Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome

By combining LSTM-extracted ECG features with SVM-based biometric features, the proposed multimodal fusion method provided highly effective OSAHS detection. The findings suggest considerable potential for the use of this approach in real medical environments.

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Publication Details

Journal
Frontiers in Medicine
Published
2026-01-23
DOI
https://doi.org/10.3389/fmed.2026.1762868
Primary Topic
Obstructive Sleep Apnea Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome

Xingxin Gong, Yong He, Mingqing Liang, Quanjing Zhu et al.
Frontiers in Medicine
Obstructive Sleep Apnea Research
article

Multimodal ECG and biometric data fusion for improved detection of obstructive sleep apnea hypopnea syndrome

Xingxin Gong, Yong He, Mingqing Liang, Quanjing Zhu, Chao Mao
article en

Abstract

By combining LSTM-extracted ECG features with SVM-based biometric features, the proposed multimodal fusion method provided highly effective OSAHS detection. The findings suggest considerable potential for the use of this approach in real medical environments.

Frontiers in MedicineVol. 13
Sichuan University (CN), West China Hospital of Sichuan University (CN), Shanghai Clinical Research Center (CN), Yuxian People's Hospital (CN), South China University of Technology (CN)
Natural Science Foundation of Sichuan Province
Good health and well-being
Openalex Percentile: Top 81%
Obstructive Sleep Apnea Research
DOIOpen Access PDFOpenAlex
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