Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals
This study proposes a multimodal AI algorithm called the SleepCVD-Net to automatically screen CVDs based on nocturnal physiological recordings. We designed and implemented a multimodal AI algorithm, SleepCVD-Net, which utilizes three-mode deep neural networks to process input signals-single-lead electrocardiography (ECG), Airflow, and oxygen saturation (SpO<sub>2</sub>). Nocturnal physiological recordings were extracted from 194 subjects (80 controls and 114 subjects with CVD) in the Sleep Heart Health Study database. The proposed SleepCVD-Net model demonstrated good performance, achieving a mean accuracy of 97.55% on the test set. The F1-scores were 97.97%, 96.35%, 97.79%, and 97.49% for the control, stroke, angina, and congestive heart failure groups, respectively. The results indicate the potential for the automatic screening of CVDs based on nocturnal physiological signals. Furthermore, the SleepCVD-Net can serve as a valuable tool for monitoring cardiac activity during sleep in inpatient, outpatient, and home healthcare settings.
Authors
- Erdenebayar Urtnasan (ORCID: https://orcid.org/0000-0002-3493-9724)
- Sung Pil Cho (ORCID: https://orcid.org/0009-0003-8914-7091)
- Dong‐Won Kang (ORCID: https://orcid.org/0000-0002-4818-8108)
- Tae Gwan Jang (ORCID: https://orcid.org/0009-0009-0449-1322)
- Ji Ae Lee
- Jung Hwan Park (ORCID: https://orcid.org/0000-0002-8737-0084)
- Sang‐Ha Kim (ORCID: https://orcid.org/0000-0002-1763-5366)
- Ha Young Park (ORCID: https://orcid.org/0000-0002-7192-2374)
- So Yeon Park (ORCID: https://orcid.org/0000-0002-0299-7268)
- Young Jun Park (ORCID: https://orcid.org/0000-0002-1461-5597)
- Youngtae Kim (ORCID: https://orcid.org/0009-0003-5659-9032)
- Tumenbat Oyun-Erdene
Institutions
- MediaTek (China) (CN)
- Yonsei University (KR)
- Wonju Severance Christian Hospital (KR)
- Woosuk University (KR)
Publication Details
- Journal
- npj Cardiovascular Health
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1038/s44325-025-00051-z
- Citations
- 3
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Korea Medical Device Development Fund
- National Research Foundation
- Ministry of Trade, Industry and Energy
- Ministry of Food and Drug Safety
- National Research Foundation of Korea
- Ministry of Education, India
- Ministry of Science and ICT, South Korea