{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:23:30Z","timestamp":1763749410404,"version":"3.32.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03604-8","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T13:56:55Z","timestamp":1736258215000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Arrhythmia Detection Using War Strategy Optimization Enabled with Archimedes Optimization Algorithm and Rule-Based Classifiers"],"prefix":"10.1007","volume":"6","author":[{"given":"Prakash Chandra","family":"Sahoo","sequence":"first","affiliation":[]},{"given":"Binod Kumar","family":"Pattnaik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"3604_CR1","doi-asserted-by":"publisher","first-page":"2793","DOI":"10.1093\/eurheartj\/ehv316","volume":"36","author":"Councils, ESC","year":"2015","unstructured":"Councils, ESC. 2015 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. Eur Heart J. 2015;36:2793\u2013867.","journal-title":"Eur Heart J"},{"issue":"3","key":"3604_CR2","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/S0735-1097(86)80478-8","volume":"7","author":"DS Baim","year":"1986","unstructured":"Baim DS, et al. Survival of patients with severe congestive heart failure treated with oral milrinone. J Am Coll Cardiol. 1986;7(3):661\u201370.","journal-title":"J Am Coll Cardiol"},{"key":"3604_CR3","doi-asserted-by":"publisher","first-page":"S70","DOI":"10.1016\/j.jelectrocard.2019.08.004","volume":"57","author":"S Parvaneh","year":"2019","unstructured":"Parvaneh S, Rubin J, Babaeizadeh S, Xu-Wilson M. Cardiac arrhythmia detection using deep learning: a review. J Electrocardiol. 2019;57:S70\u20134.","journal-title":"J Electrocardiol"},{"issue":"3","key":"3604_CR4","first-page":"44","volume":"2","author":"VK Srivastava","year":"2013","unstructured":"Srivastava VK. DWT-based feature extraction from ECG signal. Am J Eng Res. 2013;2(3):44\u201350.","journal-title":"Am J Eng Res"},{"issue":"8","key":"3604_CR5","doi-asserted-by":"publisher","first-page":"5751","DOI":"10.1016\/j.eswa.2010.02.033","volume":"37","author":"H Khorrami","year":"2010","unstructured":"Khorrami H, Moavenian M. A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst Appl. 2010;37(8):5751\u20137.","journal-title":"Expert Syst Appl"},{"key":"3604_CR6","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1016\/j.aej.2022.03.016","volume":"61","author":"RA Alharbey","year":"2022","unstructured":"Alharbey RA, et al. The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters. Alex Eng J. 2022;61:9243\u20138.","journal-title":"Alex Eng J"},{"key":"3604_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102326","volume":"65","author":"AS Eltrass","year":"2021","unstructured":"Eltrass AS, Tayel MB, Ammar AI. A new automated CNN deep learning approach for identification of ECG congestive heart failure and arrhythmia using constant-Q non-stationary Gabor transform. Biomed Signal Process Control. 2021;65: 102326.","journal-title":"Biomed Signal Process Control"},{"key":"3604_CR8","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1016\/j.procs.2016.05.512","volume":"80","author":"M Elleuch","year":"2016","unstructured":"Elleuch M, Maalej R, Kherallah M. A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Procedia Comput Sci. 2016;80:1712\u201323.","journal-title":"Procedia Comput Sci"},{"key":"3604_CR9","unstructured":"Abdelmalek B, Ahmed K, Amine TM. Lightweight CNNs-based object detection for embedded systems implementation. In: Conference on innovative trends in computer science (CITCS'2019); 2019."},{"issue":"6","key":"3604_CR10","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1007\/s40846-018-0389-7","volume":"38","author":"MM Al Rahhal","year":"2018","unstructured":"Al Rahhal MM, Bazi Y, Al Zuair M, Othman E, BenJdira B. Convolutional neural networks for electrocardiogram classification. J Med Biol Eng. 2018;38(6):1014\u201325.","journal-title":"J Med Biol Eng"},{"key":"3604_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101819","volume":"57","author":"C Chen","year":"2020","unstructured":"Chen C, Hua Z, Zhang R, Liu G, Wen W. Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomed Signal Process Control. 2020;57: 101819.","journal-title":"Biomed Signal Process Control"},{"key":"3604_CR12","doi-asserted-by":"publisher","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","volume":"7","author":"J Huang","year":"2019","unstructured":"Huang J, Chen B, Yao B, He W. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access. 2019;7:92871\u201380.","journal-title":"IEEE Access"},{"key":"3604_CR13","doi-asserted-by":"crossref","unstructured":"Kumari CU, Prasad SJ, Mounika G. (2019). Leaf disease detection: feature extraction with K-means clustering and classification with ANN. In: Proceedings of the 3rd international conference on computing methodologies and communication (ICCMC); 2019. p. 1095\u20138.","DOI":"10.1109\/ICCMC.2019.8819750"},{"key":"3604_CR14","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ymeth.2021.04.021","volume":"202","author":"C-Y Chen","year":"2022","unstructured":"Chen C-Y, et al. Automated ECG classification based on 1D deep learning network. Methods. 2022;202:127\u201335.","journal-title":"Methods"},{"issue":"11","key":"3604_CR15","doi-asserted-by":"publisher","first-page":"8755","DOI":"10.1007\/s00521-022-06889-z","volume":"34","author":"AS Eltrass","year":"2022","unstructured":"Eltrass AS, Tayel MB, Ammar AI. Automated ECG multiclass classification system based on combining deep learning features with HRV and ECG measures. Neural Comput Appl. 2022;34(11):8755\u201375.","journal-title":"Neural Comput Appl"},{"issue":"3","key":"3604_CR16","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2015","unstructured":"Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng. 2015;63(3):664\u201375.","journal-title":"IEEE Trans Biomed Eng"},{"key":"3604_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103638","volume":"76","author":"A Kumar","year":"2022","unstructured":"Kumar A, Kumar S, Dutt V, Dubey AK. IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier. Biomed Signal Process Control. 2022;76: 103638.","journal-title":"Biomed Signal Process Control"},{"key":"3604_CR18","doi-asserted-by":"crossref","unstructured":"Tummala, Ayyarao SLV, et al. War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization. IEEE Access. 2022;10:25073\u201325105","DOI":"10.1109\/ACCESS.2022.3153493"},{"key":"3604_CR19","volume-title":"Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence","author":"A Hashim","year":"2020","unstructured":"Hashim A, et al. Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence. Berlin: Springer; 2020."},{"key":"3604_CR20","doi-asserted-by":"publisher","first-page":"100519","DOI":"10.1016\/j.measen.2022.100519","volume":"24","author":"SK Mohapatra","year":"2022","unstructured":"Mohapatra SK, Patnaik S. ESA-ASO: an enhanced search ability based atom search optimization algorithm for epileptic seizure detection. Meas Sens. 2022;24:100519.","journal-title":"Meas Sens"},{"key":"3604_CR21","volume-title":"A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with computers","author":"S Barshandeh","year":"2020","unstructured":"Barshandeh S, Haghzadeh M. A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with computers. Berlin: Springer; 2020."},{"issue":"Suppl 4","key":"3604_CR22","doi-asserted-by":"publisher","first-page":"S2797","DOI":"10.1007\/s00366-021-01431-6","volume":"38","author":"HRR Zaman","year":"2022","unstructured":"Zaman HRR, Gharehchopogh FS. An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput. 2022;38(Suppl 4):S2797\u2013831.","journal-title":"Eng Comput"},{"key":"3604_CR23","doi-asserted-by":"publisher","unstructured":"Arora S, Anand P. A chaotic grasshopper optimization algorithm for global optimization. 2019;31. https:\/\/doi.org\/10.1007\/s00521-018-3343-2.","DOI":"10.1007\/s00521-018-3343-2"},{"key":"3604_CR24","doi-asserted-by":"crossref","unstructured":"Gharehchopogh FS. An improved tunicate swarm algorithm with best\u2011random mutation strategy for global optimization problems. J Bionic Eng. 2022;19(4):1177\u20131202.","DOI":"10.1007\/s42235-022-00185-1"},{"key":"3604_CR25","doi-asserted-by":"publisher","unstructured":"Liu LR, Huang MY, Huang ST, Kung LC, Lee CH, Yao WT, Tsai MF, Hsu CH, Chu YC, Hung FH, Chiu HW. An arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection. Heliyon. 2024;10(5):e27200. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e27200.","DOI":"10.1016\/j.heliyon.2024.e27200"},{"key":"3604_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102818","volume":"150","author":"S Din","year":"2024","unstructured":"Din S, Qaraqe M, Mourad O, Qaraqe K, Serpedin E. ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range dependency features. Artif Intell Med. 2024;150: 102818.","journal-title":"Artif Intell Med"},{"key":"3604_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2024.104102","volume":"124","author":"B Fatimah","year":"2024","unstructured":"Fatimah B, Singhal A, Singh P. ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning. Med Eng Phys. 2024;124: 104102.","journal-title":"Med Eng Phys"},{"issue":"3","key":"3604_CR28","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.32604\/cmc.2023.039936","volume":"79","author":"M Aboghazalah","year":"2024","unstructured":"Aboghazalah M, El-kafrawy P, Ahmed AM, Elnemr R, Bouallegue B, El-sayed A. Arrhythmia detection by using chaos theory with machine learning algorithms. Comput Mater Continua. 2024;79(3):3855\u201375.","journal-title":"Comput Mater Continua"},{"issue":"2","key":"3604_CR29","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1016\/j.eswa.2007.12.065","volume":"36","author":"H Ocak","year":"2009","unstructured":"Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl. 2009;36(2):2027\u201336.","journal-title":"Expert Syst Appl"},{"issue":"12","key":"3604_CR30","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.3390\/electronics11121889","volume":"11","author":"Y Xing","year":"2022","unstructured":"Xing Y, et al. Accurate ECG classification based on spiking neural network and attentional mechanism for real-time implementation on personal portable devices. Electronics. 2022;11(12):1889.","journal-title":"Electronics"},{"issue":"3","key":"3604_CR31","doi-asserted-by":"publisher","first-page":"904","DOI":"10.3390\/s22030904","volume":"22","author":"K Pa\u0142czynski","year":"2022","unstructured":"Pa\u0142czynski K, Smigiel S, Ledzinski D, Bujnowski S. Study of the few-shot learning for ECG classification based on the PTB-XL dataset. Sensors. 2022;22(3):904.","journal-title":"Sensors"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03604-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03604-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03604-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T14:03:54Z","timestamp":1736258634000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03604-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["3604"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03604-8","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"4 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}},{"value":"This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"70"}}