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Gholami M, Maleki M, Amirkhani S, Chaibakhsh A. Nonlinear model-based cardiac arrhythmia diagnosis using the optimization-based inverse problem solution. Biomed Eng Lett 2022; 12:205-215. [PMID: 35529347 PMCID: PMC9046521 DOI: 10.1007/s13534-022-00223-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 10/18/2022] Open
Abstract
This study investigates a nonlinear model-based feature extraction approach for the accurate classification of four types of heartbeats. The features are the morphological parameters of ECG signal derived from the nonlinear ECG model using an optimization-based inverse problem solution. In the model-based methods, high feature extraction time is a crucial issue. In order to reduce the feature extraction time, a new structure was employed in the optimization algorithms. Using the proposed structure has considerably increased the speed of feature extraction. In the following, the effectiveness of two types of optimization methods (genetic algorithm and particle swarm optimization) and the McSharry ECG model has been studied and compared in terms of speed and accuracy of diagnosis. In the classification section, the adaptive neuro-fuzzy inference system and fuzzy c-mean clustering methods, along with the principal component analysis data reduction method, have been utilized. The obtained results reveal that using an adaptive neuro-fuzzy inference system with data obtained from particle swarm optimization will have the shortest process time and the best diagnosis, with a mean accuracy of 99% and a mean sensitivity of 99.11%.
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Affiliation(s)
- Maryam Gholami
- Department of Engineering, Islamic Azad University of Kazerun, Kazerun, Fars Iran
| | - Mahsa Maleki
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Saeed Amirkhani
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
| | - Ali Chaibakhsh
- Faculty of Mechanical Engineering, University of Guilan, P.O. Box 41938-33697, Rasht, Guilan Iran.,Intelligent Systems and Advanced Control Lab, University of Guilan, Rasht, Guilan 41996-13776 Iran
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2
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Setiawan NA, Nugroho HA, Persada AG, Yuwono T, Prasojo I, Rahmadi R, Wijaya A. Classification of arrhythmia’s ECG signal using cascade transparent classifier. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-189768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature’s attributes were selected, and the final step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model.
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Affiliation(s)
- Noor Akhmad Setiawan
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Hanung Adi Nugroho
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Anugerah Galang Persada
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Tito Yuwono
- Department of Electrical Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia
| | - Ipin Prasojo
- Department of Biomedical Engineering Technology, ITS PKU Muhammadiyah, Surakarta, Indonesia
| | - Ridho Rahmadi
- Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia
| | - Adi Wijaya
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
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3
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Jangra M, Dhull SK, Singh KK, Singh A, Cheng X. O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification. COMPLEX INTELL SYST 2021; 9:2685-2698. [PMID: 34777963 PMCID: PMC8075024 DOI: 10.1007/s40747-021-00371-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/07/2021] [Indexed: 11/21/2022]
Abstract
The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.
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Affiliation(s)
- Manisha Jangra
- Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana India
| | - Sanjeev Kumar Dhull
- Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana India
| | - Krishna Kant Singh
- Faculty of Engineering and Technology, Jain (Deemed-to-be University), Bengaluru, India
| | - Akansha Singh
- Department of Computer Science Engineering, ASET, Amity University Uttar Pradesh, Noida, India
| | - Xiaochun Cheng
- Department of Computer Science, Middlesex University, London, UK
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4
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Jangra M, Dhull SK, Singh KK. ECG arrhythmia classification using modified visual geometry group network (mVGGNet). JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191135] [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]
Affiliation(s)
- Manisha Jangra
- Department of ECE, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India
| | - Sanjeev Kr. Dhull
- Department of ECE, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India
| | - Krishna Kant Singh
- Department of Electronics and Communication Engineering, KIET Group of Institutions, Ghaziabad, India
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Li F, Chen K, Ling J, Zhan Y, Manogaran G. Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.04.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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6
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Chetan A, Tripathy RK, Dandapat S. A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0294-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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7
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Dhar P, Dutta S, Das P, Mukherjee V. Cross-wavelet aided ECG beat classification using LIBSVM. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1251339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Priyadarshiny Dhar
- Applied Electronics & Instrumentation Engineering, Netaji Subhash Engineering College, Kolkata, India
| | - Saibal Dutta
- Electrical Engineering, Heritage Institute of Technology, Kolkata, India
| | - Prithwiraj Das
- Electrical Engineering, Government College of Engineering and Textile Technology, Berhampore, India
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Li F, Zheng D, Zhao T, Pedrycz W. A novel approach for anomaly detection in data streams: Fuzzy-statistical detection mode. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016. [DOI: 10.3233/ifs-151910] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Fenghuan Li
- MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China
| | - Dequan Zheng
- MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China
| | - Tiejun Zhao
- MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China
| | - Witold Pedrycz
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada
- Institute of Systems Research, Polish Academy of Sciences, Warsaw, Poland
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DESAI USHA, MARTIS ROSHANJOY, GURUDAS NAYAK C, SESHIKALA G, SARIKA K, SHETTY K. RANJAN. DECISION SUPPORT SYSTEM FOR ARRHYTHMIA BEATS USING ECG SIGNALS WITH DCT, DWT AND EMD METHODS: A COMPARATIVE STUDY. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519416400121] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test ([Formula: see text]) and fed to k-Nearest Neighbor (k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen’s kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.
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Affiliation(s)
- USHA DESAI
- Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte, Udupi, Karnataka 574110, India
- School of Electronics and Communication Engineering, REVA University, Bengaluru 560064, India
| | - ROSHAN JOY MARTIS
- Department of Electronics and Communication Engineering, St. Joseph Engineering College, Mangaloru 575028, India
| | - C. GURUDAS NAYAK
- Department of Instrumentation and Control Engineering, MIT, Manipal University, Manipal 576104, India
| | - G. SESHIKALA
- School of Electronics and Communication Engineering, REVA University, Bengaluru 560064, India
| | - K. SARIKA
- Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte, Udupi, Karnataka 574110, India
| | - RANJAN SHETTY K.
- Department of Cardiology, Kasturba Medical College, Manipal University, Manipal 576104, India
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Dilmac S, Korurek M. ECG heart beat classification method based on modified ABC algorithm. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.07.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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11
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Ebrahimzadeh A, Shakiba B, Khazaee A. Detection of electrocardiogram signals using an efficient method. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.05.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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12
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Sanchez MA, Castillo O, Castro JR, Melin P. Fuzzy granular gravitational clustering algorithm for multivariate data. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.04.005] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yang P, Yang H, Qiu W, Wang S, Li C. Optimal approach on net routing for VLSI physical design based on Tabu-ant colonies modeling. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.03.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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14
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A novel automatic detection system for ECG arrhythmias using maximum margin clustering with immune evolutionary algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:453402. [PMID: 23690875 PMCID: PMC3652208 DOI: 10.1155/2013/453402] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Revised: 04/01/2013] [Accepted: 04/02/2013] [Indexed: 11/17/2022]
Abstract
This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
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