Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals - Kim, Youngtae; Jang, Tae Gwan; Park, So Yeon; Park, Ha Young; Lee, Ji Ae; Oyun-Erdene, Tumenbat; Kim, Sang-Ha; Park, Young Jun; Cho, Sung Pil; Park, Junghwan; Kang, Dongwon; Urtnasan, Erdenebayar (NPJ cardiovascular health, 2025)
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 2 ). 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.