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Multimodal AI-approach for the automatic screening of cardiovascular diseases based on nocturnal physiological signals

Year: 2025

Type: article

Abstract: 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.

Source: npj Cardiovascular Health

DOI: 10.1038/s44325-025-00051-z