基于迁移学习和模型融合自动检测 OSAHS 患者。

IF 2.3 4区 医学 Q3 BIOPHYSICS
Li Ding, Jianxin Peng, Lijuan Song, Xiaowen Zhang
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引用次数: 0

摘要

打鼾是阻塞性睡眠呼吸暂停低通气综合征(OSAHS)最典型的症状,可用于开发自动检测 OSAHS 患者的无创方法。在这项工作中,一个基于迁移学习和模型融合的模型被用于对简单打鼾者和 OSAHS 患者进行分类。基于预训练的视觉几何组-16(VGG16)、预训练的音频神经网络(PANN)和梅尔频率倒频谱系数(MFCC)构建了三种基本模型。使用 XGBoost 根据特征的重要性选择特征,应用最大投票策略融合这些基本模型,并使用 "单主体淘汰 "交叉验证来评估所提出的模型。结果表明,嵌入了前 5 个 VGG16 特征和前 5 个 PANN 特征的融合模型可以正确识别 OSAHS 患者(AHI>5),准确率达到 100%。所提出的融合模型具有良好的分类性能、较低的计算成本和较高的鲁棒性,使在家中检测 OSAHS 患者成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatically detecting OSAHS patients based on transfer learning and model fusion.

Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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