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Lin SY, Tsai CY, Majumdar A, Ho YH, Huang YW, Kao CK, Yeh SM, Hsu WH, Kuan YC, Lee KY, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med 2024; 20:1267-1277. [PMID: 38546033 PMCID: PMC11294131 DOI: 10.5664/jcsm.11136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 08/03/2024]
Abstract
STUDY OBJECTIVES The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.
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Affiliation(s)
- Shang-Yang Lin
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Yu-Hsuan Ho
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Yu-Wen Huang
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Chun-Kai Kao
- Wireless Technology and Antenna Research and Development Department, Wistron Corporation, Taipei, Taiwan
| | - Shang-Min Yeh
- Advanced Technology Lab, Wistron Corporation, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Po-Hao Feng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Kuan-Yuan Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Jiunn-Horng Kang
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Sleep Center, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
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J RG, K D. Apnoea detection using ECG signal based on machine learning classifiers and its performances. J Med Eng Technol 2023; 47:344-354. [PMID: 38625408 DOI: 10.1080/03091902.2024.2336500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 03/23/2024] [Indexed: 04/17/2024]
Abstract
Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.
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Affiliation(s)
- Rolant Gini J
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
| | - Dhanalakshmi K
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
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Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health 2023; 9:20552076231152751. [PMID: 36896329 PMCID: PMC9989412 DOI: 10.1177/20552076231152751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/04/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wun-Hao Cheng
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Yi-Chih Lin
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements. SENSORS 2022; 22:s22103881. [PMID: 35632290 PMCID: PMC9146067 DOI: 10.3390/s22103881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/25/2022] [Accepted: 05/09/2022] [Indexed: 02/03/2023]
Abstract
Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.
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