{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T01:54:39Z","timestamp":1769219679582,"version":"3.49.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T00:00:00Z","timestamp":1720828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T00:00:00Z","timestamp":1720828800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19740-5","type":"journal-article","created":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T04:01:40Z","timestamp":1720843300000},"page":"19191-19222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["eFuseNet: A deep ensemble fusion network for efficient detection of Arrhythmia and Myocardial Infarction using ECG signals"],"prefix":"10.1007","volume":"84","author":[{"given":"Amitesh Kumar","family":"Dwivedi","sequence":"first","affiliation":[]},{"given":"Gaurav","family":"Srivastava","sequence":"additional","affiliation":[]},{"given":"Sakshi","family":"Tripathi","sequence":"additional","affiliation":[]},{"given":"Nitesh","family":"Pradhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,13]]},"reference":[{"key":"19740_CR1","unstructured":"Cardiovascular diseases. World Health Organization"},{"key":"19740_CR2","doi-asserted-by":"crossref","unstructured":"Wacker-Gussmann A, Oberhoffer-Fritz R (2022) Cardiovascular risk factors in childhood and adolescence. MDPI","DOI":"10.3390\/jcm11041136"},{"issue":"10065","key":"19740_CR3","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/S0140-6736(16)30677-8","volume":"389","author":"GW Reed","year":"2017","unstructured":"Reed GW, Rossi JE, Cannon CP (2017) Acute myocardial infarction. The Lancet. 389(10065):197\u2013210","journal-title":"The Lancet."},{"key":"19740_CR4","first-page":"262","volume":"19","author":"D Cenitta","year":"2022","unstructured":"Cenitta D, Arjunan RV, Prema K (2022) Ischemic heart disease multiple imputation technique using machine learning algorithm. Engineered Science. 19:262\u2013272","journal-title":"Engineered Science."},{"key":"19740_CR5","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.yjmcc.2015.12.024","volume":"91","author":"SDF Stuart","year":"2016","unstructured":"Stuart SDF, De Jesus NM, Lindsey ML, Ripplinger CM (2016) The crossroads of inflammation, fibrosis, and arrhythmia following myocardial infarction. J Mol Cell Cardiol 91:114\u2013122","journal-title":"J Mol Cell Cardiol"},{"issue":"7","key":"19740_CR6","first-page":"1665","volume":"3","author":"FH Fenton","year":"2008","unstructured":"Fenton FH, Cherry EM, Glass L (2008) Cardiac arrhythmia. Scholarpedia. 3(7):1665","journal-title":"Cardiac arrhythmia. Scholarpedia."},{"issue":"3","key":"19740_CR7","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1093\/oxfordjournals.aje.a112608","volume":"108","author":"RS Paffenbarger Jr","year":"1978","unstructured":"Paffenbarger RS Jr, Wing AL, Hyde RT (1978) Physical activity as an index of heart attack risk in college alumni. Am J Epidemiol 108(3):161\u2013175","journal-title":"Am J Epidemiol"},{"key":"19740_CR8","doi-asserted-by":"crossref","unstructured":"Singh N, Singh P (2019) Cardiac arrhythmia classification using machine learning techniques. In: Engineering vibration, communication and information processing, pp 469\u2013480. Springer, ???","DOI":"10.1007\/978-981-13-1642-5_42"},{"issue":"4","key":"19740_CR9","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TBME.2019.2926104","volume":"67","author":"X Tang","year":"2019","unstructured":"Tang X, Ma Z, Hu Q, Tang W (2019) A real-time arrhythmia heartbeats classification algorithm using parallel delta modulations and rotated linear-kernel support vector machines. IEEE Trans Biomed Eng 67(4):978\u2013986","journal-title":"IEEE Trans Biomed Eng"},{"key":"19740_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3389\/fphy.2019.00103","volume":"7","author":"M Alfaras","year":"2019","unstructured":"Alfaras M, Soriano MC, Ort\u00edn S (2019) A fast machine learning model for ecg-based heartbeat classification and arrhythmia detection. Frontiers in Physics. 7:103","journal-title":"Frontiers in Physics."},{"issue":"1","key":"19740_CR11","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s40258-013-0070-9","volume":"12","author":"DH Tang","year":"2014","unstructured":"Tang DH, Gilligan AM, Romero K (2014) Economic burden and disparities in healthcare resource use among adult patients with cardiac arrhythmia. Appl Health Econ Health Policy 12(1):59\u201371","journal-title":"Appl Health Econ Health Policy"},{"key":"19740_CR12","unstructured":"Newman MEJ (2013) Network data. http:\/\/www-personal.umich.edu\/~mejn\/netdata\/"},{"key":"19740_CR13","doi-asserted-by":"crossref","unstructured":"Kropf M, Hayn D, Schreier G (2017) Ecg classification based on time and frequency domain features using random forests. In: 2017 Computing in cardiology (CinC), pp 1\u20134. IEEE","DOI":"10.22489\/CinC.2017.168-168"},{"key":"19740_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103910","volume":"78","author":"C Han","year":"2022","unstructured":"Han C, Wang P, Huang R, Cui L (2022) Hctnet: An experience-guided deep learning network for inter-patient arrhythmia classification on imbalanced dataset. Biomed Signal Process Control 78:103910","journal-title":"Biomed Signal Process Control"},{"key":"19740_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103943","volume":"78","author":"N Sinha","year":"2022","unstructured":"Sinha N, Tripathy RK, Das A (2022) Ecg beat classification based on discriminative multilevel feature analysis and deep learning approach. Biomed Signal Process Control 78:103943","journal-title":"Biomed Signal Process Control"},{"key":"19740_CR16","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","volume":"415","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf Sci 415:190\u2013198","journal-title":"Inf Sci"},{"key":"19740_CR17","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.inffus.2020.11.008","volume":"69","author":"Y Khalifa","year":"2021","unstructured":"Khalifa Y, Mandic D, Sejdi\u0107 E (2021) A review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals. Information Fusion. 69:52\u201372","journal-title":"Information Fusion."},{"key":"19740_CR18","doi-asserted-by":"publisher","first-page":"145395","DOI":"10.1109\/ACCESS.2019.2939947","volume":"7","author":"HM Lynn","year":"2019","unstructured":"Lynn HM, Pan SB, Kim P (2019) A deep bidirectional gru network model for biometric electrocardiogram classification based on recurrent neural networks. IEEE Access. 7:145395\u2013145405","journal-title":"IEEE Access."},{"key":"19740_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.cmpb.2019.05.004","volume":"176","author":"O Yildirim","year":"2019","unstructured":"Yildirim O, Baloglu UB, Tan R-S, Ciaccio EJ, Acharya UR (2019) A new approach for arrhythmia classification using deep coded features and lstm networks. Comput Methods Programs Biomed 176:121\u2013133","journal-title":"Comput Methods Programs Biomed"},{"key":"19740_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103408","volume":"73","author":"J-K Kim","year":"2022","unstructured":"Kim J-K, Jung S, Park J, Han SW (2022) Arrhythmia detection model using modified densenet for comprehensible grad-cam visualization. Biomed Signal Process Control 73:103408","journal-title":"Biomed Signal Process Control"},{"issue":"7553","key":"19740_CR21","first-page":"436","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature. 521(7553):436\u2013444","journal-title":"Deep learning. nature."},{"key":"19740_CR22","doi-asserted-by":"crossref","unstructured":"Li J, Pang S-p, Xu F, Zhou S, Shu M (2022) Two-dimensional ecg-based cardiac arrhythmia classification using dse-resnet","DOI":"10.21203\/rs.3.rs-1550001\/v1"},{"key":"19740_CR23","doi-asserted-by":"crossref","unstructured":"Srivastava G, Chauhan A, Jangid M, Chaurasia S (2022) Covixnet: A novel and efficient deep learning model for detection of covid-19 using chest x-ray images. Biomed Signal Process Control 103848","DOI":"10.1016\/j.bspc.2022.103848"},{"key":"19740_CR24","doi-asserted-by":"publisher","first-page":"105979","DOI":"10.1016\/j.compbiomed.2022.105979","volume":"149","author":"G Srivastava","year":"2022","unstructured":"Srivastava G, Pradhan N, Saini Y (2022) Ensemble of deep neural networks based on condorcet\u2019s jury theorem for screening covid-19 and pneumonia from radiograph images. Comput Biol Med 149:105979","journal-title":"Comput Biol Med"},{"key":"19740_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2021.100271","volume":"26","author":"A Pal","year":"2021","unstructured":"Pal A, Srivastva R, Singh YN (2021) Cardionet: An efficient ecg arrhythmia classification system using transfer learning. Big Data Research. 26:100271","journal-title":"Big Data Research."},{"key":"19740_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3033072","volume":"70","author":"M Hammad","year":"2020","unstructured":"Hammad M, Iliyasu AM, Subasi A, Ho ES, Abd El-Latif AA (2020) A multitier deep learning model for arrhythmia detection. IEEE Trans Instrum Meas 70:1\u20139","journal-title":"IEEE Trans Instrum Meas"},{"key":"19740_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101856","volume":"106","author":"J Zhang","year":"2020","unstructured":"Zhang J, Liu A, Gao M, Chen X, Zhang X, Chen X (2020) Ecg-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif Intell Med 106:101856","journal-title":"Artif Intell Med"},{"key":"19740_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105325","volume":"144","author":"R Hu","year":"2022","unstructured":"Hu R, Chen J, Zhou L (2022) A transformer-based deep neural network for arrhythmia detection using continuous ecg signals. Comput Biol Med 144:105325","journal-title":"Comput Biol Med"},{"key":"19740_CR29","doi-asserted-by":"crossref","unstructured":"Natarajan A, Chang Y, Mariani S, Rahman A, Boverman G, Vij S, Rubin J (2020) A wide and deep transformer neural network for 12-lead ecg classification. In: 2020 Computing in cardiology, pp 1\u20134 . IEEE","DOI":"10.22489\/CinC.2020.107"},{"issue":"1","key":"19740_CR30","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aaf34d","volume":"40","author":"N Strodthoff","year":"2019","unstructured":"Strodthoff N, Strodthoff C (2019) Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas 40(1):015001","journal-title":"Physiol Meas"},{"key":"19740_CR31","doi-asserted-by":"crossref","unstructured":"Wu J, Bao Y, Chan S-C, Wu H, Zhang L, Wei X-G (2016) Myocardial infarction detection and classification\u2014a new multi-scale deep feature learning approach. In: 2016 IEEE international conference on digital signal processing (DSP), pp 309\u2013313. IEEE","DOI":"10.1109\/ICDSP.2016.7868568"},{"key":"19740_CR32","doi-asserted-by":"crossref","unstructured":"Wang H, Zhao W, Jia D, Hu J, Li Z, Yan C, You T (2019) Myocardial infarction detection based on multi-lead ensemble neural network. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2614\u20132617. IEEE","DOI":"10.1109\/EMBC.2019.8856392"},{"key":"19740_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105959","volume":"202","author":"S Mousavi","year":"2021","unstructured":"Mousavi S, Afghah F, Khadem F, Acharya UR (2021) Ecg language processing (elp): A new technique to analyze ecg signals. Comput Methods Programs Biomed 202:105959","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"19740_CR34","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1109\/JBHI.2016.2631247","volume":"22","author":"T Teijeiro","year":"2016","unstructured":"Teijeiro T, F\u00e9lix P, Presedo J, Castro D (2016) Heartbeat classification using abstract features from the abductive interpretation of the ecg. IEEE J Biomed Health Inform 22(2):409\u2013420","journal-title":"IEEE J Biomed Health Inform"},{"key":"19740_CR35","doi-asserted-by":"crossref","unstructured":"Das MK, Ari S (2014) Ecg beats classification using mixture of features. Int Scholarly Res Notices 2014","DOI":"10.1155\/2014\/178436"},{"key":"19740_CR36","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.compbiomed.2018.08.003","volume":"101","author":"W Yang","year":"2018","unstructured":"Yang W, Si Y, Wang D, Guo B (2018) Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine. Comput Biol Med 101:22\u201332","journal-title":"Comput Biol Med"},{"key":"19740_CR37","doi-asserted-by":"crossref","unstructured":"Arif M, Malagore IA, Afsar FA (2010) Automatic detection and localization of myocardial infarction using back propagation neural networks. In: 2010 4th International conference on bioinformatics and biomedical engineering, pp 1\u20134. IEEE","DOI":"10.1109\/ICBBE.2010.5514664"},{"key":"19740_CR38","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.compbiomed.2014.08.010","volume":"61","author":"B Liu","year":"2015","unstructured":"Liu B, Liu J, Wang G, Huang K, Li F, Zheng Y, Luo Y, Zhou F (2015) A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput Biol Med 61:178\u2013184","journal-title":"Comput Biol Med"},{"key":"19740_CR39","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.bspc.2016.07.007","volume":"31","author":"S Padhy","year":"2017","unstructured":"Padhy S, Dandapat S (2017) Third-order tensor based analysis of multilead ecg for classification of myocardial infarction. Biomed Signal Process Control 31:71\u201378","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"19740_CR40","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/TIM.2013.2279001","volume":"63","author":"S Banerjee","year":"2013","unstructured":"Banerjee S, Mitra M (2013) Application of cross wavelet transform for ecg pattern analysis and classification. IEEE Trans Instrum Meas 63(2):326\u2013333","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"19740_CR41","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.irbm.2019.09.003","volume":"41","author":"L Sharma","year":"2020","unstructured":"Sharma L, Sunkaria R (2020) Myocardial infarction detection and localization using optimal features based lead specific approach. Irbm. 41(1):58\u201370","journal-title":"Irbm."},{"key":"19740_CR42","doi-asserted-by":"crossref","unstructured":"Ali G, Dastgir A, Iqbal MW, Anwar M, Faheem M (2023) A hybrid convolutional neural network model for automatic diabetic retinopathy classification from fundus images. IEEE J Transl Eng Health Med","DOI":"10.1109\/JTEHM.2023.3282104"},{"key":"19740_CR43","doi-asserted-by":"crossref","unstructured":"Ahmad I, Rashid J, Faheem M, Akram A, Khan NA, Amin Ru (2024) Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks. Healthcare Technology Letters","DOI":"10.1049\/htl2.12073"},{"issue":"4","key":"19740_CR44","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1049\/htl2.12049","volume":"10","author":"AA Alarood","year":"2023","unstructured":"Alarood AA, Faheem M, Al-Khasawneh MA, Alzahrani AI, Alshdadi AA (2023) Secure medical image transmission using deep neural network in e-health applications. Healthcare Technology Letters. 10(4):87\u201398","journal-title":"Healthcare Technology Letters."},{"key":"19740_CR45","doi-asserted-by":"crossref","unstructured":"Zeeshan\u00a0Aslam M, Raza B, Faheem M, Raza A (2024) Aml-net: Attention-based multi-scale lightweight model for brain tumour segmentation in internet of medical things. CAAI Trans Intell Technol","DOI":"10.1049\/cit2.12278"},{"issue":"5","key":"19740_CR46","doi-asserted-by":"publisher","first-page":"12783","DOI":"10.1111\/anec.12783","volume":"25","author":"Z-Q Zhan","year":"2020","unstructured":"Zhan Z-Q, Li Y, Han L-H, Nikus KC, Birnbaum Y, Baranchuk A (2020) The de winter ecg pattern: Distribution and morphology of st depression. Ann Noninvasive Electrocardiol 25(5):12783","journal-title":"Ann Noninvasive Electrocardiol"},{"key":"19740_CR47","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1590\/S0066-782X2010000100006","volume":"94","author":"R Teixeira","year":"2010","unstructured":"Teixeira R, Louren\u00e7o C, Ant\u00f3nio N, Monteiro S, Baptista R, Jorge E, Ferreira MJ, Monteiro P, Freitas M, Provid\u00eancia LA (2010) The importance of a normal ecg in non-st elevation acute coronary syndromes. Arq Bras Cardiol 94:25\u201333","journal-title":"Arq Bras Cardiol"},{"key":"19740_CR48","unstructured":"Kashou AH, Basit H, Malik A (2022) St segment. In: StatPearls [Internet]. StatPearls Publishing, ???"},{"issue":"3","key":"19740_CR49","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody GB, Mark RG (2001) The impact of the mit-bih arrhythmia database. IEEE Eng Med Biol Mag 20(3):45\u201350","journal-title":"IEEE Eng Med Biol Mag"},{"key":"19740_CR50","doi-asserted-by":"crossref","unstructured":"Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der ekg-signaldatenbank cardiodat der ptb \u00fcber das internet","DOI":"10.1515\/bmte.1994.39.s1.250"},{"key":"19740_CR51","doi-asserted-by":"crossref","unstructured":"Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International conference on engineering and technology (ICET), pp 1\u20136. Ieee","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"19740_CR52","doi-asserted-by":"crossref","unstructured":"Wang J, Li Z (2018) Research on face recognition based on cnn. In: IOP Conference Series: Earth and Environmental Science, vol 170, p 032110. IOP Publishing","DOI":"10.1088\/1755-1315\/170\/3\/032110"},{"key":"19740_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102262","volume":"64","author":"BM Mathunjwa","year":"2021","unstructured":"Mathunjwa BM, Lin Y-T, Lin C-H, Abbod MF, Shieh J-S (2021) Ecg arrhythmia classification by using a recurrence plot and convolutional neural network. Biomed Signal Process Control 64:102262","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"19740_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. Journal of big Data. 8(1):1\u201374","journal-title":"Journal of big Data."},{"key":"19740_CR55","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"19740_CR56","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., ???. https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf"},{"key":"19740_CR57","unstructured":"Saxena S (2021) Introduction to the architecture of alexnet. Analytics Vidhya"},{"key":"19740_CR58","unstructured":"Wei J (2019) Alexnet: The architecture that challenged cnns. Towards Data Science 3"},{"key":"19740_CR59","doi-asserted-by":"crossref","unstructured":"Sak H, Senior A, Beaufays F (2014) Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128","DOI":"10.21437\/Interspeech.2014-80"},{"key":"19740_CR60","doi-asserted-by":"crossref","unstructured":"Wang Y (2017) A new concept using lstm neural networks for dynamic system identification. In: 2017 American control conference (ACC), pp 5324\u20135329. IEEE","DOI":"10.23919\/ACC.2017.7963782"},{"key":"19740_CR61","doi-asserted-by":"publisher","unstructured":"Van\u00a0Houdt G, Mosquera C, N\u00e1poles G (2020) A review on the long short-term memory model. Artificial Intelligence Review 53. https:\/\/doi.org\/10.1007\/s10462-020-09838-1","DOI":"10.1007\/s10462-020-09838-1"},{"key":"19740_CR62","doi-asserted-by":"crossref","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (gru) neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS), pp 1597\u20131600. IEEE","DOI":"10.1109\/MWSCAS.2017.8053243"},{"issue":"6","key":"19740_CR63","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.jelectrocard.2010.07.007","volume":"43","author":"S Luo","year":"2010","unstructured":"Luo S, Johnston P (2010) A review of electrocardiogram filtering. Journal of Electrocardiology. 43(6):486\u2013496. https:\/\/doi.org\/10.1016\/j.jelectrocard.2010.07.007","journal-title":"Journal of Electrocardiology."},{"key":"19740_CR64","doi-asserted-by":"crossref","unstructured":"Qiu X, Liang S, Zhang Y (2020) Simultaneous ecg heartbeat segmentation and classification with feature fusion and long term context dependencies. In: Pacific-Asia conference on knowledge discovery and data mining, pp 371\u2013383. Springer","DOI":"10.1007\/978-3-030-47436-2_28"},{"key":"19740_CR65","unstructured":"DeepAI: Loss function. DeepAI (2019)"},{"key":"19740_CR66","unstructured":"Parmar R (2018) Common loss functions in machine learning. Towards Data Science"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19740-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19740-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19740-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T10:27:43Z","timestamp":1748082463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19740-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,13]]},"references-count":66,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19740"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19740-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,13]]},"assertion":[{"value":"20 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors do not have any possible conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}