Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月6日

查看全文
  • Youngtae Kim,
  • Tae Gwan Jang,
  • So Yeon Park,
  • Ha Young Park,
  • Ji Ae Lee,
  • Tumenbat Oyun-Erdene,
  • Sang-Ha Kim,
  • Young Jun Park,
  • Sung Pil Cho,
  • Junghwan Park,
  • Dongwon Kang,
  • Erdenebayar Urtnasan

摘要

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 (SpO2). 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.