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Luo J, Zhao Y, Liu H, Zhang Y, Shi Z, Li R, Hei X, Ren X. SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis. Physiol Meas 2024; 45:035003. [PMID: 38316023 DOI: 10.1088/1361-6579/ad262b] [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: 08/06/2023] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.
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Affiliation(s)
- Jing Luo
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
- Human-Machine Integration Intelligent Robot Shaanxi University Engineering Research Center, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
| | - Yinuo Zhao
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
- Human-Machine Integration Intelligent Robot Shaanxi University Engineering Research Center, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
| | - Haiqin Liu
- Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, People's Republic of China
| | - Yitong Zhang
- Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, People's Republic of China
| | - Zhenghao Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
- Human-Machine Integration Intelligent Robot Shaanxi University Engineering Research Center, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
| | - Rui Li
- School of Mechanical and Instrumental Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
| | - Xinhong Hei
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
- Human-Machine Integration Intelligent Robot Shaanxi University Engineering Research Center, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China
| | - Xiaorong Ren
- Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, People's Republic of China
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Hata M, Miyazaki Y, Nagata C, Masuda H, Wada T, Takahashi S, Ishii R, Miyagawa S, Ikeda M, Ueno T. Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations. Front Psychiatry 2023; 14:1287607. [PMID: 38034919 PMCID: PMC10682064 DOI: 10.3389/fpsyt.2023.1287607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. Objective We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. Methods We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. Results Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. Discussion This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.
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Affiliation(s)
- Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Chie Nagata
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hirotada Masuda
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Tamiki Wada
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shun Takahashi
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
- Clinical Research and Education Center, Asakayama General Hospital, Osaka, Japan
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Ryouhei Ishii
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
| | - Shigeru Miyagawa
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Takayoshi Ueno
- Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Cardiovascular Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
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Arslan RS. Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. PeerJ Comput Sci 2023; 9:e1554. [PMID: 37810361 PMCID: PMC10557519 DOI: 10.7717/peerj-cs.1554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/04/2023] [Indexed: 10/10/2023]
Abstract
Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.
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Choi J, Kwon S, Park S, Han S. Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification. Digit Health 2023; 9:20552076231163783. [PMID: 36937698 PMCID: PMC10017951 DOI: 10.1177/20552076231163783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/24/2023] [Indexed: 03/15/2023] Open
Abstract
Background Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. Methods To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. Results The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. Conclusion We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.
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Affiliation(s)
- Junggu Choi
- Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea
| | | | - Sohyun Park
- Economics, Underwood International College, Yonsei University, Seoul, Republic of Korea
| | - Sanghoon Han
- Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea
- Department of Psychology, Yonsei University, Seoul, Republic of Korea
- Sanghoon Han, Department of Psychology and Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul 03722, South Korea.
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Izonin I, Kutucu H, Singh KK. Smart systems and data-driven services in healthcare. Comput Biol Med 2022; 158:106074. [PMID: 36109250 DOI: 10.1016/j.compbiomed.2022.106074] [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: 07/21/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/27/2022]
Abstract
The modern development of Medicine and Healthcare is primarily based on the automation of various processes to support the correct and timely medical decisions. A doctor or other medical staff's fast, accurate, reliable diagnosis, prevention, and treatment processes are the keys to quality patient care. This aim can be achieved by developing reliable, intelligent Healthcare and medicine service systems for various purposes. The Special Issue covers science-intensive real-time solutions that underlie smart systems and data-driven services in Healthcare and Medicine.
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Affiliation(s)
- Ivan Izonin
- Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, 79013, Ukraine.
| | - Hakan Kutucu
- Department of Software Engineering, Karabuk University, Karabuk, 78050, Turkey.
| | - Krishna Kant Singh
- Amity School of Engineering and Technology, Amity University, Uttar Pradesh, India.
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