised circulation, injury, or physical malformation at sensor sites.
• Known clotting disorders or current prescription anticoagulant therapy.
• Other health conditions preventing safe participation.
• Wrist circumference <14 cm or >23 cm.
• Any participant who, in the investigator's opinion, does not meet study requirements