Single channel photoplethysmography-based obstructive sleep apnea detection and arrhythmia classification

2021 ◽  
pp. 1-13
Author(s):  
Xiang Chen ◽  
Jiahao Huang ◽  
Feifei Luo ◽  
Shang Gao ◽  
Min Xi ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Single Channel ◽  
Classification Algorithm ◽  
Support Vector ◽  
Early Screening ◽  
Obstructive Sleep ◽  
And Performance ◽  
Events Detection ◽  
Modified Hausdorff Distance

BACKGROUND: Simplified and easy-to-use monitoring approaches are crucial for the early diagnosis and prevention of obstructive sleep apnea (OSA) and its complications. OBJECTIVE: In this study, the OSA detection and arrhythmia classification algorithms based on single-channel photoplethysmography (PPG) are proposed for the early screening of OSA. METHODS: Thirty clinically diagnosed OSA patients participated in this study. Fourteen features were extracted from the PPG signals. The relationship between the number of features as inputs of the support vector machine (SVM) and performance of apnea events detection was evaluated. Also, a multi-classification algorithm based on the modified Hausdorff distance was proposed to recognize sinus rhythm and four arrhythmias highly related with SA. RESULTS: The feature set composed of meanPP, SDPP, RMSSD, meanAm, and meank1 could provide a satisfactory balance between the performance and complexity of the algorithm for OSA detection. Also, the arrhythmia classification algorithm achieves the average sensitivity, specificity and accuracy of 83.79%, 95.91% and 93.47%, respectively in the classification of all four types of arrhythmia and regular rhythm. CONCLUSION: Single channel PPG-based OSA detection and arrhythmia classification in this study can provide a feasible and promising approach for the early screening and diagnosis of OSA and OSA-related arrhythmias.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

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):

scholarly journals Evaluation of tongue/mandible volume ratio in children with obstructive sleep apnea

2018 ◽  
Vol 23 (4) ◽  
pp. 72-78 ◽  
Author(s):  
Kavita Hotwani ◽  
Krishna Sharma ◽  
Arpan Jaiswal
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Soft Tissue ◽  
Volume Ratio ◽  
Radiology Department ◽  
Control Group ◽  
Mr Images ◽  
Early Screening ◽  
Obstructive Sleep ◽  
Tongue Volume

ABSTRACT Objective: The present study was an attempt to investigate tongue/mandible volume ratio in children, using volumetric magnetic resonance imaging (MRI) for early screening and to aid in treatment planning. Methods: Volumetric evaluation of tongue volume/mandible volume ratio (TV/MV ratio) in children with obstructive sleep apnea (OSA) using MRI was carried out retrospectively on available DICOM MR images of children in the age group of 10-14 years. MRI image records of patients diagnosed with OSA were obtained from interventional radiology department records, at Sharad Pawar Dental College and Hospital (Datta Meghe Institute of Medical Sciences, Nagpur/India). The age, gender, height and weight of the subjects were retrieved from patient database and registered. For the control group, available MRI images of healthy subjects without OSA were retrieved. Body mass index (BMI) was also calculated using the height and the weight present in the records. Measurements from MR images were made using DICOM image processing software. Soft tissue and bony structure segmentation was performed by manual tracing. The tongue volume and mandible volume were directly computed using the software. The tongue volume/mandible volume ratio (TV/MV) was generated using the above values and expressed as a percentage for both groups. Results: The difference between OSA group and control group with respect to TV/MV ratio was found to be highly significant at 0.05 level of significance. There was no significant correlation between BMI and TV/MV ratio in OSA group (p= 0.451) as well as in control group (p= 0.094). Conclusion: TV/MV ratio may be an appropriate variable to evaluate the risk of OSA, representing the balance between skeletal morphology and soft tissue morphology in craniofacial complex.

scholarly journals Use and Performance of the STOP-Bang Questionnaire for Obstructive Sleep Apnea Screening Across Geographic Regions

2021 ◽  
Vol 4 (3) ◽  
pp. e211009 ◽  
Author(s):  
Bianca Pivetta ◽  
Lina Chen ◽  
Mahesh Nagappa ◽  
Aparna Saripella ◽  
Rida Waseem ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep ◽  
Geographic Regions ◽  
And Performance

Pulse Oximetry Monitor Feasible for Early Screening of Obstructive Sleep Apnea (OSA)

2019 ◽  
Vol 40 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Huei-Chen Lin ◽  
Chien-Ling Su ◽  
Jun-Hui Ong ◽  
kun-ling Tsai ◽  
Yu-Wen Chen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Pulse Oximetry ◽  
Early Screening ◽  
Obstructive Sleep

scholarly journals Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fernando Espinoza-Cuadros ◽  
Rubén Fernández-Pozo ◽  
Doroteo T. Toledano ◽  
José D. Alcázar-Ramírez ◽  
Eduardo López-Gonzalo ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Speech Processing ◽  
Upper Airway ◽  
Support Vector ◽  
Apnea Hypopnea Index ◽  
Active Appearance Models ◽  
Obstructive Sleep ◽  
Craniofacial Features ◽  
Low Dimensional

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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