基于声学信息检测阻塞性睡眠呼吸暂停的研究进展

doi:10.11958/20250966

天津医药 ›› 2025, Vol. 53 ›› Issue (7): 776-784.doi: 10.11958/20250966

• 综述 • 上一篇

基于声学信息检测阻塞性睡眠呼吸暂停的研究进展

余辉¹(), 刘浩¹^,², 蔡凤丽³, 赵婧¹, 白相森¹, 田国梁¹, 张含悦¹, 张丽媛⁴^,^△()

1 天津大学生物医学工程系（邮编300072）
2 天津市第四中心医院网络信息科（邮编300072）
3 山东省巨野县中医医院妇产科（邮编300072）
4 天津市第四中心医院设备物资科（邮编300072）

收稿日期:2025-03-10 修回日期:2025-05-05 出版日期:2025-07-15 发布日期:2025-07-21
通讯作者: ^△E-mail：13752631906@163.com
作者简介:余辉（1976），男，副教授，主要从事生物医学信号处理方面研究。E-mail：yuhui@tju.edu.cn

New advances on detecting obstructive sleep apnea based on acoustic information

YU Hui¹(), LIU Hao¹^,², CAI Fengli³, ZHAO Jing¹, BAI Xiangsen¹, TIAN Guoliang¹, ZHANG Hanyue¹, ZHANG Liyuan⁴^,^△()

1 Department of Biomedical Engineering, Tianjin University, Tianjin 300072, China
2 Department of Network Information, Tianjin 4th Center Hospital, Tianjin 300072, China
3 Department of Obstetrics and Gynecology, Juye County Hospital of Traditional Chinese Medicine, Tianjin 300072, China
4 Department of Equipment and Material, Tianjin 4th Centre Hospital, Tianjin 300072, China

Received:2025-03-10 Revised:2025-05-05 Published:2025-07-15 Online:2025-07-21
Contact: ^△E-mail：13752631906@163.com

余辉, 刘浩, 蔡凤丽, 赵婧, 白相森, 田国梁, 张含悦, 张丽媛. 基于声学信息检测阻塞性睡眠呼吸暂停的研究进展[J]. 天津医药, 2025, 53(7): 776-784.

YU Hui, LIU Hao, CAI Fengli, ZHAO Jing, BAI Xiangsen, TIAN Guoliang, ZHANG Hanyue, ZHANG Liyuan. New advances on detecting obstructive sleep apnea based on acoustic information[J]. Tianjin Medical Journal, 2025, 53(7): 776-784.

摘要/Abstract

摘要：

阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，表现为睡眠时反复发生上气道塌陷和阻塞。多导睡眠监测是OSA诊断的金标准，但价高、耗时且易引起患者不适。近年来，基于声学信息检测OSA的方法逐渐成为研究热点。该文就近年来基于鼾声和语音信号的OSA自动检测技术最新研究进展进行综述，系统梳理了其在诊断、严重程度判断及阻塞部位识别方面的应用。鼾声和语音信号的声学特征在OSA筛查中具有重要价值，结合机器学习与深度学习模型可实现较高准确度。未来研究应重点关注声学特征与病理生理机制的关联、多模态信息融合及可穿戴设备的临床应用，以推动OSA智能化、无创化、低成本筛查技术的发展。

关键词: 睡眠呼吸暂停, 阻塞性, 打鼾, 语音, 检测, 阻塞位置, 严重程度

Abstract:

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep. Polysomnography is the gold standard for diagnosing OSA, but it is expensive, time-consuming, and can cause discomfort for patients. In recent years, acoustic-based approaches for detecting OSA have emerged as a research focus. This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals, and systematically examines their applications in diagnosis, severity assessment, and localization of obstruction sites. Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening, and when combined with machine learning and deep learning models, it can achieve high diagnostic accuracy. Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA, integrating multimodal information, and advancing the clinical application of wearable devices, with the aim of promoting intelligent, non-invasive, and cost-effective screening technologies for OSA.

Key words: sleep apnea, obstructive, snoring, speech, detection, obstruction location, severity

中图分类号:

R563.8

图/表 5

参考文献 52

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作者	样本分布	特征	模型	模型识别OSA性能
Ding等^[11]	正常7例，轻度OSA 6例，中度17例，重度65例	MFCC，VGG16和PANN提取的深度特征	特征筛选：XGBoost；分类：KNN，RF	MFCC-KNN、VGG16-KNN和PANN-KNN识别准确度为100%
Ding等^[12]	正常10例，轻度OSA 6例，中度10例，重度24例	梅尔频谱图	VGG19+LSTM	识别准确度99.31%，敏感度99.13%，特异度99.58%
Song等^[13]	受试者40例， AHI为37.9±28.9	MFCC，PLP，BSF，PR800，F₀等	3个模型融合：声学特征+ XGBoost，梅尔频谱图+CNN，梅尔频谱图+ ResNet18	准确度83.44%，召回率85.27%
Luo等^[14]	受试者32例，AHI为32.8±24.4	MFCC	TCN	OSA相关打鼾检测准确度96.7%
Sun等^[15]	正常6例，OSA 24例	MFCC，PLP，BSF SE，PR800，F₀，共振峰 GTCC	特征筛选：Fisher比率；分类：GMM	OSA识别准确度90.0%，精确度95.7%
Cheng等^[16]	正常10例，OSA 33例	MFCC，Fbanks，LPC	LSTM	呼吸事件相关打鼾和正常打鼾的分类准确度为95.3%
Castillo-Escario等^[17]	正常2例，轻度OSA 3例，中度13例，重度7例	频谱图	CNN	OSA识别的准确度、敏感度和特异度分别为88.5%、71.8%和89.1%。
Jiang等^[18]	正常4例，OSA 8例	LPC，PR800，MFCC，SE	特征筛选：随机森林分类：LR、SVM、贝叶斯、KNN、ANN	LR的分类准确度、敏感度、特异度均达到100%
Shen等^[19]	正常16例，OSA 16例	MFCC	LSTM	分类准确度、敏感度和特异度分别为87%、84%和91%
Jiang等^[20]	正常4例，OSA 11例	时域波形，频谱、声谱、梅尔频谱及CQT声谱图	CNNs-DNNs，CNNs-LSTMs-DNNs	梅尔频谱图结合CNNs-LSTMs-DNNs效果最好，准确度达到95.07%

作者	样本分布	特征	模型	模型识别OSA性能
Ding等^[11]	正常7例，轻度OSA 6例，中度17例，重度65例	MFCC，VGG16和PANN提取的深度特征	特征筛选：XGBoost；分类：KNN，RF	MFCC-KNN、VGG16-KNN和PANN-KNN识别准确度为100%
Ding等^[12]	正常10例，轻度OSA 6例，中度10例，重度24例	梅尔频谱图	VGG19+LSTM	识别准确度99.31%，敏感度99.13%，特异度99.58%
Song等^[13]	受试者40例， AHI为37.9±28.9	MFCC，PLP，BSF，PR800，F₀等	3个模型融合：声学特征+ XGBoost，梅尔频谱图+CNN，梅尔频谱图+ ResNet18	准确度83.44%，召回率85.27%
Luo等^[14]	受试者32例，AHI为32.8±24.4	MFCC	TCN	OSA相关打鼾检测准确度96.7%
Sun等^[15]	正常6例，OSA 24例	MFCC，PLP，BSF SE，PR800，F₀，共振峰 GTCC	特征筛选：Fisher比率；分类：GMM	OSA识别准确度90.0%，精确度95.7%
Cheng等^[16]	正常10例，OSA 33例	MFCC，Fbanks，LPC	LSTM	呼吸事件相关打鼾和正常打鼾的分类准确度为95.3%
Castillo-Escario等^[17]	正常2例，轻度OSA 3例，中度13例，重度7例	频谱图	CNN	OSA识别的准确度、敏感度和特异度分别为88.5%、71.8%和89.1%。
Jiang等^[18]	正常4例，OSA 8例	LPC，PR800，MFCC，SE	特征筛选：随机森林分类：LR、SVM、贝叶斯、KNN、ANN	LR的分类准确度、敏感度、特异度均达到100%
Shen等^[19]	正常16例，OSA 16例	MFCC	LSTM	分类准确度、敏感度和特异度分别为87%、84%和91%
Jiang等^[20]	正常4例，OSA 11例	时域波形，频谱、声谱、梅尔频谱及CQT声谱图	CNNs-DNNs，CNNs-LSTMs-DNNs	梅尔频谱图结合CNNs-LSTMs-DNNs效果最好，准确度达到95.07%

作者	发表年份	样本分布	特征	模型	模型性能
Bahr-Hamm等^[21]	2023	正常（R）15例，轻度OSA（L） 27例，中度（M）21例，重度（H）23例	鼾声、心电图和胸腹部偏移信号的采样熵	SVM	AUC：R vs. L：61%，R vs. M：68%，R vs.H：84%，L vs. M：63%，L vs. H：82%，M vs. H：65%
YE等^[22]	2024	正常9例，轻度OSA 11例，中度19例，重度55例	MFCC，LPC，SE， PR800，F₀，共振峰等	ReliefF-mRMR 特征筛选，SVM，LR，KNN，NB分类	SVM在AHI分别为5、15、30的阈值下，识别OSA准确度分别为100%、100%和95.94%
Luo等^[23]	2020	受试者132例，其中男109例，AHI为40.62±26.62，女23例，AHI为24.73±27.64	CNN提取的深度特征	逻辑回归	在AHI分别为c15、30的阈值下，分类准确度分别为80.17%和80.21%
Cho等^[24]	2022	受试者423例，AHI为32.6±24.4	LPC，MFCC，SE等	随机森林	在AHI分别为5、15、30的阈值下，识别OSA准确度分别为88.2%、82.3%和81.7%
Hou等^[25]	2021	正常10例，轻度OSA 23例，中度24例，重度36例	ECD	GMM	分类准确度达87.74%，其中正常、轻度、中度和重度的准确度分别为100%，81.48%，75.75%和93.75%
Qiu等^[26]	2024	正常26例，轻度OSA 45例，中度44例，重度82例	经PubMedBERT 转换的MFCC	XGBoost	分类准确度为58%，正常、轻度、中度和重度的准确度分别为50%、50%、66.7%和75%
Fang等^[27]	2025	正常40例，轻度OSA 40例，中度72例，重度313例	梅尔频谱图	AST	在AHI分别为5、15、30的阈值下，识别OSA准确度分别为92.6%、88.7%和83.0%
Le等^[28]	2023	正常282例，轻度OSA 316例，中度316例，重度401例	梅尔频谱图	DNN+线性回归模型	按AHI分别为5、15和30划分，敏感度为0.97、0.85、0.96，特异度为0.89、0.84、0.91
Romero等^[29]	2022	正常13例，轻度OSA 67例，中度38例，重度39例	梅尔滤波器组特征	DNN	AHI=15：敏感度79%，特异度80%； AHI=30：敏感度78%，特异度93%
Wang等^[30]	2022	训练组116例，AHI为27.6（9.9~61.6）；验证组19例，AHI为18.9（5.9~62.1）；测试组59例，AHI为24.4（6.6~54.3）	梅尔频谱图	OSAnet	在AHI=5、10，15、30的阈值下，识别OSA敏感度分别为93.6%、82.5%、88.5%和95.6%

作者	发表年份	样本分布	特征	模型	模型性能
Bahr-Hamm等^[21]	2023	正常（R）15例，轻度OSA（L） 27例，中度（M）21例，重度（H）23例	鼾声、心电图和胸腹部偏移信号的采样熵	SVM	AUC：R vs. L：61%，R vs. M：68%，R vs.H：84%，L vs. M：63%，L vs. H：82%，M vs. H：65%
YE等^[22]	2024	正常9例，轻度OSA 11例，中度19例，重度55例	MFCC，LPC，SE， PR800，F₀，共振峰等	ReliefF-mRMR 特征筛选，SVM，LR，KNN，NB分类	SVM在AHI分别为5、15、30的阈值下，识别OSA准确度分别为100%、100%和95.94%
Luo等^[23]	2020	受试者132例，其中男109例，AHI为40.62±26.62，女23例，AHI为24.73±27.64	CNN提取的深度特征	逻辑回归	在AHI分别为c15、30的阈值下，分类准确度分别为80.17%和80.21%
Cho等^[24]	2022	受试者423例，AHI为32.6±24.4	LPC，MFCC，SE等	随机森林	在AHI分别为5、15、30的阈值下，识别OSA准确度分别为88.2%、82.3%和81.7%
Hou等^[25]	2021	正常10例，轻度OSA 23例，中度24例，重度36例	ECD	GMM	分类准确度达87.74%，其中正常、轻度、中度和重度的准确度分别为100%，81.48%，75.75%和93.75%
Qiu等^[26]	2024	正常26例，轻度OSA 45例，中度44例，重度82例	经PubMedBERT 转换的MFCC	XGBoost	分类准确度为58%，正常、轻度、中度和重度的准确度分别为50%、50%、66.7%和75%
Fang等^[27]	2025	正常40例，轻度OSA 40例，中度72例，重度313例	梅尔频谱图	AST	在AHI分别为5、15、30的阈值下，识别OSA准确度分别为92.6%、88.7%和83.0%
Le等^[28]	2023	正常282例，轻度OSA 316例，中度316例，重度401例	梅尔频谱图	DNN+线性回归模型	按AHI分别为5、15和30划分，敏感度为0.97、0.85、0.96，特异度为0.89、0.84、0.91
Romero等^[29]	2022	正常13例，轻度OSA 67例，中度38例，重度39例	梅尔滤波器组特征	DNN	AHI=15：敏感度79%，特异度80%； AHI=30：敏感度78%，特异度93%
Wang等^[30]	2022	训练组116例，AHI为27.6（9.9~61.6）；验证组19例，AHI为18.9（5.9~62.1）；测试组59例，AHI为24.4（6.6~54.3）	梅尔频谱图	OSAnet	在AHI=5、10，15、30的阈值下，识别OSA敏感度分别为93.6%、82.5%、88.5%和95.6%

作者	发表年份	样本分布	特征	模型	模型性能
Liu等^[31]	2022	受试者42例，AHI中位数为27.45	MFCC	KNN	模型准确度为85.55%，结合年龄、性别和BMI，准确度为87.98%，鄂后，舌后和多级阻塞分别为85.88%、89.22%和88.17%
Ding等^[32]	2022	阻塞部位：软腭484，口咽216，舌39，会厌89	梅尔频谱图	嵌入Conv6NP的原型网络	模型的非加权平均召回率为77.13%，其中软腭、口咽、舌、会厌分别为75.5%、80.0%、75.0%和78.0%
Sebastian等^[33]	2020	共58例患者，其中舌阻塞32例，侧壁11例，软腭10例，多级5例	时域特征：能量，熵，零交叉率；频域特征：共振峰频率，MFCC，光谱色度特征，基波和谐波频率	LDA	模型的总体准确度为62%，舌/非舌阻塞准确度为77%
Sun等^[34]	2020	阻塞部位：软腭484，口咽216，舌39，会厌39	TCC	特征筛选：主成分分析；分类：SVM	模型非加权平均召回率为87.5%，软腭、口咽、舌、会厌的准确度分别为93%，83%、74%和96%
Sun等^[35]	2021	阻塞部位：软腭484，口咽216，舌39，会厌89	MSE，MFCC	特征筛选：主成分分析；分类：SVM	模型准确度为89.09%，敏感度为86.36%，特异度为96.4%，其中软腭、口咽、舌、会厌的敏感度分别为98.04%、80.56%、72.73%和94.12%

基于声学信息检测阻塞性睡眠呼吸暂停的研究进展

New advances on detecting obstructive sleep apnea based on acoustic information

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作者	发表年份	研究对象	声学特征	分类模型	结果
U?ur等^[42]	2023	OSA 48例（轻、中、重度均16例），健康对照组13例（打鼾）	由非线性特征提取模块获得	KNN，SVM	最佳二分类表现：准确度为95.1%，敏感度为97.9%，特异度为84.6%；最佳多分类表现：准确度为82.0%，敏感度为82.5%，特异度为94.0%
Yilmaz等^[43]	2023	32例男性和8例女性，其中20例为OSA患者	LPCC，MFCC等	KNN，SVM	准确度为82.5%
Zhang等^[44]	2023	254例因睡眠打鼾住院的患者，全部为中国男性	未提及	XLSR	最佳表现：F1值为72.5%
Botelho等^[45]	2021	40例男性和20例女性，其中22例为OSA患者	未提及	CNN	准确度为82.5%
Pang等^[46]	2020	66组语音数据，按31、13、10、12划分为正常组和轻、中、重症OSA患者	MFCC，LPCC等	QDA	准确度：95.45%；该研究提出了一种新方法用于特征选择过程，使用该方法所提取的参数使得分类器性能提高了6.06%
Simply等^[47]	2020	305例进行了PSG的患者和93例学生志愿者	MFCC等	CNN，LSTM	融合系统的AHI预测表现：皮尔逊相关系数为0.76；MAE为7.6次/h；基于AHI预测值的OSA分类：准确度为77.14%，敏感度为75%，特异度为79%
Perero-Codosero等^[48]	2020	525例男性受试者和232例女性受试者	未提及	X-vector，DANNs	准确度为76.60%，敏感度为77.20%，特异度为75.89%
Espinoza-Cuadros等^[49]	2016	125例男性对照组和301例男性OSA患者	超向量/i向量	SVR	准确度：68%/71%；敏感度：89%/92%
Ben等^[50]	2016	198例经过PSG检查的男性患者	持续元音特征，短期特征和长期特征	SVR，回归树	AHI的诊断一致性为67.3%，MAE为10.8次/h
Sole-Casals等^[51]	2014	121例重症OSA患者和127例健康对照组	253个声学特征，包括基频、峰值、时域、频域、LPC等	BC，NN，SVM，KNN，AdaBoost	最佳表现为BC模型，敏感度为81.74%，特异度为82.40%

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