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Nguyen HX, Nguyen DV, Pham HH, Do CD. MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem. IEEE J Biomed Health Inform 2024; 28:4878-4890. [PMID: 38713565 DOI: 10.1109/jbhi.2024.3397653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete ECG segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN. Our experiment results on the PhysioNet Apnea-ECG dataset (70 overnight recordings), and the UCDDB dataset (25 overnight recordings) revealed that our new feature extraction method achieved per-segment accuracies of up to 92.11% and 81.25%, respectively. Moreover, using the PhysioNet data, we achieved a per-recording accuracy of 100% and yielded the highest correlation of 0.989 compared to state-of-the-art methods. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models in DL, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea.
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Ma D, Li C, Shi W, Fan Y, Liang H, Li L, Zhang Z, Yeh CH. Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:520-532. [PMID: 39050620 PMCID: PMC11268930 DOI: 10.1109/jtehm.2024.3419805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/18/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024]
Abstract
Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, [Formula: see text], SDRatio, [Formula: see text], and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.
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Affiliation(s)
- Deshan Ma
- School of Information and ElectronicsBeijing Institute of TechnologyBeijing100811China
| | - Conghui Li
- Department of Child Rehabilitation MedicineThe Fifth Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan450052China
| | - Wenbin Shi
- School of Information and ElectronicsBeijing Institute of TechnologyBeijing100811China
- Key Laboratory of Brain Health Intelligent Evaluation and InterventionMinistry of Education (Beijing Institute of Technology)Beijing100811China
| | - Yong Fan
- Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General HospitalBeijing100036China
| | - Hong Liang
- Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General HospitalBeijing100036China
| | - Lixuan Li
- Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General HospitalBeijing100036China
| | - Zhengbo Zhang
- Centre for Artificial Intelligence in MedicineMedical Innovation Research DepartmentChinese PLA General HospitalBeijing100036China
| | - Chien-Hung Yeh
- School of Information and ElectronicsBeijing Institute of TechnologyBeijing100811China
- Key Laboratory of Brain Health Intelligent Evaluation and InterventionMinistry of Education (Beijing Institute of Technology)Beijing100811China
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Gupta K. A robust deep learning system for screening of obstructive sleep apnea using T-F spectrum of ECG signals. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 38829354 DOI: 10.1080/10255842.2024.2359635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
Obstructive sleep apnea (OSA) is a non-communicable sleep-related medical condition marked by repeated disruptions in breathing during sleep. It may induce various cardiovascular and neurocognitive complications. Electrocardiography (ECG) is a useful method for detecting numerous health-related disorders. ECG signals provide a less complex and non-invasive solution for the screening of OSA. Automated and accurate detection of OSA may enhance diagnostic performance and reduce the clinician's workload. Traditional machine learning methods typically involve several labor-intensive manual procedures, including signal decomposition, feature evaluation, selection, and categorization. This article presents the time-frequency (T-F) spectrum classification of de-noised ECG data for the automatic screening of OSA patients using deep convolutional neural networks (DCNNs). At first, a filter-fusion algorithm is used to eliminate the artifacts from the raw ECG data. Stock-well transform (S-T) is employed to change filtered time-domain ECG into T-F spectrums. To discriminate between apnea and normal ECG signals, the obtained T-F spectrums are categorized using benchmark Alex-Net and Squeeze-Net, along with a less complex DCNN. The superiority of the presented system is measured by computing the sensitivity, specificity, accuracy, negative predicted value, precision, F1-score, and Fowlkes-Mallows index. The results of comparing all three utilized DCNNs reveal that the proposed DCNN requires fewer learning parameters and provides higher accuracy. An average accuracy of 95.31% is yielded using the proposed system. The presented deep learning system is lightweight and faster than Alex-Net and Squeeze-Net as it utilizes fewer learnable parameters, making it simple and reliable.
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Affiliation(s)
- Kapil Gupta
- School of Computer Sciences, University of Petroleum and Energy Studies (UPES), Dehradun, India
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Fan X, Chen X, Ma W, Gao W. BAFNet: Bottleneck Attention Based Fusion Network for Sleep Apnea Detection. IEEE J Biomed Health Inform 2024; 28:2473-2484. [PMID: 37216250 DOI: 10.1109/jbhi.2023.3278657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring.
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Rashidi S, Asl BM. Strength of ensemble learning in automatic sleep stages classification using single-channel EEG and ECG signals. Med Biol Eng Comput 2024; 62:997-1015. [PMID: 38114690 DOI: 10.1007/s11517-023-02980-2] [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: 04/08/2023] [Accepted: 11/26/2023] [Indexed: 12/21/2023]
Abstract
Healthy sleep plays an essential role in human daily life. Classification of sleep stages is a crucial tool for assisting physicians in diagnosing and treating sleep disorders. In this study, a strong ensemble learning model is proposed to enhance the ability of classification models in accurate sleep staging, particularly in multi-class classification. We asserted that high-accuracy sleep classification is achievable using only single-channel electroencephalogram (EEG) and electrocardiogram (ECG) by combining their best-extractable features in the time and frequency domains we recommended. More importantly, the superiority of the recommended method, which is the simultaneous use of stacking and bagging, over conventional machine learning classifiers in sleep staging was demonstrated, using the MIT-BIH Polysomnographic and Sleep-EDF expanded databases. Finally, K-fold cross-validation was used to fairly estimate these models. The best mean test accuracy rates for distinguishing between two classes of "sleep vs. wake," "rapid vs. non-rapid eye movement," and "deep vs. light sleep," were obtained 99.93%, 99.64%, and 99.69%, respectively. Furthermore, our proposed method achieved accuracies of 97.14%, 95.18%, 92.7%, and 85.64% for separating three, four, five, and six sleep classes, respectively. Compared to recent studies, our method outperforms other sleep stage classification schemes, especially in multi-class staging.
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Affiliation(s)
- Samandokht Rashidi
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Babak Mohammadzadeh Asl
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
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Lin Y, Zhang H, Wu W, Gao X, Chao F, Lin J. Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals. Phys Eng Sci Med 2024; 47:119-133. [PMID: 37982985 DOI: 10.1007/s13246-023-01346-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/12/2023] [Indexed: 11/21/2023]
Abstract
Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.
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Affiliation(s)
- Yuxing Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
| | - Wanqing Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
| | - Fei Chao
- School of Informatics, Xiamen University, Xiamen, 36100, China
| | - Juqiang Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China
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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [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: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Xiong X, Wang A, He J, Wang C, Liu R, Sun Z, Zhang J, Zhang J. Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection. Front Neurosci 2024; 18:1324933. [PMID: 38440395 PMCID: PMC10909841 DOI: 10.3389/fnins.2024.1324933] [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: 10/20/2023] [Accepted: 01/30/2024] [Indexed: 03/06/2024] Open
Abstract
Introduction Sleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients' suffering. Methods To this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany. Results The experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%. Discussion All of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor's diagnostic process and provide a better medical experience for patients.
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Affiliation(s)
- Xin Xiong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Aikun Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Jianfeng He
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Chunwu Wang
- College of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, China
| | - Ruixiang Liu
- Department of Clinical Psychology, Second People’s Hospital of Yunnan, Kunming, China
| | - Zhiran Sun
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Jiancong Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Jing Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China
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9
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Škorić T. Stress Level Detection Based on the Capacitive Electrocardiogram Signals of Driving Subjects. SENSORS (BASEL, SWITZERLAND) 2023; 23:9158. [PMID: 38005545 PMCID: PMC10674978 DOI: 10.3390/s23229158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023]
Abstract
The automotive industry and scientific community are making efforts to develop innovative solutions that would increase successful driver performance in preventing crashes caused by drivers' health and concentration. High stress is one of the causes of impaired driver performance. This study investigates the ability to classify different stress levels based on capacitive electrocardiogram (cECG) recorded during driving by unobtrusive acquisition systems with different hardware implementations. The proposed machine-learning model extracted only four features, based on the detection of the R peak, which is the most reliably detected characteristic point even in inferior quality cECG. Another criterion for selecting the features is their low computational complexity, which enables real-time application. The proposed method was validated on three open data sets recorded during driving: electrocardiogram (ECG) recorded by electrodes with direct skin contact (high quality); cECG recorded without direct skin contact through clothes by electrodes built into a portable multi-modal cushion (middle quality); and cECG recorded through the clothes without direct skin contact by electrodes built into a car seat (lowest quality). The proposed model achieved a high accuracy of 100% for high-quality ECG, 96.67% for middle-quality cECG, and 98.08% for the lower-quality cECG.
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Affiliation(s)
- Tamara Škorić
- Faculty of Technical Science, University of Novi Sad, 21000 Novi Sad, Serbia
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10
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Xia F, Li H, Li Y, Liu X, Xu Y, Fang C, Hou Q, Lin S, Zhang Z, Yang J, Sawan M. Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:8882. [PMID: 37960581 PMCID: PMC10648123 DOI: 10.3390/s23218882] [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: 09/23/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68% accuracy, achieved through the utilization of a convolutional neural network (CNN) algorithm. With a skin thickness of 5 mm and a receiving coil diameter of 9 mm, the power conversion efficiency was measured as 31.8%. The implantable device, on the other hand, is composed of a front-end CMOS power management module (PMM), a binary-phase-shift-keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 mm2 (excluding PADs), demonstrated a power conversion efficiency of 77.5% when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2 V, 1.8 V, and 3.1 V). Within the data receiver component, a low-power BPSK demodulator was ingeniously incorporated, consuming only 42 μW when supplied with a voltage of 0.7 V. The performance was achieved through the implementation of the self-biased phase-locked-loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5 bit programmable range spanning from 0 to 2.4 mA. The functionality of the proposed ECG- and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing the trauma and space requirements.
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Affiliation(s)
- Fen Xia
- Zhejiang University, Hangzhou 310024, China;
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Hanrui Li
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Yixi Li
- State Key Laboratory of Superlattices, Microstructures Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100045, China;
| | - Xing Liu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Yankun Xu
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Chaoming Fang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Qiming Hou
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Siyu Lin
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Zhao Zhang
- SAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Jie Yang
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
| | - Mohamad Sawan
- CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China; (H.L.)
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11
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J RG, K D. Apnoea detection using ECG signal based on machine learning classifiers and its performances. J Med Eng Technol 2023; 47:344-354. [PMID: 38625408 DOI: 10.1080/03091902.2024.2336500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 03/23/2024] [Indexed: 04/17/2024]
Abstract
Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.
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Affiliation(s)
- Rolant Gini J
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
| | - Dhanalakshmi K
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
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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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Tyagi PK, Agarwal D. Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach. Biomed Eng Lett 2023; 13:293-312. [PMID: 37519869 PMCID: PMC10382448 DOI: 10.1007/s13534-023-00297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/10/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing studies. However, physiological information collected as 1-dimensional data has yet to be effectively extracted from this new approach to achieve the needed medical goals. So, in this study, we review the most recent studies in the field of DL applied to physiological data based on pulse oxygen saturation, electrocardiogram, airflow, and sound signal. A total of 47 articles from different journals and publishing houses that were published between 2012 and 2022 were identified. The primary objective of this work is to perform a comprehensive analysis to analyze, classify, and compare the main characteristics of deep-learning algorithms applied in physiological data processing for SLA detection. Overall, our analysis provides comprehensive and detailed information for researchers looking to add to this field. The data input source, objective, DL network, training framework, and database references are the critical factors of the DL approach examined. These are the most critical variables that influence system performance. We categorized the relevant research studies in physiological sensor data analysis using the DL approach based on (1) Physiological sensor data aspects, like signal types, sampling frequency, and window size; and (2) DL model perspectives, such as learning structure and input data types. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00297-5.
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Affiliation(s)
- Praveen Kumar Tyagi
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
| | - Dheeraj Agarwal
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Sánchez C, Hernando A, Bolea J, Izquierdo D, Rodríguez G, Olea A, Lozano MT, Peláez-Coca MD. Enhancing Safety in Hyperbaric Environments through Analysis of Autonomic Nervous System Responses: A Comparison of Dry and Humid Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115289. [PMID: 37300016 DOI: 10.3390/s23115289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⟂¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as "normal" or "abnormal" was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets' ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses.
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Affiliation(s)
- Carlos Sánchez
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Alberto Hernando
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Juan Bolea
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - David Izquierdo
- GTF Group, I3A Institute, University of Zaragoza, 50018 Zaragoza, Spain
| | - Germán Rodríguez
- Departamento de Ingeniería y Técnicas Aplicadas, Centro Universitario de la Defensa de San Javier, Academia General del Aire, 30729 Murcia, Spain
| | - Agustín Olea
- Centro de Buceo de la Armada de Cartagena, 30205 Murcia, Spain
| | - María Teresa Lozano
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - María Dolores Peláez-Coca
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
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16
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Hemrajani P, Dhaka VS, Rani G, Shukla P, Bavirisetti DP. Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:4692. [PMID: 37430605 DOI: 10.3390/s23104692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 07/12/2023]
Abstract
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.
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Affiliation(s)
- Prashant Hemrajani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Geeta Rani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Praveen Shukla
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Durga Prasad Bavirisetti
- Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Martín-González S, Ravelo-García AG, Navarro-Mesa JL, Hernández-Pérez E. Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094267. [PMID: 37177472 PMCID: PMC10181515 DOI: 10.3390/s23094267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.
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Affiliation(s)
- Sofía Martín-González
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Juan L Navarro-Mesa
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Eduardo Hernández-Pérez
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
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Bhagubai M, Vandecasteele K, Swinnen L, Macea J, Chatzichristos C, De Vos M, Van Paesschen W. The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG. Bioengineering (Basel) 2023; 10:bioengineering10040491. [PMID: 37106678 PMCID: PMC10136326 DOI: 10.3390/bioengineering10040491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/07/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm's detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.
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Affiliation(s)
- Miguel Bhagubai
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Kaat Vandecasteele
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Lauren Swinnen
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
| | - Jaiver Macea
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
| | - Christos Chatzichristos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
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Detection of obstructive sleep apnea from single-channel ECG signals using a CNN-transformer architecture. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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20
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Sharaf AI. Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier. ENTROPY (BASEL, SWITZERLAND) 2023; 25:399. [PMID: 36981288 PMCID: PMC10047098 DOI: 10.3390/e25030399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Obstructive Sleep Apnea (OSA) is a common sleep-breathing disorder that highly reduces the quality of human life. The most powerful method for the detection and classification of sleep apnea is the Polysomnogram. However, this method is time-consuming and cost-inefficient. Therefore, several methods focus on using electrocardiogram (ECG) signals to detect sleep apnea. This paper proposed a novel automated approach to detect and classify apneic events from single-lead ECG signals. Wavelet Scattering Transformation (WST) was applied to the ECG signals to decompose the signal into smaller segments. Then, a set of features, including higher-order statistics and entropy-based features, was extracted from the WST coefficients to formulate a search space. The obtained features were fed to a random forest classifier to classify the ECG segments. The experiment was validated using the 10-fold and hold-out cross-validation methods, which resulted in an accuracy of 91.65% and 90.35%, respectively. The findings were compared with different classifiers to show the significance of the proposed approach. The proposed approach achieved better performance measures than most of the existing methodologies.
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Affiliation(s)
- Ahmed I Sharaf
- Deanship of Scientific Research, Umm Al-Qura University, Mecca 24382, Saudi Arabia
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21
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Rinkevičius M, Charlton PH, Bailón R, Marozas V. Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography. SENSORS (BASEL, SWITZERLAND) 2023; 23:2220. [PMID: 36850820 PMCID: PMC9967654 DOI: 10.3390/s23042220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.
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Affiliation(s)
- Mantas Rinkevičius
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
| | - Peter H. Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 1TN, UK
- Research Centre for Biomedical Engineering, University of London, London WC1E 7HU, UK
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50009 Zaragoza, Spain
- Biomedical Research Networking Center (CIBER), 50018 Zaragoza, Spain
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania
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Kumar Tyagi P, Agrawal D. Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104401] [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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Setiawan F, Lin CW. A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram. Life (Basel) 2022; 12:1509. [PMID: 36294943 PMCID: PMC9605343 DOI: 10.3390/life12101509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/30/2022] [Accepted: 09/14/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Although polysomnography (PSG) is a gold standard tool for diagnosing sleep apnea (SA), it can reduce the patient's sleep quality by the placement of several disturbing sensors and can only be interpreted by a highly trained sleep technician or scientist. In recent years, electrocardiogram (ECG)-derived respiration (EDR) and heart rate variability (HRV) have been used to automatically diagnose SA and reduce the drawbacks of PSG. Up to now, most of the proposed approaches focus on machine-learning (ML) algorithms and feature engineering, which require prior expert knowledge and experience. The present study proposes an SA detection algorithm to differentiate a normal and apnea event using a deep-learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal. The EMD is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. In addition, the simple and compact architecture of 1D deep CNN, which only performs 1D convolutions, and pretrained 2D deep CNNs, are suitable for real-time and low-cost hardware implementation. METHOD This study was validated using 7 h to nearly 10 h overnight ECG recordings from 33 subjects with an average apnea-hypopnea index (AHI) of 30.23/h originated from PhysioNet Apnea-ECG database (PAED). In preprocessing, the raw ECG signal was normalized and filtered using the FIR band pass filter. The preprocessed ECG signal was then decomposed using the empirical mode decomposition (EMD) technique to generate several features. Several important generated features were selected using neighborhood component analysis (NCA). Finally, deep learning algorithm based on 1D and 2D deep CNN were used to perform the classification of normal and apnea event. The synthetic minority oversampling technique (SMOTE) was also applied to evaluate the influence of the imbalanced data problem. RESULTS The segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity based on 5-fold cross-validation (5fold-CV), meanwhile, the subject-level classification performance had 83.5% accuracy with 75.9% sensitivity and 88.7% specificity based on leave-one-subject-out cross-validation (LOSO-CV). CONCLUSION A novel and robust SA detection algorithm based on the ECG decomposed signal using EMD and deep CNN was successfully developed in this study.
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Affiliation(s)
- Febryan Setiawan
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Che-Wei Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Medical Device Innovation Center, National Cheng Kung University, Tainan 701, Taiwan
- Institute of Medical Informatics, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan 701, Taiwan
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Detection of sleep apnea from single-channel electroencephalogram (EEG) using an explainable convolutional neural network (CNN). PLoS One 2022; 17:e0272167. [PMID: 36099242 PMCID: PMC9469966 DOI: 10.1371/journal.pone.0272167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 06/21/2022] [Indexed: 11/19/2022] Open
Abstract
Sleep apnea (SA) is a common disorder involving the cessation of breathing during sleep. It can cause daytime hypersomnia, accidents, and, if allowed to progress, serious, chronic conditions. Continuous positive airway pressure is an effective SA treatment. However, long waitlists impede timely diagnosis; overnight sleep studies involve trained technicians scoring a polysomnograph, which comprises multiple physiological signals including multi-channel electroencephalography (EEG). Therefore, it is important to develop simplified and automated approaches to detect SA. In the present study, we have developed an explainable convolutional neural network (CNN) to detect SA events from single-channel EEG recordings which generalizes across subjects. The network architecture consisted of three convolutional layers. We tuned hyperparameters using the Hyperband algorithm, optimized parameters using Adam, and quantified network performance with subjectwise 10-fold cross-validation. Our CNN performed with an accuracy of 69.9%, and a Matthews correlation coefficient (MCC) of 0.38. To explain the mechanisms of our trained network, we used critical-band masking (CBM): after training, we added bandlimited noise to test recordings; we parametrically varied the noise band center frequency and noise intensity, quantifying the deleterious effect on performance. We reconciled the effects of CBM with lesioning, wherein we zeroed the trained network’s 1st-layer filter kernels in turn, quantifying the deleterious effect on performance. These analyses indicated that the network learned frequency-band information consistent with known SA biomarkers, specifically, delta and beta band activity. Our results indicate single-channel EEG may have clinical potential for SA diagnosis.
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Huse S, Acharya S, Shukla S, J H, Sachdev A. Computer-Aided Detection and Diagnosis of Neurological Disorder. Cureus 2022; 14:e28032. [PMID: 36120284 PMCID: PMC9473453 DOI: 10.7759/cureus.28032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 08/15/2022] [Indexed: 12/01/2022] Open
Abstract
Nowadays, neurological problems are more regular, representing a worry to pregnant ladies, guardians, healthy babies, and kids. Neurological problems emerge in a wide assortment of structures, each with its arrangement of beginnings, inconveniences, and results. The conclusion of neurological illnesses is an evolving concern and predominantly troublesome difficulty for current medication. Current diagnosis advancements (e.g., MRI and EEG) produce immense information (in proportion and aspect) as location, checking, and therapy of nervous system illnesses. As a common rule, investigation of that enormous clinical information is performed physically by specialists to distinguish and figure out the irregularities. It is a genuinely troublesome errand for an individual to collect, make due, investigate, and absorb enormous amounts of information through visible review. As an outcome, the specialist has been requesting electronic conclusion frameworks known as “computer-aided diagnosis” that can consequently identify the nervous system irregularities utilizing the essential clinical information. This framework further develops uniformity of findings, builds treatment outcomes, protects lives, and lessens price and time. As of late, few examinations have improved the computer-aided design frameworks for the executives of enormous clinical information for determination appraisal. This paper investigates the difficulties of tremendous clinical information giving. This article fundamentally evaluated and looked at the exhibition of existing AI and comprehensive learning approaches for identifying nervous system illnesses. A far-reaching piece of this concentration also shows different modalities and illness-determined datasets that identify and record pictures, signs, addresses, and so forth. Restricted related works are additionally summed up on nervous system illnesses, as this space has essentially less work zeroed in on illness and recognition rules. A portion of the standard assessment measurements is likewise introduced in this review for improved outcome examination.
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Wang T, Lu C, Sun Y, Fang H, Jiang W, Liu C. A method to detect sleep apnea using residual attention mechanism network from single-lead ECG signal. BIOMED ENG-BIOMED TE 2022; 67:357-365. [PMID: 35920638 DOI: 10.1515/bmt-2022-0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/11/2022] [Indexed: 11/15/2022]
Abstract
Sleep apnea is a sleep disorder caused by weakened or suspended breathing during sleep, which seriously affects the work and health of patients. The traditional polysomnography (PSG) detection process is complicated and expensive, which has attracted researchers to explore a rapid detection method based on single-lead ECG signals. However, existing ECG-based sleep apnea detection methods have certain limitations and complexities, mainly relying on human-crafted features. To solve the problem, the paper develops a sleep apnea detection method based on a residual attention mechanism network. The method uses the RR interval signal and the R-peak signal derived from the ECG signal as input, realizes feature extraction through the residual network (ResNet), and adds the SENet attention mechanism to deepen the mining of channel features. Experimental results show that the per-segment accuracy of the proposed method can reach 86.2%. Compared with existing works, its accuracy has increased by 1.1-8.1%. These results show that the proposed residual attention network can effectively use ECG signals to quickly detect sleep apnea. Meanwhile, compared with existing works, the proposed method overcomes the limitations and complexity of human-crafted features in sleep apnea detection research.
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Affiliation(s)
- Tao Wang
- School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
| | - Changhua Lu
- School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
| | - Yining Sun
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Hengyang Fang
- School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
| | - Weiwei Jiang
- Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology, Hefei, Anhui, China
| | - Chun Liu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui, China
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Chen X, Chen Y, Ma W, Fan X, Li Y. Toward sleep apnea detection with lightweight multi-scaled fusion network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Ayatollahi A, Afrakhteh S, Soltani F, Saleh E. Sleep apnea detection from ECG signal using deep CNN-based structures. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09445-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Uddin MB, Chow CM, Ling SH, Su SW. A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry. Physiol Meas 2022; 43. [PMID: 35477173 DOI: 10.1088/1361-6579/ac6b11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/27/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals. APPROACH Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2 with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study. MAIN RESULTS Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively. SIGNIFICANCE This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
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Affiliation(s)
- Md Bashir Uddin
- Biomedical Engineering, Khulna University of Engineering and Technology, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh, Khulna, 9203, BANGLADESH
| | - Chin-Moi Chow
- Faculty of Health Sciences, The University of Sydney, The University of Sydney, Sydney, NSW 2006, Sydney, New South Wales, NSW 2006, AUSTRALIA
| | - Steve H Ling
- University of Technology Sydney, University of Technology Sydney, Sydney, NSW 2007, Sydney, New South Wales, NSW 2007, AUSTRALIA
| | - Steven W Su
- Biomedical Systems Laboratory, The University of New South Wales, Sydney 2052, N.S.W., Sydney, 2007, AUSTRALIA
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Balci M, Tasdemir S, Ozmen G, Golcuk A. Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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JeyaJothi ES, Anitha J, Rani S, Tiwari B. A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7242667. [PMID: 35224099 PMCID: PMC8866013 DOI: 10.1155/2022/7242667] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/22/2021] [Indexed: 02/06/2023]
Abstract
Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.
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Affiliation(s)
- E. Smily JeyaJothi
- Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641108, India
| | - J. Anitha
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura Punjab-140401, India
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Qin H, Liu G. A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Yang Q, Zou L, Wei K, Liu G. Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network. Comput Biol Med 2022; 140:105124. [PMID: 34896885 DOI: 10.1016/j.compbiomed.2021.105124] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 12/04/2021] [Accepted: 12/04/2021] [Indexed: 11/17/2022]
Abstract
Obstructive sleep apnea (OSA), which has high morbidity and complications, is diagnosed via polysomnography (PSG). However, this method is expensive, time-consuming, and causes discomfort to the patient. Single-lead electrocardiogram (ECG) is a potential alternative to PSG for OSA diagnosis. Recent studies have successfully applied deep learning methods to OSA detection using ECG and obtained great success. However, most of these methods only focus on heart rate variability (HRV), ignoring the importance of ECG-derived respiration (EDR). In addition, they used relatively simple networks, and cannot extract more complex features. In this study, we proposed a one-dimensional squeeze-and-excitation (SE) residual group network to thoroughly extract the complementary information between HRV and EDR. We used the released and withheld sets in the Apnea-ECG dataset to develop and test the proposed method, respectively. In the withheld set, the method has an accuracy of 90.3%, a sensitivity of 87.6%, and a specificity of 91.9% for per-segment detection, indicating an improvement over existing methods for the same dataset. The proposed method can be integrated with wearable devices to realize inexpensive, convenient, and highly efficient OSA detectors.
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Affiliation(s)
- Quanan Yang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Lang Zou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Keming Wei
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
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Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103125] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Lin CY, Wang YW, Setiawan F, Trang NTH, Lin CW. Sleep Apnea Classification Algorithm Development Using a Machine-Learning Framework and Bag-of-Features Derived from Electrocardiogram Spectrograms. J Clin Med 2021; 11:jcm11010192. [PMID: 35011934 PMCID: PMC8745785 DOI: 10.3390/jcm11010192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/24/2021] [Accepted: 12/29/2021] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram. METHODS This study was verified using overnight ECG recordings from 83 subjects with an average apnea-hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time-frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1-50, 8-50, 0.8-10, and 0-0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection. RESULTS The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1-50 Hz and 8-50 Hz frequency bands, respectively. CONCLUSION An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.
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Affiliation(s)
- Cheng-Yu Lin
- Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan;
- Department of Environmental and Occupational Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Sleep Medicine Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yi-Wen Wang
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan; (Y.-W.W.); (F.S.); (N.T.H.T.)
| | - Febryan Setiawan
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan; (Y.-W.W.); (F.S.); (N.T.H.T.)
| | - Nguyen Thi Hoang Trang
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan; (Y.-W.W.); (F.S.); (N.T.H.T.)
| | - Che-Wei Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan; (Y.-W.W.); (F.S.); (N.T.H.T.)
- Medical Device Innovation Center, National Cheng Kung University, Tainan 704, Taiwan
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Correspondence:
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Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques. Diagnostics (Basel) 2021; 11:diagnostics11122302. [PMID: 34943539 PMCID: PMC8700500 DOI: 10.3390/diagnostics11122302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 11/20/2022] Open
Abstract
Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.
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John A, Nundy KK, Cardiff B, John D. Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:1161-1173. [PMID: 34882563 DOI: 10.1109/tbcas.2021.3134043] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
With advances in circuit design and sensing technology, the acquisition of data from a large number of Internet of Things (IoT) sensors simultaneously to enable more accurate inferences has become mainstream. In this work, we propose a novel convolutional neural network (CNN) model for the fusion of multimodal and multiresolution data obtained from several sensors. The proposed model enables the fusion of multiresolution sensor data, without having to resort to padding/ resampling to correct for frequency resolution differences even when carrying out temporal inferences like high-resolution event detection. The performance of the proposed model is evaluated for sleep apnea event detection, by fusing three different sensor signals obtained from UCD St. Vincent University Hospital's sleep apnea database. The proposed model is generalizable and this is demonstrated by incremental performance improvements, proportional to the number of sensors used for fusion. A selective dropout technique is used to prevent overfitting of the model to any specific high-resolution input, and increase the robustness of fusion to signal corruption from any sensor source. A fusion model with electrocardiogram (ECG), Peripheral oxygen saturation signal (SpO2), and abdominal movement signal achieved an accuracy of 99.72% and a sensitivity of 98.98%. Energy per classification of the proposed fusion model was estimated to be approximately 5.61 μJ for on-chip implementation. The feasibility of pruning to reduce the complexity of the fusion models was also studied.
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Akbarian S, Ghahjaverestan NM, Yadollahi A, Taati B. Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation. J Med Internet Res 2021; 23:e26524. [PMID: 34723817 PMCID: PMC8593819 DOI: 10.2196/26524] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 04/13/2021] [Accepted: 09/10/2021] [Indexed: 12/16/2022] Open
Abstract
Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.
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Affiliation(s)
- Sina Akbarian
- Kite Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
| | - Nasim Montazeri Ghahjaverestan
- Kite Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Azadeh Yadollahi
- Kite Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Babak Taati
- Kite Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Afrakhteh S, Ayatollahi A, Soltani F. Classification of sleep apnea using EMD-based features and PSO-trained neural networks. BIOMED ENG-BIOMED TE 2021; 66:459-472. [PMID: 33930264 DOI: 10.1515/bmt-2021-0025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/12/2021] [Indexed: 11/15/2022]
Abstract
In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.
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Affiliation(s)
- Sajjad Afrakhteh
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Ahmad Ayatollahi
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Fatemeh Soltani
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
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Batista E, Moncusi MA, López-Aguilar P, Martínez-Ballesté A, Solanas A. Sensors for Context-Aware Smart Healthcare: A Security Perspective. SENSORS (BASEL, SWITZERLAND) 2021; 21:6886. [PMID: 34696099 PMCID: PMC8537585 DOI: 10.3390/s21206886] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people's welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.
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Affiliation(s)
- Edgar Batista
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
- SIMPPLE S.L., C. Joan Maragall 1A, 43003 Tarragona, Spain
| | - M. Angels Moncusi
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Pablo López-Aguilar
- Anti-Phishing Working Group EU, Av. Diagonal 621–629, 08028 Barcelona, Spain;
| | - Antoni Martínez-Ballesté
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
| | - Agusti Solanas
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Spain; (E.B.); (M.A.M.); (A.M.-B.)
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Vallat R, Walker MP. An open-source, high-performance tool for automated sleep staging. eLife 2021; 10:70092. [PMID: 34648426 PMCID: PMC8516415 DOI: 10.7554/elife.70092] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 09/29/2021] [Indexed: 12/02/2022] Open
Abstract
The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.
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Affiliation(s)
- Raphael Vallat
- Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, Berkeley, United States
| | - Matthew P Walker
- Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, Berkeley, United States
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Morales Tellez JF, Moeyersons J, Testelmans D, Buyse B, Borzée P, Van Hoof C, Groenendaal W, Van Huffel S, Varon C. Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy. Physiol Meas 2021; 42. [PMID: 34571494 DOI: 10.1088/1361-6579/ac2a70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/27/2021] [Indexed: 11/12/2022]
Abstract
BACKGROUND Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals. OBJECTIVE To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case. APPROACH Simulations were used to evaluate the model order selection when calculating the RSA estimates explained before, as well as 3 different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals. MAIN RESULTS It was found that using a single model order for all the calculations suffices to characterize the RSA estimates correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 minutes might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration. SIGNIFICANCE Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.
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Affiliation(s)
- John Fredy Morales Tellez
- ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Flanders, BELGIUM
| | - Jonathan Moeyersons
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
| | - Dries Testelmans
- Department of Pneumology, KU Leuven University Hospitals Leuven, Leuven, BELGIUM
| | - Bertien Buyse
- Department of Respiratory Diseases, KUL UZ Gasthuisberg, Leuven, Flanders, BELGIUM
| | - Pascal Borzée
- Department of Pneumology, KU Leuven University Hospitals Leuven, Leuven, BELGIUM
| | | | | | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
| | - Carolina Varon
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
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Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder. SENSORS 2021; 21:s21186264. [PMID: 34577471 PMCID: PMC8469191 DOI: 10.3390/s21186264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/13/2021] [Accepted: 09/15/2021] [Indexed: 01/09/2023]
Abstract
This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.
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Yu Y, Yang Z, You Y, Shan W. FASSNet: fast apnea syndrome screening neural network based on single-lead electrocardiogram for wearable devices. Physiol Meas 2021; 42. [PMID: 34315149 DOI: 10.1088/1361-6579/ac184e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 07/27/2021] [Indexed: 01/31/2023]
Abstract
Objective. Sleep apnea (SA) is a chronic condition that fragments sleep and results in intermittent hypoxemia, which in long run leads to cardiovascular diseases like stroke. Diagnosis of SA through polysomnography is costly, inconvenient, and has long waiting list. Wearable devices provide a low-cost solution to the ambulatory detection of SA syndrome for undiagnosed patients. One of the wearables are the ones based on minute-by-minute analysis of single-lead electrocardiogram (ECG) signal. Processing ECG segments online at wearables contributes to memory conservation and privacy protection in long-term SA monitoring, and light-weight models are required due to stringent computation resource.Approach.We propose fast apnea syndrome screening neural network (FASSNet), an effective end-to-end neural network to perform minute-apnea event detection. Low-frequency components of filtered ECG spectrogram are selected as input. The model initially processes the spectrogram via convolution blocks. Bidirectional long-short-term memory blocks are used along the frequency axis to complement position information of frequency bands. Layer normalisation is implemented to retain in-epoch information since apnea periods have variable lengths. Experiments were carried out on 70 recordings of Apnea-ECG database, where each 60 s ECG segment is manually labelled as an apnea or normal minute by technician. Both ten-fold and patient-agnostic validation protocols are adopted.Main results.FASSNet is light-weighted, since its value of model parameters and multiply accumulates are 0.06% and 28.33% of those of an AlexNet benchmark, respectively. Meanwhile, FASSNet achieves an accuracy of 87.09%, a sensitivity of 77.96%, a specificity of 91.74%, and an F1 score of 81.61% in apnea event detection. Its accuracy of diagnosing SA syndrome severity exceeds 90% under the patient-agnostic protocol.Significance:FASSNet is a computationally efficient and accurate neural network for wearables to detect SA events and estimate SA severity based on minute-level diagnosis.
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Affiliation(s)
- Yunkai Yu
- Beijing Institute of Technology, Beijing, CN, People's Republic of China
| | - Zhihong Yang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing, CN, People's Republic of China
| | - Yuyang You
- Beijing Institute of Technology, Beijing, CN, People's Republic of China
| | - Wenjing Shan
- Beijing Institute of Technology, Beijing, CN, People's Republic of China
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Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation. Sleep Breath 2021; 26:1033-1044. [PMID: 34409545 DOI: 10.1007/s11325-021-02465-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/11/2021] [Accepted: 08/02/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Sleep-related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to detect sleep apnea automatically from a simple, easy-to-wear device. The objective was to detect abnormal respiration and estimate the Apnea-Hypopnea Index (AHI) automatically with a wearable respiratory device with and without SpO2 signals using a large (n = 412) dataset serving as ground truth. DESIGN Simultaneously recorded polysomnography (PSG) and wearable respiratory effort data were used to train and evaluate models in a cross-validation fashion. Time domain and complexity features were extracted, important features were identified, and a random forest model was employed to detect events and predict AHI. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%) feature, one allowing a time lag of 30 s between the two signals. RESULTS Event-based classification resulted in areas under the receiver operating characteristic curves of 0.94, 0.86, and 0.82, and areas under the precision-recall curves of 0.48, 0.32, and 0.51 for the models using respiration and SpO2, respiration-only, and SpO2-only, respectively. Correlation between expert-labelled and predicted AHI was 0.96, 0.78, and 0.93, respectively. CONCLUSIONS A wearable respiratory effort signal with or without SpO2 signal predicted AHI accurately, and best performance was achieved with using both signals.
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Armañac-Julián P, Hernando D, Lázaro J, de Haro C, Magrans R, Morales J, Moeyersons J, Sarlabous L, López-Aguilar J, Subirà C, Fernández R, Orini M, Laguna P, Varon C, Gil E, Bailón R, Blanch L. Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation. Sci Rep 2021; 11:16014. [PMID: 34362950 PMCID: PMC8346488 DOI: 10.1038/s41598-021-95282-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.
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Affiliation(s)
- Pablo Armañac-Julián
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - David Hernando
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | | | - John Morales
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jonathan Moeyersons
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Rafael Fernández
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomews Hospital, University College London, London, UK
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Varon
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Circuits and Systems (CAS) group, Delft University of Technology, Delft, The Netherlands
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
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Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bricout A, Fontecave-Jallon J, Pépin JL, Guméry PY. Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106209. [PMID: 34118579 DOI: 10.1016/j.cmpb.2021.106209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep Apnea Syndrome (SAS) is a multimorbid chronic disease with individual and societal deleterious consequences. Polysomnography (PSG) is the multi-parametric reference diagnostic tool that allows a manual quantification of the apnea-hypopnea index (AHI) to assess SAS severity. The burden of SAS is affecting nearly one billion people worldwide explaining that SAS remains largely under-diagnosed and undertreated. The development of an easy to use and automatic solution for early detection and screening of SAS is highly desirable. METHODS We proposed an Accelerometry-Derived Respiratory index (ADR) solution based on a dual accelerometry system for airflow estimation included in a machine learning process. It calculated the AHI thanks to a RUSBoosted Tree model and used physiological and explanatory specifically developed features. The performances of this method were evaluated against a configuration using gold-standard PSG signals on a database of 28 subjects. RESULTS The AHI estimation accuracy, specificity and sensitivity of the ADR index were 89%, 100% and 80% respectively. The added value of the specifically developed features was also demonstrated. CONCLUSION Overnight physiological monitoring with the proposed ADR solution using a machine learning approach provided a clinically relevant estimate of AHI for SAS screening. The physiological component of the solution has a real interest for improving performance and facilitating physician's adhesion to an automatic AHI estimation.
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Affiliation(s)
- Aurélien Bricout
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France.
| | - Julie Fontecave-Jallon
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France
| | - Jean-Louis Pépin
- HP2 Laboratory, INSERM U1042, Grenoble Alpes University, Grenoble, France
| | - Pierre-Yves Guméry
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France.
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End-to-End Sleep Apnea Detection Using Single-Lead ECG Signal and 1-D Residual Neural Networks. J Med Biol Eng 2021. [DOI: 10.1007/s40846-021-00646-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146622] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.
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