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Tyagi PK, Agrawal D. Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model. Comput Biol Med 2024; 179:108877. [PMID: 39029435 DOI: 10.1016/j.compbiomed.2024.108877] [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: 11/29/2023] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods. METHOD This study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information. The proposed framework utilizes temporal encoding and the leaky integrate and fire model to transform the ECG signal into spikes for capturing the signal's dynamic pattern nature and to simulate input response behaviors. The tempoton learning technique, a spike-based algorithm, trains the SNN model to identify SLA event patterns from encoded output spike trains. This study utilized ECG data to extract heart rate variability (HRV) and ECG-derived respiration (EDR) signals from 1-min segment data of ECG records for input to SNN model. Thirty-five recordings of both released and withheld data from the Apnea-ECG databases from Physionet have been applied to train the SNN model and validate the model's efficacy in identifying SLA occurrences. RESULTS The proposed method demonstrated substantial improvements compared to other SLA detection techniques, achieving a significant accuracy of 94.63 % for per-segment detection, along with specificity, sensitivity, F1-score and AUC values of 96.21 %, 92.04 %, 0.9285, and 0.9851 respectively. The accuracy for per-recording detection achieved 100 %, with a correlation coefficient value of 0.986. Additionally, the experiment used UCD data for validation methods, achieving an accuracy of 84.573 %. CONCLUSIONS These results suggest the effectiveness and accessibility of the presented approach for accurately identifying SLA cases. The suggested model enhances the performance of SLA detection when contrasted with various techniques based on feature engineering and feature learning.
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Affiliation(s)
- Praveen Kumar Tyagi
- Dept. of ECE, Maulana Azad National Institute of Technology, Bhopal, MP, India.
| | - Dheeraj Agrawal
- Dept. of ECE, Maulana Azad National Institute of Technology, Bhopal, MP, India
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2
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Ontimare Manlises C, Chen JW, Huang CC. A gated recurrent unit model based on ultrasound images of dynamic tongue movement for determining the severity of obstructive sleep apnea. ULTRASONICS 2024; 141:107320. [PMID: 38678641 DOI: 10.1016/j.ultras.2024.107320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 04/14/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
Obstructive sleep apnea (OSA) presents as a respiratory disorder characterized by recurrent upper pharyngeal airway collapse during sleep. Dynamic tongue movement (DTM) analysis emerges as a promising avenue for elucidating the pathophysiological underpinnings of OSA, thereby facilitating its diagnosis. Recent endeavors have utilized artificial intelligence techniques to categorize OSA severity leveraging electrocardiography and blood oxygen saturation data. Nonetheless, the integration of ultrasound (US) imaging of the tongue remains largely untapped in the development of machine learning models aimed at determining the severity of OSA. This study endeavors to bridge this gap by capturing US images of DTM dynamics during wakefulness, encompassing transitions from normal breathing (NB) to the performance of the Müller maneuver (MM) in a cohort of 53 patients. Leveraging the modified optical flow method (MOFM), the trajectories of patients' DTM were tracked, facililtating the extraction of 27 parameters vital for model training. These parameters encompassed nine-point lateral movement, nine-point axial movement, and nine-point total displacement of the tongue, resulting in a dataset of 186,030 samples. The gated recurrent unit (GRU) method, renowned for its efficacy in motion tracking, was employed for model development in this study. Validation of the developed model was conducted via stratified k-fold cross-validation (SCV). The systems' overall performance in classifying OSA severity, as quantified by mean accuracy (MA), yielded a value of 43.49%. This pilot investigation marks an exploratory endeavor into the utilization of artificial intelligence for the classification of OSA severity based on US images and dynamic movement patterns. This novel model holds potential to assist clinicians in categorizing OSA severity and guiding the selection of pertinent treatment modalities tailored to the individual needs of patients afflicted with OSA.
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Affiliation(s)
- Cyrel Ontimare Manlises
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan; School of Electrical, Electronics, and Computer Engineering, Mapua University, Manila 1002 Philippines
| | - Jeng-Wen Chen
- Department of Otolaryngology-Head and Neck Surgery, Cardinal Tien Hospital and Schhool of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Otolaryngology-Head and Neck Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Chung Huang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
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3
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Sindorf J, Szabo AL, O'Brien MK, Sunderrajan A, Knutson KL, Zee PC, Wolfe L, Arora VM, Jayaraman A. Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients. Sleep 2024:zsae123. [PMID: 38814827 DOI: 10.1093/sleep/zsae123] [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: 03/08/2024] [Indexed: 06/01/2024] Open
Abstract
STUDY OBJECTIVES To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF). METHODS A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least one night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the Apnea-Hypopnea Index (AHI≥5, AHI≥15). RESULTS Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only forty-eight participants (63%) could be successfully assessed for OSA by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger temperature features to detect moderate-severe sleep apnea (AHI≥15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions. CONCLUSIONS This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes.
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Affiliation(s)
| | - Alison L Szabo
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Megan K O'Brien
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | | | - Kristen L Knutson
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Phyllis C Zee
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Lisa Wolfe
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Vineet M Arora
- University of Chicago School of Medicine, Chicago, IL, USA
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
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Yang H, Lu S, Yang L. Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review. Syst Rev 2024; 13:38. [PMID: 38268059 PMCID: PMC10807185 DOI: 10.1186/s13643-024-02449-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/29/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive cessation or reduction in airflow during sleep. Stroke patients have a higher risk of OSA, which can worsen their cognitive and functional disabilities, prolong their hospitalization, and increase their mortality rates. METHODS We conducted a comprehensive literature search in the databases of PubMed, CINAHL, Embase, PsycINFO, Cochrane Library, and CNKI, using a combination of keywords and MeSH words in both English and Chinese. Studies published up to March 1, 2022, which reported the development and/or validation of clinical prediction models for OSA diagnosis in stroke patients. RESULTS We identified 11 studies that met our inclusion criteria. Most of the studies used logistic regression models and machine learning approaches to predict the incidence of OSA in stroke patients. The most frequently selected predictors included body mass index, sex, neck circumference, snoring, and blood pressure. However, the predictive performance of these models ranged from poor to moderate, with the area under the receiver operating characteristic curve varying from 0.55 to 0.82. All the studies have a high overall risk of bias, mainly due to the small sample size and lack of external validation. CONCLUSION Although clinical prediction models have shown the potential for diagnosing OSA in stroke patients, their limited accuracy and high risk of bias restrict their implications. Future studies should focus on developing advanced algorithms that incorporate more predictors from larger and representative samples and externally validating their performance to enhance their clinical applicability and accuracy.
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Affiliation(s)
- Hualu Yang
- Department of Rehabilitation, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 581052, China
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Shuya Lu
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Lin Yang
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China.
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Almarshad MA, Al-Ahmadi S, Islam MS, BaHammam AS, Soudani A. Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:7924. [PMID: 37765980 PMCID: PMC10536445 DOI: 10.3390/s23187924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/03/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023]
Abstract
Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model's outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79.
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Affiliation(s)
- Malak Abdullah Almarshad
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia (M.S.I.)
- Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia (M.S.I.)
| | - Md Saiful Islam
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia (M.S.I.)
| | - Ahmed S. BaHammam
- The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia
- Strategic Technologies Program of the National Plan for Sciences and Technology and Innovation in the Kingdom of Saudi Arabia, Riyadh 11324, Saudi Arabia
| | - Adel Soudani
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia (M.S.I.)
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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7
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Srivastava G, Chauhan A, Kargeti N, Pradhan N, Dhaka VS. ApneaNet: A hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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8
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Chen Y, Ma G, Zhang M, Yang S, Yan J, Zhang Z, Zhu W, Dong Y, Wang L. Contactless screening for sleep apnea with breathing vibration signals based on modified U-Net. Sleep Med 2023; 107:187-195. [PMID: 37201224 DOI: 10.1016/j.sleep.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 03/13/2023] [Accepted: 04/28/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a chronic sleep disorder characterized by frequent cessations or reductions of breathing during sleep. Polysomnography (PSG) is a definitive diagnostic tool for OSA. The costly and obtrusive nature of PSG and poor access to sleep clinics have created a demand for accurate home-based screening devices. METHODS This paper proposes a novel OSA screening method based solely on breathing vibration signals with a modified U-Net, allowing patients to be tested at home. Sleep recordings over a whole night are collected in a contactless manner, and sleep apnea-hypopnea events are labeled by a deep neural network. The apnea-hypopnea index (AHI) calculated from events estimation is then used to screen for the apnea. The performance of the model is tested by event-based analysis and comparing the estimated AHI with the manually obtained values. RESULTS The accuracy and sensitivity of sleep apnea events detection are 97.5% and 76.4%, respectively. The mean absolute error of AHI estimation for the patients is 3.0 events/hour. The correlation between the ground truth AHI and predicted AHI shows an R2 of 0.95. In addition, 88.9% of all participants are classified into correct AHI categories. CONCLUSIONS The proposed scheme has great potential as a simple screening tool for sleep apnea. It can accurately detect potential OSA and help the patients to be referred for differential diagnosis of home sleep apnea test (HSAT) or polysomnographic evaluation.
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Affiliation(s)
- Yuhang Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, China
| | - Gang Ma
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, China
| | - Miao Zhang
- Suzhou Guoke Medical Technology Development (Group) Co, China
| | | | - Jiayong Yan
- Shanghai University of Medicine and Health Sciences, China
| | | | - Wenliang Zhu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, China
| | - Yanfang Dong
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, China
| | - Lirong Wang
- School of Electronics and Information Technology, Soochow University, China.
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Martín-González S, Ravelo-García AG, Navarro-Mesa JL, Hernández-Pérez E. Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094267. [PMID: 37177472 PMCID: PMC10181515 DOI: 10.3390/s23094267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.
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Affiliation(s)
- Sofía Martín-González
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Juan L Navarro-Mesa
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Eduardo Hernández-Pérez
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
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Xu S, Faust O, Seoni S, Chakraborty S, Barua PD, Loh HW, Elphick H, Molinari F, Acharya UR. A review of automated sleep disorder detection. Comput Biol Med 2022; 150:106100. [PMID: 36182761 DOI: 10.1016/j.compbiomed.2022.106100] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/04/2022] [Accepted: 09/12/2022] [Indexed: 12/22/2022]
Abstract
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.
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Affiliation(s)
- Shuting Xu
- Cogninet Brain Team, Sydney, NSW, 2010, Australia
| | - Oliver Faust
- Anglia Ruskin University, East Rd, Cambridge CB1 1PT, UK.
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia; Centre for Advanced Modelling and Geospatial Lnformation Systems (CAMGIS), Faculty of Engineer and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Sydney, NSW, 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore
| | | | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- School of Business (Information System), University of Southern Queensland, Australia; School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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Abstract
Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.
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12
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Bernardini A, Brunello A, Gigli GL, Montanari A, Saccomanno N. OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome. Sci Data 2022; 9:177. [PMID: 35440646 PMCID: PMC9018698 DOI: 10.1038/s41597-022-01272-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 03/23/2022] [Indexed: 11/09/2022] Open
Abstract
Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits. Unfortunately, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. The data in this work pertains to 30 patients that were admitted to the stroke unit of the Udine University Hospital, Italy. Unlike previous studies, exclusion criteria are minimal. As a result, data are strongly affected by noise, and individuals may suffer from several comorbidities. Each patient instance is composed of overnight vital signs data deriving from multi-channel ECG, photoplethysmography and polysomnography, and related domain expert's OSAS annotations. The dataset aims to support the development of automated methods for the detection of OSAS events based on just routinely monitored vital signs, and capable of working in a real-world scenario.
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Affiliation(s)
- Andrea Bernardini
- Clinical Neurology Unit, Udine University Hospital, 33100, Udine, Italy.
| | - Andrea Brunello
- Department of Mathematics, Computer Science, and Physics, University of Udine, 33100, Udine, Italy.
| | - Gian Luigi Gigli
- Clinical Neurology Unit, Udine University Hospital, 33100, Udine, Italy
| | - Angelo Montanari
- Department of Mathematics, Computer Science, and Physics, University of Udine, 33100, Udine, Italy
| | - Nicola Saccomanno
- Department of Mathematics, Computer Science, and Physics, University of Udine, 33100, Udine, Italy.
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Del Campo F, Arroyo CA, Zamarrón C, Álvarez D. Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:43-61. [PMID: 36217078 DOI: 10.1007/978-3-031-06413-5_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.
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Affiliation(s)
- Félix Del Campo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN). Instituto de Salud Carlos III, Madrid, Spain
| | - C Ainhoa Arroyo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Carlos Zamarrón
- Division of Respiratory Medicine, Hospital Clínico Universitario, Santiago de Compostela, Spain
| | - Daniel Álvarez
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN). Instituto de Salud Carlos III, Madrid, Spain.
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Sleep and Stroke: Opening Our Eyes to Current Knowledge of a Key Relationship. Curr Neurol Neurosci Rep 2022; 22:767-779. [PMID: 36190654 PMCID: PMC9633474 DOI: 10.1007/s11910-022-01234-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW To elucidate the interconnection between sleep and stroke. RECENT FINDINGS Growing data support a bidirectional relationship between stroke and sleep. In particular, there is strong evidence that sleep-disordered breathing plays a pivotal role as risk factor and concur to worsening functional outcome. Conversely, for others sleep disorders (e.g., insomnia, restless legs syndrome, periodic limb movements of sleep, REM sleep behavior disorder), the evidence is weak. Moreover, sleep disturbances are highly prevalent also in chronic stroke and concur to worsening quality of life of patients. Promising novel technologies will probably allow, in a near future, to guarantee a screening of commonest sleep disturbances in a larger proportion of patients with stroke. Sleep assessment and management should enter in the routinary evaluation of stroke patients, of both acute and chronic phase. Future research should focus on the efficacy of specific sleep intervention as a therapeutic option for stroke patients.
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Álvarez D, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Moreno F, Del Campo F, Hornero R. Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:219-239. [PMID: 36217087 DOI: 10.1007/978-3-031-06413-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO2) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO2-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.
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Affiliation(s)
- Daniel Álvarez
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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John A, Nundy KK, Cardiff B, John D. Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:1161-1173. [PMID: 34882563 DOI: 10.1109/tbcas.2021.3134043] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With advances in circuit design and sensing technology, the acquisition of data from a large number of Internet of Things (IoT) sensors simultaneously to enable more accurate inferences has become mainstream. In this work, we propose a novel convolutional neural network (CNN) model for the fusion of multimodal and multiresolution data obtained from several sensors. The proposed model enables the fusion of multiresolution sensor data, without having to resort to padding/ resampling to correct for frequency resolution differences even when carrying out temporal inferences like high-resolution event detection. The performance of the proposed model is evaluated for sleep apnea event detection, by fusing three different sensor signals obtained from UCD St. Vincent University Hospital's sleep apnea database. The proposed model is generalizable and this is demonstrated by incremental performance improvements, proportional to the number of sensors used for fusion. A selective dropout technique is used to prevent overfitting of the model to any specific high-resolution input, and increase the robustness of fusion to signal corruption from any sensor source. A fusion model with electrocardiogram (ECG), Peripheral oxygen saturation signal (SpO2), and abdominal movement signal achieved an accuracy of 99.72% and a sensitivity of 98.98%. Energy per classification of the proposed fusion model was estimated to be approximately 5.61 μJ for on-chip implementation. The feasibility of pruning to reduce the complexity of the fusion models was also studied.
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