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Baygin M, Tuncer T, Dogan S, Tan RS, Acharya UR. Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Hammad M, Pławiak P, Wang K, Acharya UR. ResNet‐Attention model for human authentication using ECG signals. EXPERT SYSTEMS 2021; 38. [DOI: 10.1111/exsy.12547] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 02/17/2020] [Indexed: 07/23/2024]
Abstract
AbstractAuthentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end‐to‐end deep neural network models for ECG‐based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet‐Attention is designed for human authentication. We have used 2‐s duration ECG signals obtained from two ECG databases (Physikalisch‐Technische Bundesanstalt [PTB] and Check Your Bio‐signals Here initiative [CYBHi]) for authentication. Our proposed ResNet‐Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real‐time authentication systems after the validation with more diverse ECG data.
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Affiliation(s)
- Mohamed Hammad
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
- Faculty of Computers and Information Menoufia University Menoufia Egypt
| | - Paweł Pławiak
- Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland
- Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland
| | - Kuanquan Wang
- School of Computer Science and Technology Harbin Institute of Technology Harbin China
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore
- Department of Biomedical Engineering, School of Science and Technology Singapore School of Social Sciences Singapore Singapore
- Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan
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53
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Baygin M, Yaman O, Tuncer T, Dogan S, Barua PD, Acharya UR. Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102936] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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54
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Aydemir E, Tuncer T, Dogan S, Gururajan R, Acharya UR. Automated major depressive disorder detection using melamine pattern with EEG signals. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02426-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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55
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Assadi I, Charef A, Bensouici T. PVC arrhythmia classification based on fractional order system modeling. BIOMED ENG-BIOMED TE 2021; 66:363-373. [PMID: 33606930 DOI: 10.1515/bmt-2020-0170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/01/2021] [Indexed: 11/15/2022]
Abstract
It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.
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Affiliation(s)
- Imen Assadi
- Laboratoire de Traitement du Signal, Département d'Electronique, Université des Frères Mentouri, Constantine, Algeria.,Université Saad Dahlab Blida 1, Blida, Algeria
| | - Abdelfatah Charef
- Laboratoire de Traitement du Signal, Département d'Electronique, Université des Frères Mentouri, Constantine, Algeria
| | - Tahar Bensouici
- Département de Télécommunication, USTHB, Bab-Ezzouar, Algeria
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56
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Barua PD, Muhammad Gowdh NF, Rahmat K, Ramli N, Ng WL, Chan WY, Kuluozturk M, Dogan S, Baygin M, Yaman O, Tuncer T, Wen T, Cheong KH, Acharya UR. Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8052. [PMID: 34360343 PMCID: PMC8345793 DOI: 10.3390/ijerph18158052] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/18/2022]
Abstract
COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
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Affiliation(s)
- Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba 2550, Australia;
| | - Nadia Fareeda Muhammad Gowdh
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Norlisah Ramli
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Wei Lin Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Mutlu Kuluozturk
- Department of Pulmonology Clinic, Firat University Hospital, Firat University, Elazig 23119, Turkey;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Orhan Yaman
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Tao Wen
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore S599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore S599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Analysis and classification of heart rate using CatBoost feature ranking model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102610] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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58
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Tuncer T, Dogan S, Subasi A. EEG-based driving fatigue detection using multilevel feature extraction and iterative hybrid feature selection. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102591] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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59
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Baygin M. An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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60
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Tuncer T, Dogan S, Tan RS, Acharya UR. Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.01.088] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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61
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Dogan S, Akbal E, Tuncer T, Acharya UR. Application of substitution box of present cipher for automated detection of snoring sounds. Artif Intell Med 2021; 117:102085. [PMID: 34127246 DOI: 10.1016/j.artmed.2021.102085] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND AND PURPOSE Snoring is one of the sleep disorders, and snoring sounds have been used to diagnose many sleep-related diseases. However, the snoring sound classification is done manually which is time-consuming and prone to human errors. An automated snoring sound classification model is proposed to overcome these problems. MATERIAL AND METHOD This work proposes an automated snoring sound classification method using three new methods. These methods are maximum absolute pooling (MAP), the nonlinear present pattern, and two-layered neighborhood component analysis, and iterative neighborhood component analysis (NCAINCA) selector. Using these methods, a new snoring sound classification (SSC) model is presented. The MAP decomposition model is applied to snoring sounds to extract both low and high-level features. The presented model aims to attain high performance for SSC problem. The developed present pattern (Present-Pat) uses substitution box (SBox) and statistical feature generator. By deploying these feature generators, both textural and statistical features are generated. NCAINCA chooses the most informative/valuable features, and these selected features are fed to k-nearest neighbor (kNN) classifier with leave-one-out cross-validation (LOOCV). The Present-Pat based SSC system is developed using Munich-Passau Snore Sound Corpus (MPSSC) dataset comprising of four categories. RESULTS Our model reached an accuracy and unweighted average recall (UAR) of 97.10 % and 97.60 %, respectively, using LOOCV. Moreover, a nocturnal sound dataset is used to show the universal success of the presented model. Our model attained an accuracy of 98.14 % using the used nocturnal sound dataset. CONCLUSIONS Our developed classification model is ready to be tested with more data and can be used by sleep specialists to diagnose the sleep disorders based on snoring sounds.
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Affiliation(s)
- Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Erhan Akbal
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Hiriyannaiah S, G M S, M H M K, Srinivasa KG. A comparative study and analysis of LSTM deep neural networks for heartbeats classification. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00552-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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63
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Tuncer T, Dogan S, Ertam F, Subasi A. A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cogn Neurodyn 2021; 15:223-237. [PMID: 33854641 PMCID: PMC7969686 DOI: 10.1007/s11571-020-09601-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/10/2020] [Accepted: 05/14/2020] [Indexed: 12/24/2022] Open
Abstract
Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Abdulhamit Subasi
- College of Engineering, Department of Computer Science, Effat University, Jeddah, 21478 Saudi Arabia
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64
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Tuncer T, Ozyurt F, Dogan S, Subasi A. A novel Covid-19 and pneumonia classification method based on F-transform. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2021; 210:104256. [PMID: 33531722 PMCID: PMC7844388 DOI: 10.1016/j.chemolab.2021.104256] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/11/2021] [Accepted: 01/23/2021] [Indexed: 05/28/2023]
Abstract
Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Firat University, Elazig, 23000, Turkey
| | - Fatih Ozyurt
- Department of Software Engineering, Firat University, Elazig, 23000, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Firat University, Elazig, 23000, Turkey
| | - Abdulhamit Subasi
- Institute of Biomedicine, Faculty of Medicine, University of Turku, 20520, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, 21478, Saudi Arabia
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65
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Hammad M, Iliyasu AM, Subasi A, Ho ESL, El-Latif AAA. A Multitier Deep Learning Model for Arrhythmia Detection. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021; 70:1-9. [PMID: 0 DOI: 10.1109/tim.2020.3033072] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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66
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Gao Y, Wang H, Liu Z. An end-to-end atrial fibrillation detection by a novel residual-based temporal attention convolutional neural network with exponential nonlinearity loss. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106589] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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67
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Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106547] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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68
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Tuncer T, Akbal E, Dogan S. An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector. Biomed Signal Process Control 2021; 63:102173. [PMID: 32922509 PMCID: PMC7476581 DOI: 10.1016/j.bspc.2020.102173] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/18/2020] [Accepted: 08/22/2020] [Indexed: 02/08/2023]
Abstract
In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method. Seven leveled DWT transform, and LDOP are used together to generate low, medium, and high levels features. This feature generation network extracts 4096 features in total. RFINCA selects 95 the most discriminative and informative ones of these 4096 features. In the classification phase, kNN with leave one out cross-validation (LOOCV) is used. 95.53% classification accuracy and 94.65% unweighted average recall (UAR) have been achieved using this method. The proposed LDOP based SSC method reaches 22% better result than the best of the other state-of-the-art machine learning and deep learning-based methods. These results clearly denote the success of the proposed SSC method.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Erhan Akbal
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
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69
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Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102138] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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70
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A novel statistical decimal pattern-based surface electromyogram signal classification method using tunable q-factor wavelet transform. Soft comput 2021. [DOI: 10.1007/s00500-020-05205-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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71
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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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Hernandez-Matamoros A, Fujita H, Perez-Meana H. A novel approach to create synthetic biomedical signals using BiRNN. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.06.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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73
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Tobore I, Kandwal A, Li J, Yan Y, Omisore OM, Enitan E, Sinan L, Yuhang L, Wang L, Nie Z. Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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74
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Noh YH, Seo JY, Jeong DU. Development of a Knowledge Discovery Computing based wearable ECG monitoring system. INFORMATION TECHNOLOGY & MANAGEMENT 2020. [DOI: 10.1007/s10799-020-00318-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Benhar H, Idri A, Fernández-Alemán JL. Data preprocessing for heart disease classification: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105635. [PMID: 32652383 DOI: 10.1016/j.cmpb.2020.105635] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 06/24/2020] [Indexed: 06/11/2023]
Abstract
CONTEXT Early detection of heart disease is an important challenge since 17.3 million people yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac disease can be dangerous and risks an individual's life. Accurate diagnosis is therefore critical in cardiology. Data Mining (DM) classification techniques have been used to diagnosis heart diseases but still limited by some challenges of data quality such as inconsistencies, noise, missing data, outliers, high dimensionality and imbalanced data. Data preprocessing (DP) techniques were therefore used to prepare data with the goal of improving the performance of heart disease DM based prediction systems. OBJECTIVE The purpose of this study is to review and summarize the current evidence on the use of preprocessing techniques in heart disease classification as regards: (1) the DP tasks and techniques most frequently used, (2) the impact of DP tasks and techniques on the performance of classification in cardiology, (3) the overall performance of classifiers when using DP techniques, and (4) comparisons of different combinations classifier-preprocessing in terms of accuracy rate. METHOD A systematic literature review is carried out, by identifying and analyzing empirical studies on the application of data preprocessing in heart disease classification published in the period between January 2000 and June 2019. A total of 49 studies were therefore selected and analyzed according to the aforementioned criteria. RESULTS The review results show that data reduction is the most used preprocessing task in cardiology, followed by data cleaning. In general, preprocessing either maintained or improved the performance of heart disease classifiers. Some combinations such as (ANN + PCA), (ANN + CHI) and (SVM + PCA) are promising terms of accuracy. However the deployment of these models in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of interpretation.
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Affiliation(s)
- H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco.
| | - A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V in Rabat, Morocco; CSEHS-MSDA, Mohammed VI Polytechnic University, Benguerir, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
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Książek W, Hammad M, Pławiak P, Acharya UR, Tadeusiewicz R. Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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77
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Automatic classification of healthy and disease conditions from images or digital standard 12-lead electrocardiograms. Sci Rep 2020; 10:16331. [PMID: 33004907 PMCID: PMC7530668 DOI: 10.1038/s41598-020-73060-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 09/11/2020] [Indexed: 01/29/2023] Open
Abstract
Standard 12-lead electrocardiography (ECG) is used as the primary clinical tool to diagnose changes in heart function. The value of automated 12-lead ECG diagnostic approaches lies in their ability to screen the general population and to provide a second opinion for doctors. Yet, the clinical utility of automated ECG interpretations remains limited. We introduce a two-way approach to an automated cardiac disease identification system using standard digital or image 12-lead ECG recordings. Two different network architectures, one trained using digital signals (CNN-dig) and one trained using images (CNN-ima), were generated. An open-source dataset of 41,830 classified standard ECG recordings from patients and volunteers was generated. CNN-ima was trained to identify atrial fibrillation (AF) using 12-lead ECG digital signals and images that were also transformed to mimic mobile device camera-acquired ECG plot snapshots. CNN-dig accurately (92.9–100%) identified every possible combination of the eight most-common cardiac conditions. Both CNN-dig and CNN-ima accurately (98%) detected AF from standard 12-lead ECG digital signals and images, respectively. Similar classification accuracy was achieved with images containing smartphone camera acquisition artifacts. Automated detection of cardiac conditions in standard digital or image 12-lead ECG signals is feasible and may improve current diagnostic methods.
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78
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A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.09.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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79
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Chen A, Wang F, Liu W, Chang S, Wang H, He J, Huang Q. Multi-information fusion neural networks for arrhythmia automatic detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105479. [PMID: 32388066 DOI: 10.1016/j.cmpb.2020.105479] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/24/2020] [Accepted: 03/26/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES . The electrocardiograms (ECGs) are widely used to diagnose a variety of arrhythmias. Generally, the abnormalities of ECG signals mainly consist of ill-shaped ECG beat morphologies and irregular intervals. The ill-shaped ECG beat morphologies represent morphological information, while the irregular intervals denote the temporal information of ECG signals. But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats, because RR interval is not contained in a single short heartbeat. Therefore, to handle this problems, a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network (MF-CBRNN) is proposed for arrhythmia automatic detection. METHODS . The MF-CBRNN is designed with two parallel hybrid branches that can simultaneously focus on the beat-based information in the ECG beats and the segment-based information in the adjacent segments of the beats. A single ECG beat provides the morphological information. At the same time, the adjacent segment of the ECG beat enriches the temporal information, so the two branches are designed to exploit the multiple information contained in ECGs. Furthermore, a combination of convolutional neural networks (CNNs) and a bidirectional long short memory (BLSTM) in each branch is utilized to capture the information from the two inputs. And all the features extracted from the two branches are fused for information aggregation. RESULTS . To evaluate the performance of the proposed model, the ECG signals from MIT-BIH databases are used for intra-patient and inter-patient paradigms. The proposed model yields an accuracy of 99.56% and an F1-score of 96.40% under the intra-patient paradigm. And it obtains an overall accuracy of 96.77% and F1-score of 77.83% under the inter-patient paradigm. CONCLUSIONS . Compared with other studies on arrhythmia detection, our method achieves a state-of-the-art performance. It indicates that the proposed model is a promising arrhythmia detection algorithm for computer-aided diagnostic systems.
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Affiliation(s)
- Aiyun Chen
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Fei Wang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Wenhan Liu
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Sheng Chang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Hao Wang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Jin He
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China
| | - Qijun Huang
- School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
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80
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Tuncer T, Dogan S, Ozyurt F. An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2020; 203:104054. [PMID: 32427226 PMCID: PMC7233238 DOI: 10.1016/j.chemolab.2020.104054] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/01/2020] [Accepted: 05/12/2020] [Indexed: 05/03/2023]
Abstract
Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Fatih Ozyurt
- Department of Software Engineering, College of Engineering, Firat University, Elazig, Turkey
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81
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Tuncer T, Dogan S, Ertam F, Subasi A. A novel ensemble local graph structure based feature extraction network for EEG signal analysis. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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82
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Almustafa KM. Prediction of heart disease and classifiers' sensitivity analysis. BMC Bioinformatics 2020; 21:278. [PMID: 32615980 PMCID: PMC7331233 DOI: 10.1186/s12859-020-03626-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/22/2020] [Indexed: 12/20/2022] Open
Abstract
Background Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. The algorithms used K- Nearest Neighbor (K-NN), Naive Bayes, Decision tree J48, JRip, SVM, Adaboost, Stochastic Gradient Decent (SGD) and Decision Table (DT) classifiers to show the performance of the selected classifications algorithms to best classify, and or predict, the HD cases. Results It was shown that using different classification algorithms for the classification of the HD dataset gives very promising results in term of the classification accuracy for the K-NN (K = 1), Decision tree J48 and JRip classifiers with accuracy of classification of 99.7073, 98.0488 and 97.2683% respectively. A feature extraction method was performed using Classifier Subset Evaluator on the HD dataset, and results show enhanced performance in term of the classification accuracy for K-NN (N = 1) and Decision Table classifiers to 100 and 93.8537% respectively after using the selected features by only applying a combination of up to 4 attributes instead of 13 attributes for the predication of the HD cases. Conclusion Different classifiers were used and compared to classify the HD dataset, and we concluded the benefit of having a reliable feature selection method for HD disease prediction with using minimal number of attributes instead of having to consider all available ones.
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Affiliation(s)
- Khaled Mohamad Almustafa
- Department of Information Systems, College of Computer and Information Systems, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.
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83
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Swain SS, Patra D, Singh YO. Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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84
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Wang Z, Zhu Y, Li D, Yin Y, Zhang J. Feature rearrangement based deep learning system for predicting heart failure mortality. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105383. [PMID: 32062185 DOI: 10.1016/j.cmpb.2020.105383] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/22/2020] [Accepted: 02/03/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework. METHODS This paper proposes a feature rearrangement based deep learning system for heart failure mortality prediction. The proposed framework improves the performance of predicting heart failure mortality by handling imbalance problem and achieving better feature representation. This paper also proposes a method named Feature rearrangement based convolutional layer, which demonstrates that the order of the input features is essential for the convolutional network. RESULTS The proposed system is experimentally evaluated on real-world Heart Failure data collected from the EHR system of Shanghai Shuguang Hospital, where 10,198 in-patients records are extracted between March 2009 and April 2016. Internal comparison results illustrate that the proposed framework achieves the best performance for Heart Failure mortality prediction. Extensive experimental results compared with other machine learning methods demonstrate that the proposed method has the highest average accuracy and area under the curve while predicting the three goals of in-hospital mortality, 30-day mortality, and 1-year mortality. Finally, top 12 essential clinical features are mined with their chi-square scores, which can help to assist clinicians in the treatment and research of heart failure. CONCLUSIONS The proposed method successfully predict different target in three observation windows. Feature rearrangement based convolutional layer and Focal loss are employed into the proposed framework, which helps promote the prediction accuracy of Heart Failure death. The proposed method is fast and accurate for predicting heart failure mortality, especially for imbalance situation. This paper also provide a reasonable pipeline to model EHRs data and handle imbalance problem in medical data.
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Affiliation(s)
- Zhe Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China; Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
| | - Yiwen Zhu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China
| | - Dongdong Li
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.
| | - Yichao Yin
- Shanghai Shuguang Hospital, Shanghai 200021, PR China
| | - Jing Zhang
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China
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85
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Aileni RM, Pasca S, Florescu A. EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3346. [PMID: 32545622 PMCID: PMC7348967 DOI: 10.3390/s20123346] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/02/2020] [Accepted: 06/08/2020] [Indexed: 01/26/2023]
Abstract
Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.
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Affiliation(s)
- Raluca Maria Aileni
- Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 060042 Bucharest, Romania; (S.P.); (A.F.)
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86
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ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks. SENSORS 2020; 20:s20113069. [PMID: 32485827 PMCID: PMC7309053 DOI: 10.3390/s20113069] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/19/2020] [Accepted: 05/26/2020] [Indexed: 11/16/2022]
Abstract
Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.
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87
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J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05015-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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88
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Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 2020; 120:103726. [DOI: 10.1016/j.compbiomed.2020.103726] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/16/2020] [Accepted: 03/21/2020] [Indexed: 01/03/2023]
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89
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Sharma A, Garg N, Patidar S, San Tan R, Acharya UR. Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM. Comput Biol Med 2020; 120:103753. [PMID: 32421653 DOI: 10.1016/j.compbiomed.2020.103753] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 11/26/2022]
Abstract
Health care in developing countries demands systems-based screening solutions. In view of this, we present a new rhythm-based methodology for the point-of-care diagnosis of cardiac arrhythmia at a primary level. Such a system will reduce the workload of cardiologists significantly. The method begins by computing the RR-interval sequences from the electrocardiogram(ECG) signals. Then, the Fourier-Bessel (FB) expansion is used to obtain the intelligent series by converting the RR-interval sequences into more meaningful sequences that can characterize the underlying pathology of cardiac arrhythmia with a unique pattern. Ultimately, the obtained intelligent series are used as input to train the long short-term memory (LSTM) model for ECG classification. We have obtained an accuracy of 90.07% in classifying normal and the arrhythmia classes using MIT-BIH database. The results demonstrate that the proposed intelligent series can reveal remarkable differences between the normal and arrhythmia ECG signals. Thus, the proposed algorithm can be used as a primary screening tool for detecting cardiac arrhythmia. Potentially, the developed system can be used by paramedics in rural outreach programs with limited funding and expertise. Moreover, the use of single-lead and short-length ECG signals in the proposed system makes it a suitable candidate for applications that are intended for mobile and other hand-held or wearable devices.
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Affiliation(s)
- Ashish Sharma
- Department of Electronics and Communication Engineering, National Institute of Technology Goa, Goa 403401, India
| | - Niranjan Garg
- Nakshatra Heart and Multispeciality Hospital, Pipliyana Square, Indore, M.P., India
| | - Shivnarayan Patidar
- Department of Electronics and Communication Engineering, National Institute of Technology Goa, Goa 403401, India.
| | - Ru San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, 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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90
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Tuncer T, Dogan S, Subasi A. Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101872] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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91
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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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92
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Demir S, Key S, Tuncer T, Dogan S. An exemplar pyramid feature extraction based humerus fracture classification method. Med Hypotheses 2020; 140:109663. [PMID: 32163795 DOI: 10.1016/j.mehy.2020.109663] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 02/25/2020] [Accepted: 03/02/2020] [Indexed: 12/20/2022]
Abstract
Humerus fracture have been widely seen disease in the orthopedic clinics and classification of them is a hard process for orthopedist. The main aim of the proposed method is to classify humerus fracture by using a naïve and multileveled method. We collected a novel humerus fracture X-ray image dataset. This dataset consists of 115 images. In this paper, a novel stable feature extraction method is presented to classify humerus fractures. This method is called exemplar pyramid method and it is inspired by exemplar facial expression recognition methods. To classify humerus fractures, X-ray images were employed as input. In this study, X-ray images are resized to 512 × 512 sized image. Then, the used humerus fracture images are divided into 64 × 64 size of exemplars. To create levels, maximum pooling which has been mostly used in deep networks is used and four levels are created. Histogram of oriented gradients (HOG) and local binary pattern (LBP) are employed for feature generation. The most discriminative ones of the generated and concatenated features are selected by using ReliefF and Neighborhood Component Analysis (NCA) based two levelled feature selector (RFNCA). To emphasize success of the proposed exemplar pyramid model based feature generation, four conventional classifiers are chosen for classification and the proposed exemplar pyramid model achieved 99.12% classification accuracy by using leave one out cross validation (LOOCV). Results and tests clearly illustrates success of the proposed exemplar pyramid model based humerus fracture classification method. The results also shown that the proposed exemplar pyramid model achieved higher classification rate than Orthopedist specialized in shoulder.
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Affiliation(s)
- Sukru Demir
- Department of Orthopedic, Firat University Faculty of Medicine, Elazığ, Turkey
| | - Sefa Key
- Department of Orthopedic, Bingöl State Hospital, Bingöl, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Faculty of Technology, Elazığ, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Faculty of Technology, Elazığ, Turkey
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93
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Demir FB, Tuncer T, Kocamaz AF, Ertam F. A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection. Med Hypotheses 2020; 139:109626. [PMID: 32087492 DOI: 10.1016/j.mehy.2020.109626] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 02/10/2020] [Accepted: 02/12/2020] [Indexed: 12/18/2022]
Abstract
Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.
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Affiliation(s)
- Fahrettin Burak Demir
- Department of Computer Sciences, Vahap Kucuk Vocational School, Malatya Turgut Ozal University, Malatya, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Adnan Fatih Kocamaz
- Department of Computer Engineering, Engineering Faculty, Inonu University, Malatya, Turkey.
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
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94
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Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method. SENSORS 2019; 19:s19235079. [PMID: 31766323 PMCID: PMC6928852 DOI: 10.3390/s19235079] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/10/2019] [Accepted: 11/15/2019] [Indexed: 11/16/2022]
Abstract
Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.
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Yaman O, Ertam F, Tuncer T. Automated Parkinson's disease recognition based on statistical pooling method using acoustic features. Med Hypotheses 2019; 135:109483. [PMID: 31954340 DOI: 10.1016/j.mehy.2019.109483] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/06/2019] [Accepted: 11/08/2019] [Indexed: 02/08/2023]
Abstract
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.
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Affiliation(s)
- Orhan Yaman
- Department of Informatics, Firat University, Elazig, Turkey.
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
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