Automated Recognition of Obstructive Sleep Apnea Using Ensemble Support Vector Machine Classifier

Author(s):  
V. Kalaivani
2018 ◽  
Vol 23 (1) ◽  
pp. 65 ◽  
Author(s):  
Nader Salari ◽  
Zohreh Manoochehri ◽  
Mansour Rezaei ◽  
Habibolah Khazaie ◽  
Sara Manoochehri ◽  
...  

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176991 ◽  
Author(s):  
Wen-Te Liu ◽  
Hau-tieng Wu ◽  
Jer-Nan Juang ◽  
Adam Wisniewski ◽  
Hsin-Chien Lee ◽  
...  

SLEEP ◽  
2020 ◽  
Vol 43 (7) ◽  
Author(s):  
Wen-Chi Huang ◽  
Pei-Lin Lee ◽  
Yu-Ting Liu ◽  
Ambrose A Chiang ◽  
Feipei Lai

Abstract Study Objectives Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice. Methods The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]). Results Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o. Conclusion Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.


2015 ◽  
Vol 9 (1L) ◽  
pp. 149-157 ◽  
Author(s):  
Liang-Wen Hang ◽  
Hsuan-Hung Lin ◽  
Chung-Chuan Cheng ◽  
John Y. Chiang ◽  
Hsiang-Ling Wang ◽  
...  

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):


2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fernando Espinoza-Cuadros ◽  
Rubén Fernández-Pozo ◽  
Doroteo T. Toledano ◽  
José D. Alcázar-Ramírez ◽  
Eduardo López-Gonzalo ◽  
...  

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients’ facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.


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