An ECG classification using DNN classifier with modified pigeon inspired optimizer
Arrhythmia is a form of heart disease in which the regularity of the pulse is changed.ECG data may be analyzed to detect heart-related illnesses or arrhythmias. This paper presents a wrapper feature selection strategy that employs a Pigeon-inspired optimizer(PIO). The modified Pigeon Inspired Optimi...
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| Veröffentlicht in: | Multimedia tools and applications Jg. 81; H. 7; S. 9131 - 9150 |
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01.03.2022
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| Abstract | Arrhythmia is a form of heart disease in which the regularity of the pulse is changed.ECG data may be analyzed to detect heart-related illnesses or arrhythmias. This paper presents a wrapper feature selection strategy that employs a Pigeon-inspired optimizer(PIO). The modified Pigeon Inspired Optimizer (MPIO) is used to optimize ECG features and the Deep Neural Network (DNN) to classify the ECG signals. In MPIO, the new blood pigeons were introduced to improve the accuracy of the algorithm. Morphological features, wavelet transform coefficients, and R-R interval dynamic features are extracted for classification of ECG signals. After feature extraction, MPIO is used for feature optimization because optimizing the feature plays a key role in developing the model of machine learning, and irrelevant data features degrade model accuracy and enhance model training time. Using optimised features, the DNN classifier is utilised to classify ECG data. The proposed method achieves 99.10% accuracy, 98.90% specificity, and 98.50% sensitivity. Additionally, when compared with other state-of-the-art methodologies, our method of feature selection also exhibited better outcomes. |
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| AbstractList | Arrhythmia is a form of heart disease in which the regularity of the pulse is changed.ECG data may be analyzed to detect heart-related illnesses or arrhythmias. This paper presents a wrapper feature selection strategy that employs a Pigeon-inspired optimizer(PIO). The modified Pigeon Inspired Optimizer (MPIO) is used to optimize ECG features and the Deep Neural Network (DNN) to classify the ECG signals. In MPIO, the new blood pigeons were introduced to improve the accuracy of the algorithm. Morphological features, wavelet transform coefficients, and R-R interval dynamic features are extracted for classification of ECG signals. After feature extraction, MPIO is used for feature optimization because optimizing the feature plays a key role in developing the model of machine learning, and irrelevant data features degrade model accuracy and enhance model training time. Using optimised features, the DNN classifier is utilised to classify ECG data. The proposed method achieves 99.10% accuracy, 98.90% specificity, and 98.50% sensitivity. Additionally, when compared with other state-of-the-art methodologies, our method of feature selection also exhibited better outcomes. |
| Author | Nainwal, Ashish Jha, Bhola Kumar, Yatindra |
| Author_xml | – sequence: 1 givenname: Ashish orcidid: 0000-0003-4857-4911 surname: Nainwal fullname: Nainwal, Ashish email: ashish.nainwal@gkv.ac.in organization: Department of ECE, FET Gurukul Kangri University – sequence: 2 givenname: Yatindra surname: Kumar fullname: Kumar, Yatindra organization: Department of Electrical Engineering, GBPIET – sequence: 3 givenname: Bhola surname: Jha fullname: Jha, Bhola organization: Department of Electrical Engineering, GBPIET |
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| Cites_doi | 10.1016/j.mehy.2019.109515 10.1016/j.cmpb.2015.12.024 10.1016/j.patrec.2019.02.016 10.1016/j.eswa.2020.113364 10.1016/j.eswa.2010.04.087 10.1109/TBME.2011.2112658 10.1016/j.compbiomed.2018.06.002 10.1016/j.neucom.2015.04.063 10.1108/IJICC-02-2014-0005 10.1016/j.jksuci.2018.02.005 10.1109/PROC.1981.12095 10.1007/s00034-015-0068-7 10.1016/j.cmpb.2013.12.002 10.1016/j.advengsoft.2016.01.008 10.1109/TBME.1985.325532 10.1016/j.bspc.2015.10.008 10.1016/j.ins.2018.06.062 10.1007/s10916-018-1083-6 10.1016/j.future.2018.03.057 10.1016/j.ins.2016.01.082 10.1007/s11760-012-0339-8 10.1038/s41598-016-0028-x 10.1016/j.compbiomed.2018.03.016 10.1016/j.artmed.2020.101843 10.1016/j.amc.2009.03.090 10.1016/j.ijcac.2015.12.001 10.1016/j.eswa.2011.08.025 10.1109/51.932724 10.1016/j.bspc.2018.08.007 10.1016/j.bspc.2011.07.001 10.1109/TBME.2009.2013934 10.1109/TBME.2013.2290800 10.1109/MCI.2006.329691 10.1109/TBME.2011.2113395 10.17678/beuscitech.344953 10.1016/j.jelectrocard.2019.11.046 10.1007/BF02637023 10.1016/j.dsp.2008.09.002 10.1016/j.neunet.2018.01.009 10.1016/j.eswax.2019.100003 10.1007/978-981-13-9263-4_1 10.5334/jors.bi 10.1145/3278576.3278598 10.1007/978-0-387-33532-2_16 |
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| References | Bhagyalakshmi, Pujeri, Devanagavi (CR6) 2021; 33 Ince, Kiranyaz, Gabbouj (CR21) 2009; 56 Elhaj, Salim, Harris, Swee, Ahmed (CR18) 2016; 127 CR19 Lake (CR28) 1981; 69 Choi, Seo, Lee, Kim (CR9) 2020; 105 Li, Feng, Cao, Li, Liang, Chen (CR29) 2016; 35 Moody, Mark (CR35) 2001; 20 CR12 Alonso-Atienza, Morgado, Fernandez-Martinez, García-Alberola, Rojo-Alvarez (CR4) 2013; 61 Karaboga, Akay (CR23) 2009; 214 Singh (CR42) 2015; 167 Pan, Tompkins (CR38) 1985; 3 Wang, Zhang, Liu, Yang, Fu, Wang, Zhang (CR44) 2019; 501 Chandrakar, Yadav, Chandra (CR8) 2013; 2 Deng, Wang, Tang, Zheng (CR13) 2018; 100 Homaeinezhad, Atyabi, Tavakkoli, Toosi, Ghaffari, Ebrahimpour (CR20) 2012; 39 Jiang, Zhang, Pi, Dai (CR22) 2019; 1 Mondéjar-Guerra, Novo, Rouco, Penedo, Ortega (CR34) 2019; 47 Al Rahhal, Bazi, AlHichri, Alajlan, Melgani, Yager (CR1) 2016; 345 Alajlan, Bazi, Melgani, Malek, Bencherif (CR2) 2014; 8 Oh, Ng, San Tan, Acharya (CR37) 2018; 102 Laguna, Jané, Olmos, Thakor, Rix, Caminal (CR27) 1996; 34 Mirjalili, Lewis (CR33) 2016; 95 Li, Zhou, Wan, Li, Mou (CR31) 2020; 58 Duan, Qiao (CR17) 2014; 7 Li, Yuan, Ma, Cui, Cao (CR30) 2017; 7 Übeyli (CR43) 2009; 19 Diker, Avci, Tanyildizi, Gedikpinar (CR15) 2020; 136 Korürek, Doğan (CR26) 2010; 37 CR24 Kora, Krishna (CR25) 2016; 2 Alcaraz, Sandberg, Sörnmo, Rieta (CR3) 2011; 58 Daqrouq, Alkhateeb, Ajour, Morfeq (CR11) 2014; 113 Celin, Vasanth (CR7) 2018; 42 Neggaz, Houssein, Hussain (CR36) 2020; 152 Sannino, De Pietro (CR39) 2018; 86 CR41 Shadmand, Mashoufi (CR40) 2016; 25 Baloglu, Talo, Yildirim, San Tan, Acharya (CR5) 2019; 122 Yildirim (CR45) 2018; 96 Diker, Cömert, Engin (CR14) 2017; 7 Daamouche, Hamami, Alajlan, Melgani (CR10) 2012; 7 Mar, Zaunseder, Martínez, Llamedo, Poll (CR32) 2011; 58 Dorigo, Birattari, Stutzle (CR16) 2006; 1 N Alajlan (11594_CR2) 2014; 8 V Bhagyalakshmi (11594_CR6) 2021; 33 P Kora (11594_CR25) 2016; 2 11594_CR41 V Mondéjar-Guerra (11594_CR34) 2019; 47 B Chandrakar (11594_CR8) 2013; 2 H Li (11594_CR29) 2016; 35 M Deng (11594_CR13) 2018; 100 UB Baloglu (11594_CR5) 2019; 122 N Neggaz (11594_CR36) 2020; 152 M Choi (11594_CR9) 2020; 105 S Celin (11594_CR7) 2018; 42 FA Elhaj (11594_CR18) 2016; 127 11594_CR19 R Lake (11594_CR28) 1981; 69 F Alonso-Atienza (11594_CR4) 2013; 61 T Ince (11594_CR21) 2009; 56 Z Li (11594_CR31) 2020; 58 MM Al Rahhal (11594_CR1) 2016; 345 11594_CR12 J Pan (11594_CR38) 1985; 3 H Li (11594_CR30) 2017; 7 T Mar (11594_CR32) 2011; 58 H Duan (11594_CR17) 2014; 7 S Shadmand (11594_CR40) 2016; 25 M Korürek (11594_CR26) 2010; 37 P Laguna (11594_CR27) 1996; 34 R Alcaraz (11594_CR3) 2011; 58 ED Übeyli (11594_CR43) 2009; 19 A Diker (11594_CR15) 2020; 136 M Dorigo (11594_CR16) 2006; 1 SL Oh (11594_CR37) 2018; 102 GB Moody (11594_CR35) 2001; 20 G Wang (11594_CR44) 2019; 501 MR Homaeinezhad (11594_CR20) 2012; 39 S Mirjalili (11594_CR33) 2016; 95 D Karaboga (11594_CR23) 2009; 214 YN Singh (11594_CR42) 2015; 167 A Daamouche (11594_CR10) 2012; 7 J Jiang (11594_CR22) 2019; 1 K Daqrouq (11594_CR11) 2014; 113 G Sannino (11594_CR39) 2018; 86 A Diker (11594_CR14) 2017; 7 11594_CR24 Ö Yildirim (11594_CR45) 2018; 96 |
| References_xml | – volume: 136 start-page: 109515 year: 2020 ident: CR15 article-title: A novel ecg signal classification method using dea-elm publication-title: Medical Hypotheses doi: 10.1016/j.mehy.2019.109515 – volume: 127 start-page: 52 year: 2016 end-page: 63 ident: CR18 article-title: Arrhythmia recognition and classification using combined linear and nonlinear features of ecg signals publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2015.12.024 – volume: 122 start-page: 23 year: 2019 end-page: 30 ident: CR5 article-title: Classification of myocardial infarction with multi-lead ecg signals and deep cnn publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2019.02.016 – volume: 152 start-page: 113364 year: 2020 ident: CR36 article-title: An efficient henry gas solubility optimization for feature selection publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113364 – volume: 37 start-page: 7563 issue: 12 year: 2010 end-page: 7569 ident: CR26 article-title: Ecg beat classification using particle swarm optimization and radial basis function neural network publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.04.087 – volume: 58 start-page: 1441 issue: 5 year: 2011 end-page: 1449 ident: CR3 article-title: Classification of paroxysmal and persistent atrial fibrillation in ambulatory ecg recordings publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2011.2112658 – ident: CR12 – volume: 102 start-page: 278 year: 2018 end-page: 287 ident: CR37 article-title: Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.06.002 – volume: 167 start-page: 322 year: 2015 end-page: 335 ident: CR42 article-title: Human recognition using fishers discriminant analysis of heartbeat interval features and ecg morphology publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.063 – volume: 7 start-page: 24 issue: 1 year: 2014 end-page: 37 ident: CR17 article-title: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning publication-title: International Journal of Intelligent Computing and Cybernetics doi: 10.1108/IJICC-02-2014-0005 – volume: 33 start-page: 54 issue: 1 year: 2021 end-page: 67 ident: CR6 article-title: Gb-svnn: genetic bat assisted support vector neural network for arrhythmia classification using ecg signals publication-title: Journal of King Saud University-Computer and Information Sciences doi: 10.1016/j.jksuci.2018.02.005 – volume: 69 start-page: 856 issue: 7 year: 1981 end-page: 857 ident: CR28 article-title: Programs for digital signal processing publication-title: Proceedings of the IEEE doi: 10.1109/PROC.1981.12095 – volume: 35 start-page: 339 issue: 1 year: 2016 end-page: 352 ident: CR29 article-title: A new ecg signal classification based on wpd and apen feature extraction publication-title: Circuits, Systems, and Signal Processing doi: 10.1007/s00034-015-0068-7 – volume: 113 start-page: 919 issue: 3 year: 2014 end-page: 926 ident: CR11 article-title: Neural network and wavelet average framing percentage energy for atrial fibrillation classification publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2013.12.002 – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: CR33 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 3 start-page: 230 year: 1985 end-page: 236 ident: CR38 article-title: A real-time qrs detection algorithm publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.1985.325532 – volume: 25 start-page: 12 year: 2016 end-page: 23 ident: CR40 article-title: A new personalized ecg signal classification algorithm using block-based neural network and particle swarm optimization publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2015.10.008 – volume: 501 start-page: 523 year: 2019 end-page: 542 ident: CR44 article-title: A global and updatable ecg beat classification system based on recurrent neural networks and active learning publication-title: Information Sciences doi: 10.1016/j.ins.2018.06.062 – volume: 42 start-page: 1 issue: 12 year: 2018 end-page: 11 ident: CR7 article-title: Ecg signal classification using various machine learning techniques publication-title: Journal of Medical Systems doi: 10.1007/s10916-018-1083-6 – volume: 86 start-page: 446 year: 2018 end-page: 455 ident: CR39 article-title: A deep learning approach for ecg-based heartbeat classification for arrhythmia detection publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2018.03.057 – volume: 345 start-page: 340 year: 2016 end-page: 354 ident: CR1 article-title: Deep learning approach for active classification of electrocardiogram signals publication-title: Inform Sci doi: 10.1016/j.ins.2016.01.082 – volume: 8 start-page: 931 issue: 5 year: 2014 end-page: 942 ident: CR2 article-title: Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods publication-title: Signal, Image and Video Processing doi: 10.1007/s11760-012-0339-8 – volume: 7 start-page: 1 issue: 1 year: 2017 end-page: 12 ident: CR30 article-title: Genetic algorithm for the optimization of features and neural networks in ecg signals classification publication-title: Scientific Reports doi: 10.1038/s41598-016-0028-x – volume: 96 start-page: 189 year: 2018 end-page: 202 ident: CR45 article-title: A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.03.016 – volume: 105 start-page: 101843 year: 2020 ident: CR9 article-title: Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system publication-title: Artificial Intelligence in Medicine doi: 10.1016/j.artmed.2020.101843 – ident: CR19 – volume: 214 start-page: 108 issue: 1 year: 2009 end-page: 132 ident: CR23 article-title: A comparative study of artificial bee colony algorithm publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2009.03.090 – volume: 2 start-page: 44 issue: 1 year: 2016 end-page: 48 ident: CR25 article-title: Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block publication-title: International Journal of the Cardiovascular Academy doi: 10.1016/j.ijcac.2015.12.001 – volume: 39 start-page: 2047 issue: 2 year: 2012 end-page: 2058 ident: CR20 article-title: Ecg arrhythmia recognition via a neuro-svm-knn hybrid classifier with virtual qrs image-based geometrical features publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.08.025 – volume: 20 start-page: 45 issue: 3 year: 2001 end-page: 50 ident: CR35 article-title: The impact of the mit-bih arrhythmia database publication-title: IEEE Engineering in Medicine and Biology Magazine doi: 10.1109/51.932724 – volume: 47 start-page: 41 year: 2019 end-page: 48 ident: CR34 article-title: Heartbeat classification fusing temporal and morphological information of ecgs via ensemble of classifiers publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2018.08.007 – volume: 7 start-page: 342 issue: 4 year: 2012 end-page: 349 ident: CR10 article-title: A wavelet optimization approach for ecg signal classification publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2011.07.001 – volume: 56 start-page: 1415 issue: 5 year: 2009 end-page: 1426 ident: CR21 article-title: A generic and robust system for automated patient-specific classification of ecg signals publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2009.2013934 – volume: 61 start-page: 832 issue: 3 year: 2013 end-page: 840 ident: CR4 article-title: Detection of life-threatening arrhythmias using feature selection and support vector machines publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2013.2290800 – volume: 1 start-page: 28 issue: 4 year: 2006 end-page: 39 ident: CR16 article-title: Ant colony optimization publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2006.329691 – volume: 58 start-page: 2168 issue: 8 year: 2011 end-page: 2177 ident: CR32 article-title: Optimization of ecg classification by means of feature selection publication-title: IEEE Transactions on Biomedical Engineering doi: 10.1109/TBME.2011.2113395 – volume: 7 start-page: 132 issue: 2 year: 2017 end-page: 139 ident: CR14 article-title: A diagnostic model for identification of myocardial infarction from electrocardiography signals publication-title: Bitlis Eren University Journal of Science and Technology doi: 10.17678/beuscitech.344953 – volume: 58 start-page: 105 year: 2020 end-page: 112 ident: CR31 article-title: Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram publication-title: Journal of Electrocardiology doi: 10.1016/j.jelectrocard.2019.11.046 – volume: 34 start-page: 58 issue: 1 year: 1996 end-page: 68 ident: CR27 article-title: Adaptive estimation of qrs complex wave features of ecg signal by the hermite model publication-title: Medical and Biological Engineering and Computing doi: 10.1007/BF02637023 – volume: 19 start-page: 320 issue: 2 year: 2009 end-page: 329 ident: CR43 article-title: Combining recurrent neural networks with eigenvector methods for classification of ecg beats publication-title: Digital Signal Processing doi: 10.1016/j.dsp.2008.09.002 – ident: CR41 – ident: CR24 – volume: 2 start-page: 1354 issue: 3 year: 2013 end-page: 1357 ident: CR8 article-title: A survey of noise removal techniques for ECG signals publication-title: International Journal of Advanced Research in Computer and Communication Engineering – volume: 100 start-page: 70 year: 2018 end-page: 83 ident: CR13 article-title: Extracting cardiac dynamics within ecg signal for human identification and cardiovascular diseases classification publication-title: Neural Netw doi: 10.1016/j.neunet.2018.01.009 – volume: 1 start-page: 100003 year: 2019 ident: CR22 article-title: A novel multi-module neural network system for imbalanced heartbeats classification publication-title: Expert Systems with Applications: X doi: 10.1016/j.eswax.2019.100003 – volume: 501 start-page: 523 year: 2019 ident: 11594_CR44 publication-title: Information Sciences doi: 10.1016/j.ins.2018.06.062 – volume: 105 start-page: 101843 year: 2020 ident: 11594_CR9 publication-title: Artificial Intelligence in Medicine doi: 10.1016/j.artmed.2020.101843 – ident: 11594_CR24 doi: 10.1007/978-981-13-9263-4_1 – volume: 1 start-page: 100003 year: 2019 ident: 11594_CR22 publication-title: Expert Systems with Applications: X doi: 10.1016/j.eswax.2019.100003 – volume: 7 start-page: 24 issue: 1 year: 2014 ident: 11594_CR17 publication-title: International Journal of Intelligent Computing and Cybernetics doi: 10.1108/IJICC-02-2014-0005 – volume: 122 start-page: 23 year: 2019 ident: 11594_CR5 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2019.02.016 – volume: 39 start-page: 2047 issue: 2 year: 2012 ident: 11594_CR20 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.08.025 – volume: 152 start-page: 113364 year: 2020 ident: 11594_CR36 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113364 – volume: 113 start-page: 919 issue: 3 year: 2014 ident: 11594_CR11 publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2013.12.002 – volume: 102 start-page: 278 year: 2018 ident: 11594_CR37 publication-title: Computers in Biology and Medicine doi: 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publication-title: Bitlis Eren University Journal of Science and Technology doi: 10.17678/beuscitech.344953 – volume: 42 start-page: 1 issue: 12 year: 2018 ident: 11594_CR7 publication-title: Journal of Medical Systems doi: 10.1007/s10916-018-1083-6 – volume: 1 start-page: 28 issue: 4 year: 2006 ident: 11594_CR16 publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2006.329691 – volume: 2 start-page: 44 issue: 1 year: 2016 ident: 11594_CR25 publication-title: International Journal of the Cardiovascular Academy doi: 10.1016/j.ijcac.2015.12.001 – volume: 100 start-page: 70 year: 2018 ident: 11594_CR13 publication-title: Neural Netw doi: 10.1016/j.neunet.2018.01.009 – volume: 96 start-page: 189 year: 2018 ident: 11594_CR45 publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2018.03.016 – volume: 33 start-page: 54 issue: 1 year: 2021 ident: 11594_CR6 publication-title: Journal of King Saud University-Computer and Information Sciences doi: 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