Severity Evaluation of Obstructive Sleep Apnea Patients Based on Machine Learning of Speech Signal
Creators: name: Xue, Liminhao
Publisher: name: Zenodo
Year: 2023

description: Severity Evaluation of Obstructive Sleep Apnea Patients Based on
Machine Learning of Speech Signal
Liminhao Xue1
 
, Hong Hong2,*
 
1. High School Affiliated to Nanjing Normal University Jiangning Campus, Nanjing, China
 
211102
 
2. Novel Biotech Laboratory, Nanjing University of Science and Technology, Nanjing, China
 
210094
 
Abstract
Introduction
Obstructive sleep apnea (OSA) is a common chronic disease characterized by recurrent
collapse of the upper airway during sleep resulting in a periodic decrease or cessation of
ventilation which seriously affects human health and even causes unpredicted accident due to
sleep structure disorder [1]. Therefore, early diagnosis and timely intervention of OSA are of
great significance. Traditional polysomnography (PSG) diagnosis, observing the average
number of apnea and hypopnea events per hour during sleep (apnea-hypopnea index, AHI), is
time consuming and expensive, and can disrupt the normal sleep structure because of multiple
sensors contact during sleep monitoring [2]
 
. The speech characteristics of patients with OSA
are different from those of unaffected people which has the potential to be used as OSA
severity classifier [3]
 
. This study attempts to evaluate the OSA severity by machine learning
 
technology based on the speech signals of Chinese male patients.
Methods
In total, 89 adult male Mandarin native speakers who had suspected OSA completed PSG to
assess the severity of the disease. They are classified into two groups: mild to moderate and
severe by AHI=12.14±8.81 and AHI=64.29±20.46 events/h. A high-fidelity recorder was used
to record the voice data of OSA patients in a closed and quiet room. The accuracy of
predicting AHI of the participants using Support Vector Machine (SVM), Random
Forest(RF), K-Nearest Neighbor (KNN) and Linear Regression (LR) were analyzed based on
features extracted from the speech signals. The Classification performance of different
pronunciation types and different models were compared.
Results
(1) Among four machine learning models, SVM shows the best prediction accuracy.
(2) Vowel has the best classification performance in terms of specificity, velar and nasal have
the better classification performance in terms of accuracy and recall rate, overall, the
comprehensive performance of nasal sound is the best.
(3) The average classification accuracy of SVM with nasal signal was 73.5 %, the sensitivity
was 85.2%, and the specificities was 53.3%.
Conclusion
 
This study constructed an effective way to classify the severity of patients with OSA based on
machine learning and speech signal. A speech database based on Chinese speech characteristics
including the speech data and PSG data of 89 patients were collected and established for OSA
evaluation. It was found that Chinese pronunciation can be used as an effective feature to predict
OSA. The SVM model has the best classification performance for nasal sounds among the three
vocal types. The method demonstrates that rhythmic features have a significant effect on the
extraction of information about the vocal characteristics of OSA.
Keywords: Obstructive sleep apnea, Speech signal, Machine learning
References
[1] Veasey S C, Rosen I M. Obstructive sleep apnea in adults. The New England Journal of
Medicine, 2019, 380(15): 1442–1449.
[2] Kapur V, Strohl K P, Redline S, et al. Underdiagnosis of sleep apnea syndrome in US.
Communities[J]. Sleep and Breath, 2002, 6(2):049-054.
[3] Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, et al. Reviewing the connection
between speech and obstructive sleep apnea. Biomedical Engineering Online, 2016,
15(1):20.