Multimodal apnea detection: advancements through convolutional neural networks and STFT analysis of EEG, ECG, and nasal signals to tackle key challenges in innovation.

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-08 DOI:10.1080/10255842.2025.2510367

Mandana Sadat Ghafourian, Amin Noori, Shaghayegh Taghipour, Amin Ramezani

引用次数: 0

Abstract

Sleep apnea is a prevalent chronic disorder posing significant health risks, requiring prompt diagnosis for effective treatment. This study introduces a reliable and efficient method using simultaneous electroencephalography (EEG), electrocardiography (ECG), and nasal airflow signals to distinguish apnea subtypes. The approach involves signal preprocessing with bandpass filtering and applying the short-time Fourier transform (STFT) to create spectrograms for 30-second segments. A convolutional neural network (CNN) classifies normal events, obstructive sleep apnea (OSA), central sleep apnea (CSA), and hypopnea. Results show our method achieved 98.01% accuracy, highlighting its potential to enhance personalized care for sleep apnea patients.

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多模态呼吸暂停检测：通过卷积神经网络和STFT分析脑电图、心电图和鼻信号来解决创新中的关键挑战。

睡眠呼吸暂停是一种普遍存在的慢性疾病，对健康构成重大威胁，需要及时诊断才能有效治疗。本研究介绍了一种可靠有效的方法，通过同时使用脑电图（EEG）、心电图（ECG）和鼻气流信号来区分呼吸暂停亚型。该方法包括带通滤波的信号预处理，并应用短时傅立叶变换（STFT）创建30秒片段的频谱图。卷积神经网络（CNN）对正常事件、阻塞性睡眠呼吸暂停（OSA）、中枢性睡眠呼吸暂停（CSA）和低通气进行分类。结果表明，该方法的准确率为98.01%，显示了其在提高睡眠呼吸暂停患者个性化护理方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.