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Vitazkova D, Foltan E, Kosnacova H, Micjan M, Donoval M, Kuzma A, Kopani M, Vavrinsky E. Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies. BIOSENSORS 2024; 14:90. [PMID: 38392009 PMCID: PMC10886711 DOI: 10.3390/bios14020090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/28/2024] [Accepted: 02/03/2024] [Indexed: 02/24/2024]
Abstract
This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.
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Affiliation(s)
- Diana Vitazkova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Erik Foltan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Helena Kosnacova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia
| | - Michal Micjan
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Anton Kuzma
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
| | - Martin Kopani
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
| | - Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (E.F.); (H.K.); (M.M.); (M.D.); (A.K.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
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Abu K, Khraiche ML, Amatoury J. Obstructive sleep apnea diagnosis and beyond using portable monitors. Sleep Med 2024; 113:260-274. [PMID: 38070375 DOI: 10.1016/j.sleep.2023.11.034] [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: 07/03/2023] [Revised: 08/03/2023] [Accepted: 11/21/2023] [Indexed: 01/07/2024]
Abstract
Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.
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Affiliation(s)
- Kareem Abu
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Neural Engineering and Nanobiosensors Group, American University of Beirut, Beirut, Lebanon; Sleep and Upper Airway Research Group (SUARG), American University of Beirut, Beirut, Lebanon
| | - Massoud L Khraiche
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Neural Engineering and Nanobiosensors Group, American University of Beirut, Beirut, Lebanon
| | - Jason Amatoury
- Biomedical Engineering Program, Maroun Semaan Faculty of Engineering and Architecture (MSFEA), American University of Beirut, Beirut, Lebanon; Sleep and Upper Airway Research Group (SUARG), American University of Beirut, Beirut, Lebanon.
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Jung H, Kim D, Choi J, Joo EY. Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:7976. [PMID: 37766031 PMCID: PMC10536355 DOI: 10.3390/s23187976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/10/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland-Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW's ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.
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Affiliation(s)
- Hyunjun Jung
- Samsung Electronics, Suwon 16677, Republic of Korea
| | - Dongyeop Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Republic of Korea
| | - Jongmin Choi
- Samsung Electronics, Suwon 16677, Republic of Korea
| | - Eun Yeon Joo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
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Romano C, Nicolò A, Innocenti L, Bravi M, Miccinilli S, Sterzi S, Sacchetti M, Schena E, Massaroni C. Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone. BIOSENSORS 2023; 13:637. [PMID: 37367002 DOI: 10.3390/bios13060637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023]
Abstract
Emerging evidence suggests that respiratory frequency (fR) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating fR from breath sounds during walking and running. fR was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and -2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of -0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating fR during exercise.
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Affiliation(s)
- Chiara Romano
- Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Andrea Nicolò
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - Lorenzo Innocenti
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - Marco Bravi
- Unit of Physical and Rehabilitative Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Sandra Miccinilli
- Unit of Physical and Rehabilitative Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Silvia Sterzi
- Unit of Physical and Rehabilitative Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Massimo Sacchetti
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", 00135 Rome, Italy
| | - Emiliano Schena
- Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Carlo Massaroni
- Departmental Faculty of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:6197564. [PMID: 36818388 PMCID: PMC9935909 DOI: 10.1155/2023/6197564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/03/2022] [Accepted: 11/24/2022] [Indexed: 02/11/2023]
Abstract
Sleep-related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel-scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.
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A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram. Sci Rep 2022; 12:5913. [PMID: 35396565 PMCID: PMC8993803 DOI: 10.1038/s41598-022-09905-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/23/2022] [Indexed: 12/23/2022] Open
Abstract
Research in deep learning (DL) has continued to provide significant solutions to the challenges of detecting breast cancer in digital images. Image preprocessing methods and architecture enhancement techniques have been proposed to improve the performance of DL models such as convolutional neural networks (CNNs). For instance, the wavelet decomposition function has been used for image feature extraction in CNNs due to its strong compactness. Additionally, CNN architectures have been optimized to improve the process of feature detection to support the classification process. However, these approaches still lack completeness, as no mechanism exists to discriminate features to be enhanced and features to be eliminated for feature enhancement. More so, no studies have approached the use of wavelet transform to restructure CNN architectures to improve the detection of discriminant features in digital mammography for increased classification accuracy. Therefore, this study addresses these problems through wavelet-CNN-wavelet architecture. The approach presented in this paper combines seam carving and wavelet decomposition algorithms for image preprocessing to find discriminative features. These features are passed as input to a CNN-wavelet structure that uses the new wavelet transformation function proposed in this paper. The CNN-wavelet architecture applied layers of wavelet transform and reduced feature maps to obtain features suggestive of abnormalities that support the classification process. Meanwhile, we synthesized image samples with architectural distortion using a generative adversarial network (GAN) model to argue for their training datasets' insufficiency. Experimentation of the proposed method was carried out using DDSM + CBIS and MIAS datasets. The results obtained showed that the new method improved the classification accuracy and lowered the loss function values. The study's findings demonstrate the usefulness of the wavelet transform function in restructuring CNN architectures for performance enhancement in detecting abnormalities leading to breast cancer in digital mammography.
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Montazeri Ghahjaverestan N, Fan W, Aguiar C, Yu J, Bradley TD. Respiratory Motion and Airflow Estimation During Sleep Using Tracheal Movement and Sound. Nat Sci Sleep 2022; 14:1213-1223. [PMID: 35800029 PMCID: PMC9255718 DOI: 10.2147/nss.s360970] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/07/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Due to lack of access and high cost of polysomnography, portable sleep apnea testing has been developed to diagnose sleep apnea. Despite being less expensive, and having fewer sensors and reasonable accuracy in identifying sleep apnea, such devices can be less accurate than polysomnography in detecting apneas/hypopneas. To increase the accuracy of apnea/hypopnea detection, an accurate airflow estimation is required. However, current airflow measurement techniques employed in portable devices are inconvenient and subject to displacement during sleep. In this study, algorithms were developed to estimate respiratory motion and airflow using tracheo-sternal motion and tracheal sounds. PATIENTS AND METHODS Adults referred for polysomnography were included. Simultaneous to polysomnography, a patch device with an embedded 3-dimensional accelerometer and microphone was affixed to the suprasternal notch to record tracheo-sternal motion and tracheal sounds, respectively. Tracheo-sternal motion was used to train two mathematical models for estimating changes in respiratory motion and airflow compared to simultaneously measured thoracoabdominal motion and nasal pressure from polysomnography. The amplitude of the estimated airflow was then adjusted by the tracheal sound envelope in segments with unstable breathing. RESULTS Two hundred and fifty-two subjects participated in this study. Overall, the algorithms provided highly accurate estimates of changes in respiratory motion and airflow with mean square errors (MSE) of 3.58 ± 0.82% and 2.82 ± 0.71%, respectively, compared to polysomnographic signals. The estimated motion and airflow from the patch signals detected apneas and hypopneas scored on polysomnography in 63.9% and 88.3% of cases, respectively. CONCLUSION This study presents algorithms to accurately estimate changes in respiratory motion and airflow, which provides the ability to detect respiratory events during sleep. Our study suggests that such a simple and convenient method could be used for portable monitoring to detect sleep apnea. Further studies will be required to test this possibility.
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Affiliation(s)
- Nasim Montazeri Ghahjaverestan
- Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Wei Fan
- Bresotec Inc., Toronto, Ontario, Canada
| | | | | | - T Douglas Bradley
- Sleep Research Laboratory of the University Health Network Toronto Rehabilitation Institute, Toronto, Ontario, Canada.,Bresotec Inc., Toronto, Ontario, Canada.,Department of Medicine of the University of Toronto, Toronto, Ontario, Canada.,Department of Medicine of the University Health Network Toronto General Hospital, Toronto, Ontario, Canada
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An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring. ELECTRONICS 2021. [DOI: 10.3390/electronics10172178] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.
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Prediction of obstructive sleep apnea using Fast Fourier Transform of overnight breath recordings. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
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Ben Salah G, Abbes K, Abdelmoula C, Naji B, Masmoudi M, Abdelmoula MH, Turki M. An efficient design for real-time obstructive sleep apnea OSA detection through esophageal pressure Pes signal. ACTA ACUST UNITED AC 2021; 66:473-487. [PMID: 33951763 DOI: 10.1515/bmt-2020-0207] [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: 08/05/2020] [Accepted: 04/12/2021] [Indexed: 11/15/2022]
Abstract
Obstructive Sleep Apnea (OSA) is a potentially common sleep disorder in which the upper airways are collapsed either partially or completely. The golden standard method for treating OSA, is the full night Continuous Positive Airway Pressure (CPAP). Yet, due to the ensuing discomfort, it incurs on patients, researchers have been motivated to investigate other alternatives, whereby, OSA can be effectively treated. Recently, an increasingly popular OSA treatment has been developed that consists in activating the protrusion muscles of the tongue by stimulating the Hypoglossal Nerve (HGN). In this context, the present work is conducted to propose the design of apnea detector module as part of an implantable HGN stimulator based on the esophageal Pressure Pes signal as a new approach for controlling OSA occurrence. Specifically, an effective real-time apnea event detecting algorithm is put forward. Following the achievement of satisfactory simulation results, attained through the Modelsim simulation tool, we proceeded with assessing the possibility of its hardware implementation on a Field-Programmable Gate Array (FPGA) device. To this end, the apnea detector module was synthesized and designed. The low power consumption and the small size, characterizing this module, which have made it possible to integrate it as part of a wirelessly-powered implantable HGN stimulator.
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Affiliation(s)
- Ghada Ben Salah
- Electrical Engineering Department, METS Laboratory, National School of Engineers of Sfax ENIS, University of Sfax, Sfax, Tunisia
| | - Karim Abbes
- Physics Department, METS Laboratory, Faculty of Sciences of Sfax FSS, University of Sfax, Sfax, Tunisia
| | - Chokri Abdelmoula
- Industrial Computing Department, METS Laboratory, National School of Electronics and Telecommunications of Sfax ENET'Com, University of Sfax, Sfax, Tunisia
| | - Baligh Naji
- Electrical Engineering Department, METS Laboratory, National School of Engineers of Sfax ENIS, University of Sfax, Sfax, Tunisia
| | - Mohamed Masmoudi
- Electrical Engineering Department, METS Laboratory, National School of Engineers of Sfax ENIS, University of Sfax, Sfax, Tunisia
| | | | - Mohamed Turki
- Tunisian Society of Sleep Medicine TSSM, Tunis, Tunisia
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Chen Y, Wang W, Guo Y, Zhang H, Chen Y, Xie L. A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea. Nat Sci Sleep 2021; 13:1533-1544. [PMID: 34557047 PMCID: PMC8453177 DOI: 10.2147/nss.s323286] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/01/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Obstructive sleep apnea (OSA), the most common upper-airway disease, is closely associated with the risk of cardiovascular diseases. However, the early screening of OSA is a main challenge, relying on polysomnography (PSG) or home sleep apnea test (HSAT) in hospitals. Photoplethysmography (PPG) has been developed as a novel technology for screening of OSA, while the validation of PPG-based smart devices is limited compared to that for PSG or HSAT devices. OBJECTIVE This study aimed to investigate the feasibility and validity of a PPG-based smartwatch in the screening of OSA. METHODS A total of 119 patients were recruited from the Chinese People's Liberty Army General Hospital (Beijing, China). Among them, 20 patients were assessed for a whole-night sleep study by a smartwatch and PSG simultaneously, as well as 82 cases by a smartwatch and HSAT simultaneously. Using PSG or HSAT as the "gold standard", we compared the accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and positive likelihood ratio (+LR) or negative likelihood ratio (-LR) at three apnea hypopnea index (AHI) levels: AHI≥5, AHI≥15, and AHI≥30. RESULTS A total of 17/119 patients were excluded from the study due to the poor quality of PPG signals. Among the remaining cases, 83 patients were diagnosed with OSA. Compared to HSAT device, the accuracy, sensitivity, and specificity of the PPG-based smartwatch in predicting moderate-to-severe OSA patients (AHI≥15) were 87.9%, 89.7%, and 86.0%, respectively. Compared to PSG device, the accuracy, sensitivity, and specificity of the PPG-based smartwatch in predicting OSA in patients (AHI≥5) were 81.1%, 76.5%, and 100%, respectively. CONCLUSION The PPG-based smartwatch outperformed in terms of detecting OSA; nevertheless, validation in a large-scale population is imperative. TRIAL REGISTRATION Chinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.
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Affiliation(s)
- Yibing Chen
- Department of Respiratory and Critical Care Medicine, Senior Department of Respiratory and Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, People's Republic of China
| | - Weifang Wang
- Department of Respiratory and Critical Care Medicine, Senior Department of Respiratory and Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, People's Republic of China
| | - Yutao Guo
- Senior Department of Cardiology, The Six Medical Center of PLA General Hospital, Beijing, People's Republic of China
| | - Hui Zhang
- Senior Department of Cardiology, The Six Medical Center of PLA General Hospital, Beijing, People's Republic of China
| | - Yundai Chen
- Senior Department of Cardiology, The Six Medical Center of PLA General Hospital, Beijing, People's Republic of China
| | - Lixin Xie
- Senior Department of Respiratory and Critical Care Medicine, The Eighth Medical Center PLA General Hospital, Beijing, People's Republic of China
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Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning. SENSORS 2020; 20:s20226481. [PMID: 33202857 PMCID: PMC7698281 DOI: 10.3390/s20226481] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 11/17/2022]
Abstract
Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.
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13
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Jarchi D, Andreu-Perez J, Kiani M, Vysata O, Kuchynka J, Prochazka A, Sanei S. Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning. SENSORS 2020; 20:s20092594. [PMID: 32370185 PMCID: PMC7248846 DOI: 10.3390/s20092594] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 11/16/2022]
Abstract
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.
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Affiliation(s)
- Delaram Jarchi
- Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UK; (J.A.-P.); (M.K.)
- Embedded and Intelligent Systems Laboratory, School of Computer Science and Electronics, University of Essex, Colchester CO4 3SQ, UK
- Correspondence:
| | - Javier Andreu-Perez
- Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UK; (J.A.-P.); (M.K.)
- Embedded and Intelligent Systems Laboratory, School of Computer Science and Electronics, University of Essex, Colchester CO4 3SQ, UK
| | - Mehrin Kiani
- Smart Health Technologies Group, School of Computer Science and Electronic Engineering; University of Essex, Colchester CO4 3SQ, UK; (J.A.-P.); (M.K.)
| | - Oldrich Vysata
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic; (O.V.); (A.P.)
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic;
| | - Jiri Kuchynka
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic;
| | - Ales Prochazka
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic; (O.V.); (A.P.)
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK;
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In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:959-979. [PMID: 31515685 DOI: 10.1007/s13246-019-00796-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 08/30/2019] [Indexed: 01/09/2023]
Abstract
Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.
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15
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A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med Hypotheses 2019; 127:15-22. [DOI: 10.1016/j.mehy.2019.03.026] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 03/11/2019] [Accepted: 03/26/2019] [Indexed: 11/20/2022]
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