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Chu R, Wei J, Lu W, Dong C, Chen Y. MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection. Comput Biol Med 2024; 179:108842. [PMID: 38996552 DOI: 10.1016/j.compbiomed.2024.108842] [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: 10/30/2023] [Revised: 04/15/2024] [Accepted: 06/04/2024] [Indexed: 07/14/2024]
Abstract
The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.
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Affiliation(s)
- Ronghe Chu
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Jianguo Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Wenhuan Lu
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Chaoyu Dong
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
| | - Yibing Chen
- Department of Pulmonary and Critical Care Medicine, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China.
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2
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Jiang C, Xie W, Zheng J, Yan B, Luo J, Zhang J. MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice. BIOSENSORS 2024; 14:406. [PMID: 39194635 DOI: 10.3390/bios14080406] [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: 05/31/2024] [Revised: 08/20/2024] [Accepted: 08/20/2024] [Indexed: 08/29/2024]
Abstract
Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial-temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial-temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.
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Affiliation(s)
- Chengyong Jiang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Wenbin Xie
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Jiadong Zheng
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Biao Yan
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Junwen Luo
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Jiayi Zhang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
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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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Li H, Lin X, Lu Y, Wang M, Cheng H. Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation. Physiol Meas 2023; 44:085003. [PMID: 37506712 DOI: 10.1088/1361-6579/acebb5] [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: 09/22/2022] [Accepted: 07/28/2023] [Indexed: 07/30/2023]
Abstract
Objective.Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs.Approach.To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People's Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism.Main results.The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation.Significance.The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.
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Affiliation(s)
- Heng Li
- Shenzhen Key Laboratory of IoT Key Technology, Harbin Institute of Technology, Shenzhen 518055, People's Republic of China
| | - Xu Lin
- Shenzhen Key Laboratory of IoT Key Technology, Harbin Institute of Technology, Shenzhen 518055, People's Republic of China
| | - Yun Lu
- Shenzhen Key Laboratory of IoT Key Technology, Harbin Institute of Technology, Shenzhen 518055, People's Republic of China
- School of Computer Science and Engineering, Huizhou University, Huizhou, Guangdong 516007, People's Republic of China
| | - Mingjiang Wang
- Shenzhen Key Laboratory of IoT Key Technology, Harbin Institute of Technology, Shenzhen 518055, People's Republic of China
| | - Hanrong Cheng
- Department of Sleep Medicine, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China
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Jiang X, Ren Y, Wu H, Li Y, Liu F. Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection. Front Neurosci 2023; 17:1222715. [PMID: 37547138 PMCID: PMC10400763 DOI: 10.3389/fnins.2023.1222715] [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: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome. Methods In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments. Results and Discussion The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.
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Affiliation(s)
- Xinge Jiang
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - YongLian Ren
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Hua Wu
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - Yanxiu Li
- School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China
| | - Feifei Liu
- School of Science, Shandong Jianzhu University, Jinan, China
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6
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Wang S, Xuan W, Chen D, Gu Y, Liu F, Chen J, Xia S, Dong S, Luo J. Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis. BIOSENSORS 2023; 13:bios13040483. [PMID: 37185558 PMCID: PMC10136920 DOI: 10.3390/bios13040483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023]
Abstract
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
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Affiliation(s)
- Shaokui Wang
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Weipeng Xuan
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Ding Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yexin Gu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fuhai Liu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jinkai Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Shudong Xia
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China
| | - Shurong Dong
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jikui Luo
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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Valenti S, Volpes G, Parisi A, Peri D, Lee J, Faes L, Busacca A, Pernice R. Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements. BIOSENSORS 2023; 13:bios13040460. [PMID: 37185535 PMCID: PMC10136507 DOI: 10.3390/bios13040460] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023]
Abstract
The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO2), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO2 calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO2 across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.
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Affiliation(s)
- Simone Valenti
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Gabriele Volpes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Antonino Parisi
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Daniele Peri
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Jinseok Lee
- Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Alessandro Busacca
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
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8
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Massie F, Vits S, Khachatryan A, Van Pee B, Verbraecken J, Bergmann J. Central Sleep Apnea Detection by Means of Finger Photoplethysmography. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:126-136. [PMID: 36704242 PMCID: PMC9873144 DOI: 10.1109/jtehm.2023.3236393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/15/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Obstructive Sleep Apnea (OSA) and Central Sleep Apnea (CSA) are two types of Sleep Apnea (SA) with different etiologies and treatment options. Home sleep apnea testing based on photoplethysmography-derived peripheral arterial tonometry (PAT HSAT) has become the most widely deployed outpatient SA diagnostic method. Being able to differentiate between CSA and OSA based solely on photoplethysmography-data would further increase PAT HSAT's clinical utility. The present work proposes a method to detect CSA using finger photoplethysmography (PPG) data and evaluates the proposed method against simultaneous in-lab polysomnography (PSG). METHODS For 266 patients with a suspicion of SA, concurrent in-lab PSG and PPG data were acquired. The respiratory information embedded in the PPG data was extracted and used to train an ensemble of trees classifiers that predicts the central or obstructive nature of each respiratory event. The classifier performance was evaluated using patient-wise leave-one-out cross-validation where an expert analysis of the PSG served as ground truth. A second, independent analysis of the PSG was also evaluated against the ground truth to allow benchmarking of the PPG-based method. RESULTS The method achieved a sensitivity of 81%, a specificity of 99%, a positive predictive value of 90%, and a negative predictive value of 98% at the central apnea-hypopnea index cutoff of 10 events per hour of sleep. CONCLUSION AND SIGNIFICANCE The present study aimed to evaluate a method to detect CSA in SA patients using only PPG data which could be used to flag CSA which in turn may aid in more optimal therapy decision making.
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Affiliation(s)
- Frederik Massie
- Natural Interaction LabDepartment of EngineeringUniversity of Oxford OX1 2JD Oxford U.K
| | - Steven Vits
- Research Group LEMPFaculty of Medicine and Health Sciences, University of Antwerp 2000 Antwerp Belgium
| | | | - Bart Van Pee
- Natural Interaction LabDepartment of EngineeringUniversity of Oxford OX1 2JD Oxford U.K
| | - Johan Verbraecken
- Research Group LEMPFaculty of Medicine and Health Sciences, University of Antwerp 2000 Antwerp Belgium
- Medicine and Multidisciplinary Sleep Disorders CentreDepartment of PulmonaryAntwerp University Hospital 2650 Edegem Belgium
| | - Jeroen Bergmann
- Natural Interaction LabDepartment of EngineeringUniversity of Oxford OX1 2JD Oxford U.K
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9
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Riha RL, Celmina M, Cooper B, Hamutcu-Ersu R, Kaditis A, Morley A, Pataka A, Penzel T, Roberti L, Ruehland W, Testelmans D, van Eyck A, Grundström G, Verbraecken J, Randerath W. ERS technical standards for using type III devices (limited channel studies) in the diagnosis of sleep disordered breathing in adults and children. Eur Respir J 2023; 61:13993003.00422-2022. [PMID: 36609518 DOI: 10.1183/13993003.00422-2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/27/2022] [Indexed: 02/01/2023]
Abstract
For more than three decades, type III devices have been used in the diagnosis of sleep disordered breathing in supervised as well as unsupervised settings. They have satisfactory positive and negative predictive values for detecting obstructive and central sleep apnoea in populations with moderately high pre-test probability of symptoms associated with these events. However, standardisation of commercially available type III devices has never been undertaken and the technical specifications can vary widely. None have been subjected to the same rigorous processes as most other diagnostic modalities in the medical field. Although type III devices do not include acquisition of electroencephalographic signals overnight, the minimum number of physical sensors required to allow for respiratory event scoring using standards outlined by the American Academy of Sleep Medicine remains debatable. This technical standard summarises data on type III studies published since 2007 from multiple perspectives in both adult and paediatric sleep practice. Most importantly, it aims to provide a framework for considering current type III device limitations in the diagnosis of sleep disordered breathing while raising research- and practice-related questions aimed at improving our use of these devices in the present and future.
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Affiliation(s)
- Renata L Riha
- Department of Sleep Medicine, The Royal Infirmary Edinburgh, Edinburgh, UK
| | - Marta Celmina
- Epilepsy and Sleep Medicine Centre, Children's Clinical University Hospital, Riga, Latvia
| | - Brendan Cooper
- Lung Function and Sleep, University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Edgbaston, UK
| | | | - Athanasios Kaditis
- Division of Paediatric Pulmonology and Sleep Disorders Laboratory, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece
| | | | - Athanasia Pataka
- Respiratory Failure Unit, G. Papanikolaou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Thomas Penzel
- Department of Cardiology and Angiology, Interdisciplinary Center of Sleep Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Warren Ruehland
- Institute for Breathing and Sleep, Austin Health, Melbourne, Australia
| | - Dries Testelmans
- Department of Pneumology, University Hospitals Leuven, Leuven, Belgium
| | - Annelies van Eyck
- Laboratory of Experimental Medicine and Pediatrics, University of Antwerp, Antwerp (Edegem), Belgium
- Department of Pediatrics, Antwerp University Hospital, Antwerp (Edegem), Belgium
| | | | - Johan Verbraecken
- Antwerp University Hospital and University of Antwerp, Edegem (Antwerp), Belgium
| | - Winfried Randerath
- Bethanien Hospital, Clinic of Pneumology and Allergology, Center for Sleep Medicine and Respiratory Care, Institute of Pneumology at the University of Cologne, Solingen, Germany
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10
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Melekoglu E, Kocabicak U, Uçar MK, Bilgin C, Bozkurt MR, Cunkas M. A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence. PeerJ Comput Sci 2022; 8:e1188. [PMID: 37346306 PMCID: PMC10280226 DOI: 10.7717/peerj-cs.1188] [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: 10/07/2022] [Accepted: 11/22/2022] [Indexed: 06/23/2023]
Abstract
Background and Purpose Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance. Results This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase. Conclusion According to the findings, PPG-based COPD prediction models are suitable for usage in practice.
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Affiliation(s)
| | - Umit Kocabicak
- Computer Engineering, Sakarya University, Sakarya, Turkey
| | | | - Cahit Bilgin
- Faculty of Medicine, Sakarya University, Sakarya, Turkey
| | | | - Mehmet Cunkas
- Electrical and Electronics Engineering, Selcuk University, Konya, Turkey
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11
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Chen M, Wu S, Chen T, Wang C, Liu G. Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets. BIOSENSORS 2022; 12:1089. [PMID: 36551056 PMCID: PMC9775447 DOI: 10.3390/bios12121089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
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Affiliation(s)
- Mingjing Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1112, USA
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Tian Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
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12
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Andreozzi E, Sabbadini R, Centracchio J, Bifulco P, Irace A, Breglio G, Riccio M. Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197566. [PMID: 36236663 PMCID: PMC9570799 DOI: 10.3390/s22197566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/26/2022] [Accepted: 10/01/2022] [Indexed: 05/31/2023]
Abstract
Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.
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13
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MonEco: a Novel Health Monitoring Ecosystem to Predict Respiratory and Cardiovascular Disorders. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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14
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Lin X, Cheng H, Lu Y, Luo H, Li H, Qian Y, Zhou L, Zhang L, Wang M. Contactless sleep apnea detection in snoring signals using hybrid deep neural networks targeted for embedded hardware platform with real-time applications. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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15
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Sharma P, Jalali A, Majmudar M, Rajput KS, Selvaraj N. Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2611-2614. [PMID: 36085724 DOI: 10.1109/embc48229.2022.9871295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≥40 years) from the sleep heart health study dataset with annotated sleep events have been employed in this study. A deep-learning model is trained to detect apnea in successive 30 s epochs and performances are assessed on two independent sub-cohorts of test data. The proposed algorithm showcases the highest test performance of 90.4 % area under the receiver operating characteristic curve and 58.9% area under the precision-recall curve for epoch-based apnea detection. Additionally, the model consistently performs well across various apnea subtypes, with the highest sensitivity of 93.4 % for obstructive apnea detection followed by 90.5 % for central apnea and 89.1 % for desaturation associated hypopnea. Overall, the proposed algorithm provides a robust and sensitive approach for sleep apnea event detection using a noninvasive pulse oximeter sensor. Clinical Relevance - The study establishes high sensitivity for automated epoch-based apnea detection across a diverse study cohort with various comorbidities using simply a pulse oximeter. This highly cost-effective approach could also enable convenient sleep and health monitoring over long-term.
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16
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Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare (Basel) 2022; 10:547. [PMID: 35327025 PMCID: PMC8950880 DOI: 10.3390/healthcare10030547] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023] Open
Abstract
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
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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.); (S.A.-A.)
- Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
| | - Md Saiful Islam
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Ahmed S. BaHammam
- The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia;
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17
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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18
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Leppänen T, Kainulainen S, Korkalainen H, Sillanmäki S, Kulkas A, Töyräs J, Nikkonen S. Pulse Oximetry: The Working Principle, Signal Formation, and Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:205-218. [PMID: 36217086 DOI: 10.1007/978-3-031-06413-5_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Pulse oximeters are routinely used in various medical-grade and consumer-grade applications. They can be used to estimate, for example, blood oxygen saturation, autonomic nervous system activity and cardiac function, blood pressure, sleep quality, and recovery through the recording of photoplethysmography signal. Medical-grade devices often record red and infra-red light-based photoplethysmography signals while smartwatches and other consumer-grade devices usually rely on a green light. At its simplest, a pulse oximeter can consist of one or two photodiodes and a photodetector attached, for example, a fingertip or earlobe. These sensors are used to record light absorption in a medium as a function of time. This time-varying absorption information is used to form a photoplethysmography signal. In this chapter, we discuss the working principles of pulse oximeters and the formation of the photoplethysmography signal. We will further discuss the advantages and disadvantages of pulse oximeters, which kind of applications exist in the medical field, and how pulse oximeters are utilized in daily health monitoring.
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Affiliation(s)
- Timo Leppänen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
| | - Samu Kainulainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Saara Sillanmäki
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Antti Kulkas
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Neurophysiology, Seinäjoki Central Hospital, Seinäjoki, Finland
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Sami Nikkonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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19
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Lazazzera R, Laguna P, Gil E, Carrault G. Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove. SENSORS 2021; 21:s21237976. [PMID: 34883979 PMCID: PMC8659764 DOI: 10.3390/s21237976] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022]
Abstract
The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.
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Affiliation(s)
- Remo Lazazzera
- Laboratoire Traitement du Signal et de l’Image (LTSI-Inserm UMR 1099), Université de Rennes 1, 35000 Rennes, France;
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, I3A, IIS Aragón, University of Zaragoza, and with the CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain; (P.L.); (E.G.)
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, I3A, IIS Aragón, University of Zaragoza, and with the CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain; (P.L.); (E.G.)
| | - Guy Carrault
- Laboratoire Traitement du Signal et de l’Image (LTSI-Inserm UMR 1099), Université de Rennes 1, 35000 Rennes, France;
- Correspondence:
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20
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Karavaev AS, Borovik AS, Borovkova EI, Orlova EA, Simonyan MA, Ponomarenko VI, Skazkina VV, Gridnev VI, Bezruchko BP, Prokhorov MD, Kiselev AR. Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure. Biophys J 2021; 120:2657-2664. [PMID: 34087217 PMCID: PMC8390904 DOI: 10.1016/j.bpj.2021.05.020] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/18/2021] [Accepted: 05/17/2021] [Indexed: 11/30/2022] Open
Abstract
The question of how much information the photoplethysmogram (PPG) signal contains on the autonomic regulation of blood pressure (BP) remains unsolved. This study aims to compare the low-frequency (LF) and high-frequency components of PPG and BP and assess their correlation with oscillations in interbeat (RR) intervals at similar frequencies. The PPG signal from the distal phalanx of the right index finger recorded using a reflective PPG sensor at green light, the BP signal from the left hand recorded using a Finometer, and RR intervals were analyzed. These signals were simultaneously recorded within 15 min in a supine resting condition in 17 healthy subjects (12 males and 5 females) aged 33 ± 9 years (mean ± SD). The study revealed the high coherence of LF components of PPG and BP with the LF component of RR intervals. The high-frequency components of these signals had low coherence. The analysis of the signal instantaneous phases revealed the presence of high-phase coherence between the LF components of PPG and BP. It is shown that the LF component of PPG is determined not only by local myogenic activity but also reflects the processes of autonomic control of BP.
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Affiliation(s)
- Anatoly S Karavaev
- Saratov State Medical University, Saratov, Russia; Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Anatoly S Borovik
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina I Borovkova
- Saratov State Medical University, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Eugeniya A Orlova
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | | | - Vladimir I Ponomarenko
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | | | - Vladimir I Gridnev
- Saratov State Medical University, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Boris P Bezruchko
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Mikhail D Prokhorov
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia
| | - Anton R Kiselev
- Saratov State Medical University, Saratov, Russia; National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia.
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