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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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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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Li P, Ma W, Yue H, Lei W, Fan X, Li Y. Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network. Physiol Meas 2024; 45:025002. [PMID: 38237197 DOI: 10.1088/1361-6579/ad205a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Objective.Explore a network architecture that can efficiently perform single-lead electrocardiogram (ECG) sleep apnea (SA) detection by utilizing the beneficial information of extended ECG segments and reducing the impact of their noisy information.Approach.We propose an effective deep-shallow fusion network (EDSFnet). The deeper residual network is used to extract high-level features with stronger semantics and less noise from the original ECG segments. The shallower convolutional neural network is used to extract lower-level features with higher resolution containing more detailed neighborhood information from the extended ECG segments. These two types of features are then fused using Effective Channel Attention, implementing automatic weight assignment to take advantage of their complementary nature.Main results.The performance of EDSFnet is evaluated on the Apnea-ECG dataset and the FAH-ECG dataset. In the Apnea-ECG dataset with 35 subjects as the training set and 35 subjects as the test set, the accuracy of EDSFnet was 92.6% and 100% for per-segment and per-recording test, respectively. In the FAH-ECG dataset with 348 subjects as the training set and 88 subjects as the test set, the accuracy of EDSFnet was 89.0% and 93.2% for per-segment and per-recording test, respectively. EDSFnet has achieved state-of-the-art results in both experiments using the publicly available Apnea-ECG dataset and subject-independent experiments using the FAH-ECG clinical dataset.Significance.The success of EDSFnet in handling SA detection underlines its robustness and adaptability. By achieving superior results across different datasets, EDSFnet offers promise in advancing the cost-effective and efficient detection of SA through single-lead ECG, reducing the burden on patients and healthcare systems alike.
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Affiliation(s)
- Pan Li
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
- Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Guangzhou, People's Republic of China
| | - Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- Colledge of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Ye Li
- Institue of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen, Shenzhen, People's Republic of China
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Arslan RS. Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. PeerJ Comput Sci 2023; 9:e1554. [PMID: 37810361 PMCID: PMC10557519 DOI: 10.7717/peerj-cs.1554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/04/2023] [Indexed: 10/10/2023]
Abstract
Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.
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Romero D, Jané R. Dynamic Bayesian Model for Detecting Obstructive Respiratory Events by Using an Experimental Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:3371. [PMID: 37050431 PMCID: PMC10097311 DOI: 10.3390/s23073371] [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: 02/06/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
In this study, we propose a model-based tool for the detection of obstructive apnea episodes by using ECG features from a single lead channel. Several sequences of recurrent apnea were provoked in separate 15-min periods in anesthetized rats during an experimental model of obstructive sleep apnea (OSA). Morphology-based ECG markers and the beat-to-beat interval (RR) were assessed in each sequence. These markers were used to train dynamic Bayesian networks (DBN) with different orders and feature combinations to find a good tradeoff between network complexity and apnea-detection performance. By using a filtering approach, the resulting DBNs were used to infer the apnea probability signal for subsequent episodes in the same rat. These signals were then processed using by 15-s epochs to determine whether epochs were classified as apneic or nonapneic. Our results showed that fifth-order models provided suitable RMSE values, since higher order models become significantly more complex and present worse generalization. A global threshold of 0.2 gave the best overall performance for all combinations tested, with Acc = 81.3%, Se = 69.8% and Sp = 81.5%, using only two parameters including the RR and Ds (R-wave downslope) markers. We concluded that multivariate models using DBNs represent a powerful tool for detecting obstructive apnea episodes in short segments, which may also serve to estimate the number of total events in a given time period.
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Affiliation(s)
- Daniel Romero
- ESAII Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC-BIST), 08028 Barcelona, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
| | - Raimon Jané
- ESAII Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC-BIST), 08028 Barcelona, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
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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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Indrawati AN, Nuryani N, Nugroho AS, Utomo TP. Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms. J Biomed Phys Eng 2022; 12:627-636. [PMID: 36569571 PMCID: PMC9759644 DOI: 10.31661/jbpe.v0i0.2010-1216] [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: 10/29/2020] [Accepted: 02/03/2021] [Indexed: 06/17/2023]
Abstract
BACKGROUND Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient's body is fitted with many sensors. OBJECTIVE This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms. MATERIAL AND METHODS In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). RESULTS The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. CONCLUSION ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.
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Affiliation(s)
- Aida Noor Indrawati
- MSc, Medical Instrumentation, Physics, University Sebelas Maret, Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
| | - Nuryani Nuryani
- PhD, Medical Instrumentation, Physics, University of Sebelas Maret. Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
| | - Anto Satriyo Nugroho
- PhD, Center for Information and Communication Technology Agency for Assessment and Application of Technology, Indonesia
| | - Trio Pambudi Utomo
- MSc, Medical Instrumentation, Physics, University Sebelas Maret, Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia
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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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Shen N, Luo T, Chen C, Zhang Y, Zhu H, Zhou Y, Wang Y, Chen W. Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model. J Neural Eng 2022; 19. [PMID: 36001951 DOI: 10.1088/1741-2552/ac8c6b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/24/2022] [Indexed: 11/11/2022]
Abstract
Objective.Mixing/dissociation of sleep stages in narcolepsy adds to the difficulty in automatic sleep staging. Moreover, automatic analytical studies for narcolepsy and multiple sleep latency test (MSLT) have only done automatic sleep staging without leveraging the sleep stage profile for further patient identification. This study aims to establish an automatic narcolepsy detection method for MSLT.Approach.We construct a two-phase model on MSLT recordings, where ambiguous sleep staging and sleep transition dynamics make joint efforts to address this issue. In phase 1, we extract representative features from electroencephalogram (EEG) and electrooculogram (EOG) signals. Then, the features are input to an EasyEnsemble classifier for automatic sleep staging. In phase 2, we investigate sleep transition dynamics, including sleep stage transitions and sleep stages, and output likelihood of narcolepsy by virtue of principal component analysis (PCA) and a logistic regression classifier. To demonstrate the proposed framework in clinical application, we conduct experiments on 24 participants from our hospital, considering ten patients with narcolepsy and fourteen patients with MSLT negative.Main results.Applying the two-phase leave-one-subject-out testing scheme, the model reaches an accuracy, sensitivity, and specificity of 87.5%, 80.0%, and 92.9% for narcolepsy detection. Influenced by disease pathology, accuracy of automatic sleep staging in narcolepsy appears to decrease compared to that in the non-narcoleptic population.Significance.This method can automatically and efficiently distinguish patients with narcolepsy based on MSLT. It probes into the amalgamation of automatic sleep staging and sleep transition dynamics for narcolepsy detection, which would assist clinic and neuroelectrophysiology specialists in visual interpretation and diagnosis.
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Affiliation(s)
- Ning Shen
- Fudan University School of Information Science and Engineering, 220 Handan Road, Yangpu District, Shanghai, China, 2005 Songhu Road, Yangpu District, Shanghai, China, Shanghai, 200433, CHINA
| | - Tian Luo
- Department of Neurology, Children's Hospital of Fudan University, 399 Wanyuan Road, Minhang District, Shanghai, China, Shanghai, 201102, CHINA
| | - Chen Chen
- Fudan University Human Phenome Institute, 825 Zhangheng Road, Pudong District, Shanghai, China, Shanghai, 201203, CHINA
| | - Yanjiong Zhang
- Department of Neurology, Children's Hospital of Fudan University, 399 Wanyuan Road, Minhang District, Shanghai, China, Shanghai, 201102, CHINA
| | - Hangyu Zhu
- Fudan University School of Information Science and Engineering, 220 Handan Road, Yangpu District, Shanghai, China, 2005 Songhu Road, Yangpu District, Shanghai, China, Shanghai, 200433, CHINA
| | - Yuanfeng Zhou
- Department of Neurology, Children's Hospital of Fudan University, 399 Wanyuan Road, Minhang District, Shanghai, China, Shanghai, 201102, CHINA
| | - Yi Wang
- Department of Neurology, Children's Hospital of Fudan University, 399 Wanyuan Road, Minhang District, Shanghai, China, Shanghai, 201102, CHINA
| | - Wei Chen
- Department of Electronic Engineering, Fudan University, 220 Handan Road, Yangpu District, Shanghai, China, 2005 Songhu Road, Yangpu District, Shanghai, China, Shanghai, Shanghai, 200433, CHINA
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Arslan RS, Ulutaş H, Köksal AS, Bakır M, Çiftçi B. Automated sleep scoring system using multi-channel data and machine learning. Comput Biol Med 2022; 146:105653. [DOI: 10.1016/j.compbiomed.2022.105653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/06/2022] [Accepted: 05/18/2022] [Indexed: 11/03/2022]
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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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13
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Iwasaki A, Fujiwara K, Nakayama C, Sumi Y, Kano M, Nagamoto T, Kadotani H. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. Clin Neurophysiol 2022; 139:80-89. [DOI: 10.1016/j.clinph.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/10/2022] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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14
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Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03264-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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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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Karimi Moridani M. An automated method for sleep apnoea detection using HRV. J Med Eng Technol 2022; 46:158-173. [DOI: 10.1080/03091902.2022.2026504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Mohammad Karimi Moridani
- Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Yeh CY, Chang HY, Hu JY, Lin CC. Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:510. [PMID: 35062470 PMCID: PMC8777653 DOI: 10.3390/s22020510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/31/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether the reduction of lower frequency P and T waves can increase the accuracy of the detection of apnea events. This study proposed filter bank decomposition to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal. One-minute ECG signals obtained from the MIT PhysioNet Apnea-ECG database were used to train the CNN models and test the accuracy of detecting apnea events for different subbands. The results show that the use of the newly selected subject-independent datasets can avoid the overestimation of the accuracy of the apnea event detection and can test the difference in the accuracy of different subbands. The frequency band of 31.25-37.5 Hz can achieve 100% per-recording accuracy with 85.8% per-minute accuracy using the newly selected subject-independent datasets and is recommended as a promising subband of ECG signals that can cooperate with the proposed 1D CNN model for the diagnosis of OSA.
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Affiliation(s)
- Cheng-Yu Yeh
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
| | - Hung-Yu Chang
- Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jiy-Yao Hu
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
| | - Chun-Cheng Lin
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
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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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19
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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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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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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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Bayrak T, Ogul H. Computer-aided diagnosis of sleep apnea using gene expression. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00557-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Zhang J, Tang Z, Gao J, Lin L, Liu Z, Wu H, Liu F, Yao R. Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5594733. [PMID: 33859679 PMCID: PMC8009718 DOI: 10.1155/2021/5594733] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/05/2021] [Accepted: 03/13/2021] [Indexed: 01/16/2023]
Abstract
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.
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Affiliation(s)
- Junming Zhang
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
- Henan Key Laboratory of Smart Lighting, Zhumadian, Henan 463000, China
- Henan Joint International Research Laboratory of Behavior Optimization Control for Smart Robots, Zhumadian, Henan 463000, China
- Zhumadian Artificial Intelligence & Medical Engineering Technical Research Centre, Zhumadian, Henan 463000, China
- Academy of Industry Innovation and Development, Huanghuai University, Zhumadian, Henan 463000, China
| | - Zhen Tang
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
| | - Jinfeng Gao
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
- Henan Key Laboratory of Smart Lighting, Zhumadian, Henan 463000, China
| | - Li Lin
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
| | - Zhiliang Liu
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
| | - Haitao Wu
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
- Henan Key Laboratory of Smart Lighting, Zhumadian, Henan 463000, China
| | - Fang Liu
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
- Henan Joint International Research Laboratory of Behavior Optimization Control for Smart Robots, Zhumadian, Henan 463000, China
| | - Ruxian Yao
- College of Information Engineering, Huanghuai University, Zhumadian, Henan 463000, China
- Henan Key Laboratory of Smart Lighting, Zhumadian, Henan 463000, China
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Rajesh KNVPS, Dhuli R, Kumar TS. Obstructive sleep apnea detection using discrete wavelet transform-based statistical features. Comput Biol Med 2020; 130:104199. [PMID: 33422885 DOI: 10.1016/j.compbiomed.2020.104199] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/19/2020] [Accepted: 12/19/2020] [Indexed: 11/29/2022]
Abstract
MOTIVATION AND OBJECTIVE Obstructive sleep apnea (OSA) is a sleep disorder identified in nearly 10% of middle-aged people, which deteriorates the normal functioning of human organs, notably that of the heart. Furthermore, untreated OSA is associated with increased hypertension, diabetes, stroke, and cardiovascular diseases, thereby increasing the mortality risk. Therefore, early identification of sleep apnea is of significant interest. METHOD In this paper, an automated approach for OSA diagnosis using a single-lead electrocardiogram (ECG) has been reported. Three sets of features, namely moments of power spectrum density (PSD), waveform complexity measures, and higher-order moments, are extracted from the 1-min segmented ECG subbands obtained from discrete wavelet transform (DWT). Later, correlation-based feature selection with particle swarm optimization (PSO) search strategy is employed for getting an optimum feature vector. This process retained 18 significant features from initially computed 32 features. Finally, the acquired feature set is fed to different classifiers including, linear discriminant analysis, nearest neighbors, support vector machine, and random forest to perform per segment classification. RESULTS Experiments on the publicly available physionet single-lead ECG dataset show that the proposed approach using the random forest classifier effectively discriminates normal and OSA ECG signals. Specifically, our method achieved an accuracy of 89% and 90%, with 50-50 hold-out validation and 10-fold cross-validation, respectively. Besides, in both these validation scenarios, our method obtained 96% of the area under ROC. Importantly, our proposed approach provided better performance results than most of the existing methodologies.
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Affiliation(s)
- Kandala N V P S Rajesh
- Department of ECE, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, 530048, India.
| | - Ravindra Dhuli
- School of Electronics Engineering, VIT- AP University, Amaravathi, 522237, India.
| | - T Sunil Kumar
- Department of Engineering Cybernetics, NTNU, Norway.
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25
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Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2020.05.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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26
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Zarei A, Mohammadzadeh Asl B. Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105626. [PMID: 32634646 DOI: 10.1016/j.cmpb.2020.105626] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 06/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper addresses the automated recognition of obstructive sleep apnea (OSA) from the analysis of single-lead ECG signals. This is one of the most important problems that is, critical to the realization of monitoring patients with sleep apnea. METHODS In the present study, a novel solution based on autoregressive (AR) modeling of the single-lead ECG, and spectral autocorrelation function as an ECG feature extraction method is presented. The more effective features are opted by sequential forward feature selection (SFFS) technique and fed into the random forest for binary classification between the apnea and normal events. RESULTS Experimental results on Apnea-ECG database proved that the introduced algorithm resulted in an accuracy of 93.90% (sensitivity of 92.26% and specificity of 94.92%) in per-segment classification, which outperforms the other cutting-edge automatic OSA recognition techniques. Moreover, the proposed algorithm provided an accuracy of 97.14% (sensitivity of 95.65% and specificity of 100%) in discrimination of apnea patients from the normal subjects, which is comparable to the traditional and existing approaches. CONCLUSIONS This study suggests that automatic OSA recognition from single-lead ECG signals is possible, which can be used as an inexpensive and low complexity burden alternative to more conventional methods such as Polysomnography.
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Affiliation(s)
- Asghar Zarei
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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Fatimah B, Singh P, Singhal A, Pachori RB. Detection of apnea events from ECG segments using Fourier decomposition method. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102005] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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29
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A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram. SENSORS 2020; 20:s20154157. [PMID: 32722630 PMCID: PMC7435835 DOI: 10.3390/s20154157] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/17/2020] [Accepted: 07/24/2020] [Indexed: 11/30/2022]
Abstract
Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches.
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Uddin MB, Moi Chow C, Ling SH, Su SW. A Per-sample Digitized Algorithm for Automatically Detecting Apnea and Hypopnea Events from Airflow and Oximetry. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5339-5342. [PMID: 33019189 DOI: 10.1109/embc44109.2020.9176212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Sleep apnea is a common sleep disorder that can significantly decrease the quality of life. An accurate and early diagnosis of sleep apnea is required before getting proper treatment. A reliable automated detection of sleep apnea can overcome the problems of manual diagnosis (scoring) due to variability in recording and scoring criteria (for example across Europe) and to inter-scorer variability. This study explored a novel automated algorithm to detect apnea and hypopnea events from airflow and pulse oximetry signals, extracted from 30 polysomnography records of the Sleep Heart Health Study. Apneas and hypopneas were manually scored by a trained sleep physiologist according to the updated 2017 American Academy of Sleep Medicine respiratory scoring rules. From pre-processed airflow, the peak signal excursion was precisely determined from the peak-to-trough amplitude using a sliding window, with a per-sample digitized algorithm for detecting apnea and hypopnea. For apnea, the peak signal excursion drop was operationalized at ≥85% and for hypopnea at ≥35% of its pre-event baseline. Using backward shifting of oximetry, hypopneas were filtered with ≥3% oxygen desaturation from its baseline. The performance of the automated algorithm was evaluated by comparing the detection with manual scoring (a standard practice). The sensitivity and positive predictive value of detecting apneas and hypopneas were respectively 98.1% and 95.3%. This automated algorithm is applicable to any portable sleep monitoring device for the accurate detection of sleep apnea.
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Carrillo-Alarcón JC, Morales-Rosales LA, Rodríguez-Rángel H, Lobato-Báez M, Muñoz A, Algredo-Badillo I. A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20113139. [PMID: 32498271 PMCID: PMC7308921 DOI: 10.3390/s20113139] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/22/2020] [Accepted: 05/29/2020] [Indexed: 06/11/2023]
Abstract
The electrocardiogram records the heart's electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.
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Affiliation(s)
- Juan Carlos Carrillo-Alarcón
- Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Tonantzintla, Puebla 72840, Mexico;
| | - Luis Alberto Morales-Rosales
- Faculty of Civil Engineering, Conacyt-Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58030, Michoacán, Mexico;
| | | | | | - Antonio Muñoz
- Engineering Department, University of Guadalajara, Av. Independencia Nacional 151, Autlán, Jalisco 48900, Mexico;
| | - Ignacio Algredo-Badillo
- Department of Computer Science, Conacyt-Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Tonantzintla, Puebla 72840, Mexico
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Zarei A, Asl BM. Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101927] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Mahjoub C, Le Bouquin Jeannès R, Lajnef T, Kachouri A. Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods. ACTA ACUST UNITED AC 2020; 65:33-50. [PMID: 31469648 DOI: 10.1515/bmt-2019-0001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/07/2019] [Indexed: 11/15/2022]
Abstract
Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). The classification procedure is executed using a support vector machine (SVM). The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection.
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Affiliation(s)
- Chahira Mahjoub
- LETI-ENIS, University of Sfax, Street of Soukra, 3038 Sfax, Tunisia
| | - Régine Le Bouquin Jeannès
- Univ Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes, France.,Univ Rennes, INSERM, CRIBs, F-35000 Rennes, France
| | - Tarek Lajnef
- Psychology Department, University of Montreal, Montreal, QC, Canada
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Yang C, Zhang H. Singing voice separation with pre-learned dictionary and reconstructed voice spectrogram. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-3757-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Hassan AR, Subasi A, Zhang Y. Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105333] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Zhang J, Tian J, Cao Y, Yang Y, Xu X. Deep time–frequency representation and progressive decision fusion for ECG classification. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105402] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu J, Zhang C, Ristaniemi T, Cong F. Detection of Myocardial Infarction from Multi-lead ECG using Dual-Q Tunable Q-Factor Wavelet Transform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1496-1499. [PMID: 31946177 DOI: 10.1109/embc.2019.8857775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Electrocardiography (ECG) signal analysis is an effective method for diagnosis of heart disease. However, the quality of ECG, corrupted by artifacts, limits the automatic ECG classification. In order to extract good quality ECG, we proposed a new ECG enhancement method based on tunable Q-factor wavelet transform (TQWT). In the proposed method, the original ECG signal was decomposed into high Q-factor component and low Q-factor component with dual-Q TQWT. According to the morphological of P, QRS, T waves in ECG, low Q-factor component was chosen for the representation of ECG. The proposed method was tested on 52 healthy volunteers and 52 myocardial infarction patients from the openly dataset of PTB diagnostic ECG. A total of 288 features, covering time, frequency, nonlinear, and entropy domains, were extracted from R-R interval and ECG (in a window of 5s) across 12 leads. The features were selected by Relief method, and 22 discriminative features were fed into five different classifiers. The classification accuracy for dual-Q TQWT was 86.3%, which was 4.7% higher than the filtered data based on k-nearest neighbors (KNN) algorithm. The comparison results verified that the proposed dual-Q TQWT method provides good feasibility for ECG de-noising.
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Ostadieh J, Amirani MC. Introducing the Hybrid "K-means, RLS" Learning for the RBF Network in Obstructive Apnea Disease Detection using Dual-tree Complex Wavelet Transform Based Features. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2020; 11:4-11. [PMID: 33584897 PMCID: PMC7531097 DOI: 10.2478/joeb-2020-0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Indexed: 06/12/2023]
Abstract
Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.
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Affiliation(s)
- Javad Ostadieh
- Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran
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Fraiwan L, Alkhodari M. Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100370] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Jadhav P, Rajguru G, Datta D, Mukhopadhyay S. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang Y, Qiao S, Lu R, Han N, Liu D, Zhou J. How to balance the bioinformatics data: pseudo-negative sampling. BMC Bioinformatics 2019; 20:695. [PMID: 31874622 PMCID: PMC6929457 DOI: 10.1186/s12859-019-3269-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance of the minority class of positive samples. Therefore, how to balance the bioinformatic data becomes a very challenging and difficult problem. RESULTS In this study, we propose a new data sampling approach, called pseudo-negative sampling, which can be effectively applied to handle the case that: negative samples greatly dominate positive samples. Specifically, we design a supervised learning method based on a max-relevance min-redundancy criterion beyond Pearson correlation coefficient (MMPCC), which is used to choose pseudo-negative samples from the negative samples and view them as positive samples. In addition, MMPCC uses an incremental searching technique to select optimal pseudo-negative samples to reduce the computation cost. Consequently, the discovered pseudo-negative samples have strong relevance to positive samples and less redundancy to negative ones. CONCLUSIONS To validate the performance of our method, we conduct experiments base on four UCI datasets and three real bioinformatics datasets. According to the experimental results, we clearly observe the performance of MMPCC is better than other sampling methods in terms of Sensitivity, Specificity, Accuracy and the Mathew's Correlation Coefficient. This reveals that the pseudo-negative samples are particularly helpful to solve the imbalance dataset problem. Moreover, the gain of Sensitivity from the minority samples with pseudo-negative samples grows with the improvement of prediction accuracy on all dataset.
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Affiliation(s)
- Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Shaojie Qiao
- School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China.
- Software Automatic Generation and Intelligent Service Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, 610225, China.
| | - Rongzhao Lu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Nan Han
- School of Management, Chengdu University of Information Technology, Chengdu, 610103, China
| | - Dingxiang Liu
- School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Jiliu Zhou
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
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Erdenebayar U, Kim YJ, Park JU, Joo EY, Lee KJ. Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 180:105001. [PMID: 31421606 DOI: 10.1016/j.cmpb.2019.105001] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 07/20/2019] [Accepted: 07/29/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal. METHODS Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated-recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the signal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients. RESULTS The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates. CONCLUSIONS The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies.
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Affiliation(s)
- Urtnasan Erdenebayar
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea
| | - Yoon Ji Kim
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea
| | - Jong-Uk Park
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea
| | - Eun Yeon Joo
- Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Korea
| | - Kyoung-Joung Lee
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju 26493, Korea.
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In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:959-979. [PMID: 31515685 DOI: 10.1007/s13246-019-00796-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 08/30/2019] [Indexed: 01/09/2023]
Abstract
Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.
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Zhao D, Wang Y, Wang Q, Wang X. Comparative analysis of different characteristics of automatic sleep stages. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:53-72. [PMID: 31104715 DOI: 10.1016/j.cmpb.2019.04.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 04/03/2019] [Accepted: 04/05/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status. METHODS This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA. RESULTS By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging. CONCLUSION In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.
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Affiliation(s)
- Dechun Zhao
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yi Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Qiangqiang Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Xing Wang
- College of Biomedical Engineering, Chongqing University, Chongqing 400044, China
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A novel hybrid network of fusing rhythmic and morphological features for atrial fibrillation detection on mobile ECG signals. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04318-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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46
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Mendonca F, Mostafa SS, Ravelo-Garcia AG, Morgado-Dias F, Penzel T. A Review of Obstructive Sleep Apnea Detection Approaches. IEEE J Biomed Health Inform 2019; 23:825-837. [DOI: 10.1109/jbhi.2018.2823265] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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47
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Identifying the mislabeled training samples of ECG signals using machine learning. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.026] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Rakshit M, Das S. Electrocardiogram beat type dictionary based compressed sensing for telecardiology application. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Yin W, Yang X, Li L, Zhang L, Kitsuwan N, Shinkuma R, Oki E. Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Mazandarani FN, Mohebbi M. Wide complex tachycardia discrimination using dynamic time warping of ECG beats. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:238-249. [PMID: 29703454 DOI: 10.1016/j.cmpb.2018.04.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/09/2018] [Accepted: 04/12/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Automatic processing and accurate diagnosis of wide complex tachycardia (WCT) arrhythmia groups using electrocardiogram signals (ECG) remains a challenge. WCT arrhythmia consists of two main groups: ventricular tachycardia (VT) and supraventricular tachycardia with aberrancy (SVT-A). These two groups have similar morphologies in the realm of ECG signals. VT and SVT-A arrhythmias originate from the ventricle and atrium, respectively. Hence, inaccurate diagnosis of SVT-A instead of VT can be fatal. METHODS In this paper, we present a novel algorithm using dynamic time warping (DTW) to discriminate between VT and SVT-A arrhythmias. This method includes pre-processing, best template search (BTS), and classifier modules. The first module, pre-processing, is responsible for filtering, R-wave detection, and beat detection of ECG signals. The second module, BTS, automatically extracts the minimum possible number of signals as a template from the entire training dataset using an intelligent algorithm. These template signals have the greatest morphological difference, which leads to accurate WCT discrimination. Finally, a 1NN classifier categorizes the test data using DTW distance. RESULTS Our proposed method was evaluated on an ECG signal database consisting of 171 subjects. The results showed that the proposed algorithm can accurately discriminate between VT, SVT-A, and normal subjects, and appears to be suitable for future use in clinical application. The obtained accuracy, sensitivity, specificity, and positive predictive values were 93.22%, 88.68%, 96.98%, and 90.27%, respectively. CONCLUSION The presented diagnostic method for discriminating VT and SVT-A, using only one ECG lead, is suitable for future clinical use. It can reduce needless therapeutic interventions and minimize risk for patients.
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Affiliation(s)
- F Niknejad Mazandarani
- Biomedical Engineering Department, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - M Mohebbi
- Biomedical Engineering Department, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
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