{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:24:13Z","timestamp":1774369453835,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"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":["Multidim Syst Sign Process"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11045-023-00875-x","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T17:03:08Z","timestamp":1679418188000},"page":"503-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An improved cardiac arrhythmia classification using stationary wavelet transform decomposed short duration QRS segment and Bi-LSTM network"],"prefix":"10.1007","volume":"34","author":[{"given":"Lakhan Dev","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4890-1898","authenticated-orcid":false,"given":"Jagdeep","family":"Rahul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apeksha","family":"Aggarwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijay Kumar","family":"Bohat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"issue":"2","key":"875_CR1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.bbe.2018.03.001","volume":"38","author":"UR Acharya","year":"2018","unstructured":"Acharya, U. R., Hagiwara, Y., Koh, J. E. W., Oh, S. L., Tan, J. H., Adam, M., & San Tan, R. (2018). Entropies for automated detection of coronary artery disease using ECG signals: A review. Biocybernetics and Biomedical Engineering, 38(2), 373\u2013384.","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"875_CR2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104164","volume":"130","author":"A Albaba","year":"2021","unstructured":"Albaba, A., Sim\u00f5es-Capela, N., Wang, Y., Hendriks, R. C., De Raedt, W., & Van Hoof, C. (2021). Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation. Computers in Biology and Medicine, 130, 104164.","journal-title":"Computers in Biology and Medicine"},{"issue":"5","key":"875_CR3","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1088\/0967-3334\/29\/5\/003","volume":"29","author":"M Arif","year":"2008","unstructured":"Arif, M., et al. (2008). Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiological Measurement, 29(5), 555.","journal-title":"Physiological Measurement"},{"issue":"2","key":"875_CR4","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.bbe.2020.02.004","volume":"40","author":"A Asgharzadeh-Bonab","year":"2020","unstructured":"Asgharzadeh-Bonab, A., Amirani, M. C., & Mehri, A. (2020). Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybernetics and Biomedical Engineering, 40(2), 691\u2013700.","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"875_CR5","volume-title":"ECG interpretation made incredibly easy!","author":"JS Coviello","year":"2020","unstructured":"Coviello, J. S. (2020). ECG interpretation made incredibly easy! Philadelphia: Lippincott Williams & Wilkins."},{"issue":"10","key":"875_CR6","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1109\/TSP.2014.2312316","volume":"62","author":"S Edla","year":"2014","unstructured":"Edla, S., Kovvali, N., & Papandreou-Suppappola, A. (2014). Electrocardiogram signal modeling with adaptive parameter estimation using sequential Bayesian methods. IEEE Transactions on Signal Processing, 62(10), 2667\u20132680.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"875_CR7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.cmpb.2015.12.024","volume":"127","author":"FA Elhaj","year":"2016","unstructured":"Elhaj, F. A., Salim, N., Harris, A. R., Swee, T. T., & Ahmed, T. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52\u201363.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"15","key":"875_CR8","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1016\/j.patrec.2004.06.014","volume":"25","author":"M Engin","year":"2004","unstructured":"Engin, M. (2004). ECG beat classification using neuro-fuzzy network. Pattern Recognition Letters, 25(15), 1715\u20131722.","journal-title":"Pattern Recognition Letters"},{"issue":"3","key":"875_CR9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.11591\/APTIKOM.J.CSIT.120","volume":"1","author":"MK Gautam","year":"2016","unstructured":"Gautam, M. K., & Giri, V. K. (2016). An approach of neural network for electrocardiogram classification. APTIKOM Journal on Computer Science and Information Technologies, 1(3), 119\u2013127.","journal-title":"APTIKOM Journal on Computer Science and Information Technologies"},{"key":"875_CR10","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1109\/JBHI.2018.2878492","volume":"23","author":"G Goovaerts","year":"2018","unstructured":"Goovaerts, G., Padhy, S., Vandenberk, B., Varon, C., Willems, R., & Van Huffel, S. (2018). A machine learning approach for detection and quantification of QRS fragmentation. IEEE Journal of Biomedical and Health Informatics, 23, 1980\u20131989.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"6","key":"875_CR11","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1049\/htl.2015.0029","volume":"2","author":"P Gupta","year":"2015","unstructured":"Gupta, P., Sharma, K. K., & Joshi, S. D. (2015). Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. Healthcare Technology Letters, 2(6), 164\u2013166.","journal-title":"Healthcare Technology Letters"},{"key":"875_CR12","doi-asserted-by":"crossref","first-page":"93275","DOI":"10.1109\/ACCESS.2019.2927726","volume":"7","author":"A Habib","year":"2019","unstructured":"Habib, A., Karmakar, C., & Yearwood, J. (2019). Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex. IEEE Access, 7, 93275\u201393285.","journal-title":"IEEE Access"},{"key":"875_CR13","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.bspc.2017.09.032","volume":"40","author":"S Hamdi","year":"2018","unstructured":"Hamdi, S., Abdallah, A. B., & Bedoui, M. H. (2018). A robust QRS complex detection using regular grammar and deterministic automata. Biomedical Signal Processing and Control, 40, 263\u2013274.","journal-title":"Biomedical Signal Processing and Control"},{"key":"875_CR14","doi-asserted-by":"crossref","unstructured":"Henzel, N. (2017). QRS complex detection based on ensemble empirical mode decomposition. In: Innovations in biomedical engineering (pp. 286\u2013293). Springer.","DOI":"10.1007\/978-3-319-47154-9_33"},{"key":"875_CR15","doi-asserted-by":"crossref","first-page":"128869","DOI":"10.1109\/ACCESS.2019.2939943","volume":"7","author":"MB Hossain","year":"2019","unstructured":"Hossain, M. B., Bashar, S. K., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). An accurate QRS complex and P wave detection in ECG signals using complete ensemble empirical mode decomposition with adaptive noise approach. IEEE Access, 7, 128869\u2013128880.","journal-title":"IEEE Access"},{"issue":"9","key":"875_CR16","doi-asserted-by":"crossref","first-page":"3694","DOI":"10.1109\/JSEN.2018.2812792","volume":"18","author":"Z Hou","year":"2018","unstructured":"Hou, Z., Dong, Y., Xiang, J., Li, X., & Yang, B. (2018). A real-time QRS detection method based on phase portraits and box-scoring calculation. IEEE Sensors Journal, 18(9), 3694\u20133702.","journal-title":"IEEE Sensors Journal"},{"key":"875_CR17","doi-asserted-by":"crossref","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. (2019). ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access, 7, 92871\u201392880.","journal-title":"IEEE Access"},{"issue":"3","key":"875_CR18","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.irbm.2017.04.002","volume":"38","author":"W-H Jung","year":"2017","unstructured":"Jung, W.-H., & Lee, S.-G. (2017). An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM, 38(3), 138\u2013148.","journal-title":"IRBM"},{"issue":"5","key":"875_CR19","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/S1350-4533(97)00013-1","volume":"19","author":"L Keselbrener","year":"1997","unstructured":"Keselbrener, L., Keselbrener, M., & Akselrod, S. (1997). Nonlinear high pass filter for R-wave detection in ECG signal. Medical Engineering & Physics, 19(5), 481\u2013484.","journal-title":"Medical Engineering & Physics"},{"issue":"1","key":"875_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-8-1","volume":"8","author":"J Kim","year":"2009","unstructured":"Kim, J., Shin, H. S., Shin, K., & Lee, M. (2009). Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomedical Engineering Online, 8(1), 1\u201312.","journal-title":"Biomedical Engineering Online"},{"issue":"12","key":"875_CR21","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1016\/j.eswa.2010.04.087","volume":"37","author":"M Kor\u00fcrek","year":"2010","unstructured":"Kor\u00fcrek, M., & Do\u011fan, B. (2010). ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Systems with Applications, 37(12), 7563\u20137569.","journal-title":"Expert Systems with Applications"},{"key":"875_CR22","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) organization (pp. 1\u20134). IEEE.","DOI":"10.22489\/CinC.2017.168-168"},{"issue":"1","key":"875_CR23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s13534-018-0087-y","volume":"9","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Ranganatham, R., Komaragiri, R., & Kumar, M. (2019). Efficient QRS complex detection algorithm based on Fast Fourier Transform. Biomedical Engineering Letters, 9(1), 145\u2013151.","journal-title":"Biomedical Engineering Letters"},{"issue":"8","key":"875_CR24","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1007\/s11517-019-01990-3","volume":"57","author":"CA Ledezma","year":"2019","unstructured":"Ledezma, C. A., & Altuve, M. (2019). Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings. Medical & Biological Engineering & Computing, 57(8), 1673\u20131681.","journal-title":"Medical & Biological Engineering & Computing"},{"key":"875_CR25","volume":"112","author":"JM Lee","year":"2021","unstructured":"Lee, J. M., & Hauskrecht, M. (2021). Modeling multivariate clinical event time-series with recurrent temporal mechanisms. Artificial Intelligence in Medicine, 112, 102021.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eswa.2019.05.033","volume":"134","author":"JS Lee","year":"2019","unstructured":"Lee, J. S., Lee, S. J., Choi, M., Seo, M., & Kim, S. W. (2019). QRS detection method based on fully convolutional networks for capacitive electrocardiogram. Expert Systems with Applications, 134, 66\u201378.","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"875_CR27","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s12872-018-0815-3","volume":"18","author":"W Lesyuk","year":"2018","unstructured":"Lesyuk, W., Kriza, C., & Kolominsky-Rabas, P. (2018). Cost-of-illness studies in heart failure: A systematic review 2004\u20132016. BMC Cardiovascular Disorders, 18(1), 74.","journal-title":"BMC Cardiovascular Disorders"},{"key":"875_CR28","volume":"103","author":"OS Lih","year":"2020","unstructured":"Lih, O. S., Jahmunah, V., San, T. R., Ciaccio, E. J., Yamakawa, T., Tanabe, M., Kobayashi, M., Faust, O., & Acharya, U. R. (2020). Comprehensive electrocardiographic diagnosis based on deep learning. Artificial Intelligence in Medicine, 103, 101789.","journal-title":"Artificial Intelligence in Medicine"},{"issue":"8","key":"875_CR29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3390\/e18080285","volume":"18","author":"T Li","year":"2016","unstructured":"Li, T., & Zhou, M. (2016). ECG classification using wavelet packet entropy and random forests. Entropy, 18(8), 285.","journal-title":"Entropy"},{"key":"875_CR30","volume":"156","author":"JP Madeiro","year":"2020","unstructured":"Madeiro, J. P., Marques, J. A. L., Han, T., & Pedrosa, R. C. (2020). Evaluation of mathematical models for QRS feature extraction and QRS morphology classification in ECG signals. Measurement, 156, 107580.","journal-title":"Measurement"},{"issue":"04","key":"875_CR31","doi-asserted-by":"crossref","first-page":"1350014","DOI":"10.1142\/S0129065713500147","volume":"23","author":"RJ Martis","year":"2013","unstructured":"Martis, R. J., Acharya, U. R., Lim, C. M., Mandana, K., Ray, A. K., & Chakraborty, C. (2013). Application of higher order cumulant features for cardiac health diagnosis using ECG signals. International Journal of Neural Systems, 23(04), 1350014.","journal-title":"International Journal of Neural Systems"},{"key":"875_CR32","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102098","volume":"118","author":"T Mayer","year":"2021","unstructured":"Mayer, T., Marro, S., Cabrio, E., & Villata, S. (2021). Enhancing evidence-based medicine with natural language argumentative analysis of clinical trials. Artificial Intelligence in Medicine, 118, 102098.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR33","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.ins.2014.04.003","volume":"279","author":"P Melin","year":"2014","unstructured":"Melin, P., Amezcua, J., Valdez, F., & Castillo, O. (2014). A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Information Sciences, 279, 483\u2013497.","journal-title":"Information Sciences"},{"issue":"6","key":"875_CR34","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.medengphy.2015.03.019","volume":"37","author":"M Merino","year":"2015","unstructured":"Merino, M., G\u00f3mez, I. M., & Molina, A. J. (2015). Envelopment filter and K-means for the detection of QRS waveforms in electrocardiogram. Medical Engineering & Physics, 37(6), 605\u2013609.","journal-title":"Medical Engineering & Physics"},{"key":"875_CR35","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.bspc.2016.07.012","volume":"31","author":"S Mihandoost","year":"2017","unstructured":"Mihandoost, S., & Amirani, M. C. (2017). Cyclic spectral analysis of electrocardiogram signals based on GARCH model. Biomedical Signal Processing and Control, 31, 79\u201388.","journal-title":"Biomedical Signal Processing and Control"},{"issue":"3","key":"875_CR36","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. Engineering in Medicine and Biology Magazine, IEEE, 20(3), 45\u201350.","journal-title":"Engineering in Medicine and Biology Magazine, IEEE"},{"key":"875_CR37","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TBCAS.2019.2916676","volume":"13","author":"C Nayak","year":"2019","unstructured":"Nayak, C., Saha, S. K., Kar, R., & Mandal, D. (2019). An Efficient and Robust Digital Fractional Order Differentiator Based ECG Pre-processor Design for QRS Detection. IEEE Transactions on Biomedical Circuits and Systems, 13, 682\u2013696.","journal-title":"IEEE Transactions on Biomedical Circuits and Systems"},{"key":"875_CR38","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","volume":"102","author":"SL Oh","year":"2018","unstructured":"Oh, S. L., Ng, E. Y., San Tan, R., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in Biology and Medicine, 102, 278\u2013287.","journal-title":"Computers in Biology and Medicine"},{"key":"875_CR39","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101898","volume":"107","author":"OJ Pellicer-Valero","year":"2020","unstructured":"Pellicer-Valero, O. J., Cattinelli, I., Neri, L., Mari, F., Mart\u00edn-Guerrero, J. D., & Barbieri, C. (2020). Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artificial Intelligence in Medicine, 107, 101898.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR40","doi-asserted-by":"crossref","unstructured":"Rahul, J., & Sharma, L. D. (2022a). Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and bi-LSTM model. Biocybernetics and Biomedical Engineering,42(1), 312\u2013324.","DOI":"10.1016\/j.bbe.2022.02.006"},{"key":"875_CR41","doi-asserted-by":"crossref","unstructured":"Rahul, J., & Sharma, L. D. (2022b). Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG. Biomedical Signal Processing and Control,71, 103270.","DOI":"10.1016\/j.bspc.2021.103270"},{"key":"875_CR42","doi-asserted-by":"crossref","unstructured":"Rahul, J., & Sora, M. (2020). A novel adaptive window based technique for T wave detection and delineation in the ECG. Bio-Algorithms and Med-Systems, 16(1), 20190064.","DOI":"10.1515\/bams-2019-0064"},{"key":"875_CR43","doi-asserted-by":"crossref","unstructured":"Rahul, J., Sharma, L. D., & Bohat, V. K. (2021d). Short duration vector cardiogram based inferior myocardial infarction detection: Class and subject-oriented approach. Biomedical Engineering\/Biomedizinische Technik,66(5), 489\u2013501.","DOI":"10.1515\/bmt-2020-0329"},{"key":"875_CR44","doi-asserted-by":"crossref","unstructured":"Rahul, J., Sora, M., & Sharma, L. D. (2021a). Dynamic thresholding based efficient QRS complex detection with low computational overhead. Biomedical Signal Processing and Control,67, 102519.","DOI":"10.1016\/j.bspc.2021.102519"},{"key":"875_CR45","doi-asserted-by":"crossref","unstructured":"Rahul, J., Sora, M., & Sharma, L. D. (2021b). A novel and lightweight P, QRS, and T peaks detector using adaptive thresholding and template waveform. Computers in Biology and Medicine,132, 104307.","DOI":"10.1016\/j.compbiomed.2021.104307"},{"key":"875_CR46","doi-asserted-by":"publisher","unstructured":"Rahul, J., Sora, M., Sharma, L. D., & Bohat, V. K. (2021c). An improved cardiac arrhythmia classification using an RR interval-based approach. Biocybernetics and Biomedical Engineering. https:\/\/doi.org\/10.1016\/j.bbe.2021.04.004. ISSN 0208-5216.","DOI":"10.1016\/j.bbe.2021.04.004"},{"issue":"3","key":"875_CR47","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s13246-020-00906-y","volume":"43","author":"J Rahul","year":"2020","unstructured":"Rahul, J., Sora, M., & Sharma, L. D. (2020). Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load. Physical and Engineering Sciences in Medicine, 43(3), 1049\u20131067.","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"875_CR48","doi-asserted-by":"crossref","DOI":"10.1002\/9781119068129","volume-title":"Biomedical signal analysis","author":"RM Rangayyan","year":"2015","unstructured":"Rangayyan, R. M. (2015). Biomedical signal analysis. New York: Wiley."},{"key":"875_CR49","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.101788","volume":"103","author":"AK Sangaiah","year":"2020","unstructured":"Sangaiah, A. K., Arumugam, M., & Bian, G.-B. (2020). An intelligent learning approach for improving ECG signal classification and arrhythmia analysis. Artificial Intelligence in Medicine, 103, 101788.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR50","doi-asserted-by":"crossref","unstructured":"Sharma, L. D., & Sunkaria, R. K. (2018a). Stationary wavelet transform based technique for automated external defibrillator using optimally selected classifiers. Measurement,125, 29\u201336.","DOI":"10.1016\/j.measurement.2018.04.054"},{"key":"875_CR51","doi-asserted-by":"crossref","unstructured":"Sharma, L. D., & Sunkaria, R. K. (2018b). Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image and Video Processing,12(2), 199\u2013206.","DOI":"10.1007\/s11760-017-1146-z"},{"key":"875_CR52","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103753","volume":"120","author":"A Sharma","year":"2020","unstructured":"Sharma, A., Garg, N., Patidar, S., San Tan, R., & Acharya, U. R. (2020). Automated pre-screening of arrhythmia using hybrid combination of Fourier\u2013Bessel expansion and LSTM. Computers in Biology and Medicine, 120, 103753.","journal-title":"Computers in Biology and Medicine"},{"key":"875_CR53","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.compeleceng.2019.01.025","volume":"75","author":"A Sharma","year":"2019","unstructured":"Sharma, A., Patidar, S., Upadhyay, A., & Acharya, U. R. (2019). Accurate tunable-Q wavelet transform based method for QRS complex detection. Computers & Electrical Engineering, 75, 101\u2013111.","journal-title":"Computers & Electrical Engineering"},{"issue":"2","key":"875_CR54","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s13246-018-0640-0","volume":"41","author":"H Sharma","year":"2018","unstructured":"Sharma, H., & Sharma, K. (2018). ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. Australasian Physical & Engineering Sciences in Medicine, 41(2), 429\u2013443.","journal-title":"Australasian Physical & Engineering Sciences in Medicine"},{"issue":"6","key":"875_CR55","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1016\/j.jelectrocard.2015.08.034","volume":"48","author":"LG Tereshchenko","year":"2015","unstructured":"Tereshchenko, L. G., & Josephson, M. E. (2015). Frequency content and characteristics of ventricular conduction. Journal of Electrocardiology, 48(6), 933\u2013937.","journal-title":"Journal of Electrocardiology"},{"issue":"3","key":"875_CR56","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.artmed.2004.03.007","volume":"33","author":"MG Tsipouras","year":"2005","unstructured":"Tsipouras, M. G., Fotiadis, D. I., & Sideris, D. (2005). An arrhythmia classification system based on the RR-interval signal. Artificial Intelligence in Medicine, 33(3), 237\u2013250.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR57","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.artmed.2018.10.008","volume":"97","author":"T Van Steenkiste","year":"2019","unstructured":"Van Steenkiste, T., Ruyssinck, J., De Baets, L., Decruyenaere, J., De Turck, F., Ongenae, F., & Dhaene, T. (2019). Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. Artificial Intelligence in Medicine, 97, 38\u201343.","journal-title":"Artificial Intelligence in Medicine"},{"key":"875_CR58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.compbiomed.2016.08.013","volume":"77","author":"Z Wang","year":"2016","unstructured":"Wang, Z., Wan, F., Wong, C. M., & Zhang, L. (2016). Adaptive Fourier decomposition based ECG denoising. Computers in Biology and Medicine, 77, 195\u2013205.","journal-title":"Computers in Biology and Medicine"},{"key":"875_CR59","doi-asserted-by":"crossref","first-page":"47103","DOI":"10.1109\/ACCESS.2020.2979256","volume":"8","author":"H Yang","year":"2020","unstructured":"Yang, H., & Wei, Z. (2020). Arrhythmia recognition and classification using combined parametric and visual pattern features of ECG morphology. IEEE Access, 8, 47103\u201347117.","journal-title":"IEEE Access"},{"key":"875_CR60","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compbiomed.2018.03.016","volume":"96","author":"\u00d6 Yildirim","year":"2018","unstructured":"Yildirim, \u00d6. (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine, 96, 189\u2013202.","journal-title":"Computers in Biology and Medicine"},{"key":"875_CR61","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"Y\u0131ld\u0131r\u0131m, \u00d6., P\u0142awiak, P., Tan, R.-S., & Acharya, U. R. (2018). Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in Biology and Medicine, 102, 411\u2013420.","journal-title":"Computers in Biology and Medicine"},{"issue":"4","key":"875_CR62","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1016\/j.eswa.2007.05.006","volume":"34","author":"S-N Yu","year":"2008","unstructured":"Yu, S.-N., & Chou, K.-T. (2008). Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with Applications, 34(4), 2841\u20132846.","journal-title":"Expert Systems with Applications"},{"key":"875_CR63","doi-asserted-by":"crossref","first-page":"169359","DOI":"10.1109\/ACCESS.2019.2955738","volume":"7","author":"B Yuen","year":"2019","unstructured":"Yuen, B., Dong, X., & Lu, T. (2019). Inter-patient CNN-LSTM for QRS complex detection in noisy ECG signals. IEEE Access, 7, 169359\u2013169370.","journal-title":"IEEE Access"},{"key":"875_CR64","doi-asserted-by":"crossref","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. Artificial Intelligence in Medicine, 106, 101856.","journal-title":"Artificial Intelligence in Medicine"},{"issue":"4","key":"875_CR65","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/aab297","volume":"39","author":"W Zhong","year":"2018","unstructured":"Zhong, W., Liao, L., Guo, X., & Wang, G. (2018). A deep learning approach for fetal QRS complex detection. Physiological Measurement, 39(4), 045004.","journal-title":"Physiological Measurement"},{"key":"875_CR66","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.artmed.2017.06.004","volume":"79","author":"F-Y Zhou","year":"2017","unstructured":"Zhou, F.-Y., Jin, L.-P., & Dong, J. (2017). Premature ventricular contraction detection combining deep neural networks and rules inference. Artificial Intelligence in Medicine, 79, 42\u201351.","journal-title":"Artificial Intelligence in Medicine"}],"container-title":["Multidimensional Systems and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-023-00875-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11045-023-00875-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-023-00875-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:54:55Z","timestamp":1683899695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11045-023-00875-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":66,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["875"],"URL":"https:\/\/doi.org\/10.1007\/s11045-023-00875-x","relation":{},"ISSN":["0923-6082","1573-0824"],"issn-type":[{"value":"0923-6082","type":"print"},{"value":"1573-0824","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]},"assertion":[{"value":"13 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}