Severity evaluation of obstructive sleep apnea based on speech features
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SLEEP BREATHING PHYSIOLOGY AND DISORDERS • ORIGINAL ARTICLE
Severity evaluation of obstructive sleep apnea based on speech features Yiming Ding 1,2,3 & Jiaxi Wang 4 & Jiandong Gao 4,5 & Qiang Fang 6 & Yanru Li 1,2,3 & Wen Xu 1,2,3 & Ji Wu 4,5 & Demin Han 1,2,3 Received: 16 April 2020 / Revised: 3 August 2020 / Accepted: 10 August 2020 # Springer Nature Switzerland AG 2020
Abstract Purpose There are upper airway abnormalities in patients with obstructive sleep apnea (OSA), and their speech signal characteristics are different from those of unaffected people. In this study, the severity of OSA was evaluated automatically by machine learning technology based on the speech signals of Chinese people. Methods In total, 151 adult male Mandarin native speakers who had suspected OSA completed polysomnography to assess the severity of the disease. Chinese vowels and nasal sounds were recorded in sitting and supine positions, and the accuracy of predicting the apnea-hypopnea index (AHI) of the participants using a machine learning method was analyzed based on features extracted from the speech signals. Results Among the 151 participants, 75 had AHI > 30 events/h, and 76 had AHI ≤ 30 events/h. Various features including linear prediction cepstral coefficients (LPCC) were extracted from the data collected from participants recorded in the sitting and supine positions and by using a linear support vector machine (SVM); we classified the participants with thresholds of AHI = 30 and AHI = 10 events/h. The accuracies of the classifications were both 78.8%, the sensitivities were 77.3% and 79.1%, and the specificities were 80.3% and 78.0%, respectively. Conclusion This study constructed a severity evaluation model of OSA based on speech signal processing and machine learning, which can be used as an effective method to screen patients with OSA. In addition, it was found that Chinese pronunciation can be used as an effective feature to predict OSA. Keywords Obstructive sleep apnea (OSA) . Speech signal processing . Machine learning
Introduction Obstructive sleep apnea (OSA) is a common chronic disease, the prevalence of which is 17% (4–50%) in women and 22% (9–37%) in men [1]. The disease is characterized by recurrent collapse of the upper airway during sleep, resulting in a
periodic decrease or cessation of ventilation, accompanied by hypoxia, hypercapnia, or waking from sleep [2]. Researches have shown that the prevalence of hypertension, stroke, coronary artery disease, heart failure, and diabetes in patients with OSA is significantly higher than that in unaffected people [3, 4]. Sleep structure disorder of patients with OSA
Yiming Ding and Jiaxi Wang contributed equally to this work. * Ji Wu [email protected] * Demin Han [email protected] 1
2
Beijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing 100730, People’s Republic of China Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University,
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