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Jha CK. Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38566498 DOI: 10.1080/10255842.2024.2332942] [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: 04/20/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024]
Abstract
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac anomalies seeks early detection of arrhythmias using automated classification techniques. In the past, numerous automated arrhythmia detection techniques have been developed that are based on electrocardiogram (ECG) signal analysis. Focusing on the prospective research in this field, this article reports a comprehensive review of existing techniques that are obtained using search engines such as IEEE explore, Google scholar and science direct. Based on the review, the existing techniques are broadly categorized into two types: machine-learning and deep-learning-based techniques. In this study, it is noticed that the performance of the machine-learning-based arrhythmia detection techniques depend on pre-processing of ECG signal, R-peaks detection, features extraction and classification tools while the deep-learning-based techniques do not require the features extraction step. Generally, the existing techniques utilize Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database to evaluate the classification performance. The classification performance of automated techniques also depends on ECG data used for training and testing of the classifier. It is expected that the performance should be evaluated using a variety of ECG signals including the cases of inter-patient and intra-patient paradigm. The existing techniques also require to deal with the class-imbalance problem. In addition to this, a specific partition-ratio between training and testing datasets should be maintained for fair comparison of performance of different techniques.
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Affiliation(s)
- Chandan Kumar Jha
- Department of Electronics & Communication Engineering, Indian Institute of Information Technology Bhagalpur, India
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2
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Cuevas-Chávez A, Hernández Y, Ortiz-Hernandez J, Sánchez-Jiménez E, Ochoa-Ruiz G, Pérez J, González-Serna G. A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases. Healthcare (Basel) 2023; 11:2240. [PMID: 37628438 PMCID: PMC10454027 DOI: 10.3390/healthcare11162240] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
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Affiliation(s)
- Alejandra Cuevas-Chávez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Yasmín Hernández
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Javier Ortiz-Hernandez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Eduardo Sánchez-Jiménez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gilberto Ochoa-Ruiz
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico;
| | - Joaquín Pérez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gabriel González-Serna
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
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Nasimi F, Yazdchi M. LDIAED: A lightweight deep learning algorithm implementable on automated external defibrillators. PLoS One 2022; 17:e0264405. [PMID: 35213628 PMCID: PMC8880955 DOI: 10.1371/journal.pone.0264405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 02/06/2022] [Indexed: 11/28/2022] Open
Abstract
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s.
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Affiliation(s)
- Fahimeh Nasimi
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohammadreza Yazdchi
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
- * E-mail:
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Hammad M, Kandala RN, Abdelatey A, Abdar M, Zomorodi‐Moghadam M, Tan RS, Acharya UR, Pławiak J, Tadeusiewicz R, Makarenkov V, Sarrafzadegan N, Khosravi A, Nahavandi S, EL-Latif AAA, Pławiak P. Automated detection of shockable ECG signals: A review. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.05.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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5
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Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals. ELECTRONICS 2021. [DOI: 10.3390/electronics10131531] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.
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Sharma M, Rajput JS, Tan RS, Acharya UR. Automated Detection of Hypertension Using Physiological Signals: A Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5838. [PMID: 34072304 PMCID: PMC8198170 DOI: 10.3390/ijerph18115838] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/10/2021] [Accepted: 05/24/2021] [Indexed: 01/09/2023]
Abstract
Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India;
| | - Jaypal Singh Rajput
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India;
| | - Ru San Tan
- National Heart Centre, Singapore 639798, Singapore;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, SUSS, Singapore 599494, Singapore
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Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02422-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Sharma M, Tiwari J, Acharya UR. Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:3087. [PMID: 33802799 PMCID: PMC8002569 DOI: 10.3390/ijerph18063087] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 01/20/2023]
Abstract
Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet's cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen's Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen's Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India;
| | - Jainendra Tiwari
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia
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Sharma M, Acharya UR. Automated detection of schizophrenia using optimal wavelet-based l 1 norm features extracted from single-channel EEG. Cogn Neurodyn 2021; 15:661-674. [PMID: 34367367 DOI: 10.1007/s11571-020-09655-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/12/2020] [Accepted: 11/27/2020] [Indexed: 10/22/2022] Open
Abstract
Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l 1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan, ROC
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Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network. Phys Eng Sci Med 2021; 44:135-145. [PMID: 33417159 DOI: 10.1007/s13246-020-00964-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 12/15/2020] [Indexed: 12/24/2022]
Abstract
Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.
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Yildirim O, Talo M, Ciaccio EJ, Tan RS, Acharya UR. Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105740. [PMID: 32932129 PMCID: PMC7477611 DOI: 10.1016/j.cmpb.2020.105740] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 08/31/2020] [Indexed: 05/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database. METHODS Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers. RESULTS We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. CONCLUSION Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.
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Affiliation(s)
- Ozal Yildirim
- Department of Computer Engineering, Munzur University, Tunceli,62000, Turkey
| | - Muhammed Talo
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Edward J Ciaccio
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Ru San Tan
- National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; School of Management and Enterprise University of Southern Queensland, Springfield, Australia.
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Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network. Comput Biol Med 2020; 124:103939. [DOI: 10.1016/j.compbiomed.2020.103939] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 07/26/2020] [Accepted: 07/26/2020] [Indexed: 02/07/2023]
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Rajput JS, Sharma M, Tan RS, Acharya UR. Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank. Comput Biol Med 2020; 123:103924. [DOI: 10.1016/j.compbiomed.2020.103924] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/18/2020] [Accepted: 07/18/2020] [Indexed: 12/18/2022]
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Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA. Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. MULTIMEDIA TOOLS AND APPLICATIONS 2020. [DOI: 10.1007/s11042-020-08769-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/29/2019] [Accepted: 02/17/2020] [Indexed: 09/02/2023]
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16
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Soh DCK, Ng EYK, Jahmunah V, Oh SL, San TR, Acharya UR. A computational intelligence tool for the detection of hypertension using empirical mode decomposition. Comput Biol Med 2020; 118:103630. [PMID: 32174317 DOI: 10.1016/j.compbiomed.2020.103630] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 01/23/2020] [Accepted: 01/24/2020] [Indexed: 12/28/2022]
Abstract
Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.
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Affiliation(s)
| | - E Y K Ng
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | - V Jahmunah
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | | | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O, Acharya UR. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 2020; 103:101789. [PMID: 32143796 DOI: 10.1016/j.artmed.2019.101789] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/06/2019] [Accepted: 12/31/2019] [Indexed: 11/15/2022]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.
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Affiliation(s)
- Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | | | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Masayuki Tanabe
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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Rajput JS, Sharma M, Acharya UR. Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E4068. [PMID: 31652712 PMCID: PMC6861956 DOI: 10.3390/ijerph16214068] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 10/13/2019] [Accepted: 10/14/2019] [Indexed: 12/15/2022]
Abstract
Hypertension (HT) is an extreme increment in blood pressure that can prompt a stroke, kidney disease, and heart attack. HT does not show any symptoms at the early stage, but can lead to various cardiovascular diseases. Hence, it is essential to identify it at the beginning stages. It is tedious to analyze electrocardiogram (ECG) signals visually due to their low amplitude and small bandwidth. Hence, to avoid possible human errors in the diagnosis of HT patients, an automated ECG-based system is developed. This paper proposes the computerized segregation of low-risk hypertension (LRHT) and high-risk hypertension (HRHT) using ECG signals with an optimal orthogonal wavelet filter bank (OWFB) system. The HRHT class is comprised of patients with myocardial infarction, stroke, and syncope ECG signals. The ECG-data are acquired from physionet's smart health for accessing risk via ECG event (SHAREE) database, which contains recordings of a total 139 subjects. First, ECG signals are segmented into epochs of 5 min. The segmented epochs are then decomposed into six wavelet sub-bands (WSBs) using OWFB. We extract the signal fractional dimension (SFD) and log-energy (LOGE) features from all six WSBs. Using Student's t-test ranking, we choose the high ranked WSBs of LOGE and SFD features. We develop a novel hypertension diagnosis index (HDI) using two features (SFD and LOGE) to discriminate LRHT and HRHT classes using a single numeric value. The performance of our developed system is found to be encouraging, and we believe that it can be employed in intensive care units to monitor the abrupt rise in blood pressure while screening the ECG signals, provided this is tested with an extensive independent database.
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Affiliation(s)
- Jaypal Singh Rajput
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India.
| | - Manish Sharma
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489 Singapore, Singapore.
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, 599494 Singapore, Singapore.
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan.
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