Selected Publications
Key research papers validating CardioMood technology and applications.
Validation of Photoplethysmography-Based Heart Rate Variability Measurement Against Electrocardiography in Ambulatory Settings
Mueller, S., Dupont, A., Weber, M., Bernstein, K., Chen, L.
Journal of Biomedical Engineering • Vol. 45, Issue 3
This study validates the accuracy of CardioMood PPG sensors against gold-standard ECG in 156 participants. Results demonstrate 99.2% correlation for R-R interval detection across diverse demographics and activity levels.
Motion Artifact Rejection in Wrist-Worn PPG Sensors: A Novel Adaptive Filtering Approach
Rodriguez, P., Chen, L., Smith, J., Patel, V.
Sensors and Actuators B: Chemical • Vol. 312
Presents a novel adaptive filtering algorithm for motion artifact rejection in wrist-based PPG sensors. Testing on 84 athletes demonstrated 97.8% accuracy maintenance during physical activity.
Long-term Reliability and User Compliance of Consumer-Grade PPG Devices for Continuous HRV Monitoring
Tanaka, K., Johnson, R., Patel, S., Garcia, M., Weber, M.
npj Digital Medicine • Vol. 5, Article 89
A 90-day study of 320 users evaluating measurement stability and device reliability. Results showed 98.5% consistency with <0.5% data loss, supporting the use of PPG for long-term health monitoring.
Comparative Analysis of Commercial HRV Devices for Clinical Research Applications
European Heart Health Consortium
European Heart Journal - Digital Health • Vol. 3, Issue 4
Multi-center comparison of 12 commercial HRV devices against ECG reference across 8 European research centers. CardioMood ranked first in overall accuracy and second in user compliance scores.
Heart Rate Variability as a Biomarker for Workplace Stress: A Corporate Wellness Pilot Study
Williams, A., Brown, T., Johnson, R., Mueller, S.
Journal of Occupational Health Psychology • Vol. 28, Issue 2
Pilot study of 450 corporate employees using continuous HRV monitoring for stress assessment. Results showed significant correlation between HRV metrics and self-reported stress levels, with 32% reduction in perceived stress after intervention.
PPG-Based Sleep Stage Classification Using Deep Learning: Validation Against Polysomnography
Kim, S., Park, J., Chen, L., Rodriguez, P.
Sleep Medicine • Vol. 98
Development and validation of a deep learning model for sleep stage classification using PPG signals. Accuracy of 87% for 4-stage classification (Wake, Light, Deep, REM) compared to polysomnography reference.
Real-time HRV Biofeedback for Stress Management: A Randomized Controlled Trial
Fernandez, M., Smith, J., Tanaka, K., Williams, A.
Applied Psychophysiology and Biofeedback • Vol. 47, Issue 3
RCT of 180 participants comparing HRV biofeedback training to control conditions. The intervention group showed 45% improvement in resting HRV and 28% improvement in sleep quality scores after 8 weeks.
Continuous Remote Monitoring for Early Detection of Cardiovascular Events in Elderly Populations
Garcia, M., Weber, M., Dupont, A., Kim, S.
Journal of the American Geriatrics Society • Vol. 69, Issue 11
Study of 280 elderly adults (65+) using continuous HRV monitoring. Early warning algorithms detected 73% of cardiac events 24-48 hours before clinical presentation, enabling proactive intervention.
Collaborate with us
We actively partner with academic institutions and research organizations to advance the science of heart rate variability and its applications.
- Access to research-grade devices and data
- Technical support and study design consultation
- Co-authorship opportunities on publications
- IRB-ready documentation and protocols
Research Partners Include
And 40+ additional research institutions worldwide