{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:16:31Z","timestamp":1782962191645,"version":"3.54.5"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"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":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11517-023-02827-w","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T05:02:53Z","timestamp":1682571773000},"page":"2417-2439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Design of ear-contactless stethoscope and improvement in the performance of deep learning based on CNN to classify the heart sound"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4790-5387","authenticated-orcid":false,"given":"Tanmay Sinha","family":"Roy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyanta Kumar","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nirupama","family":"Mandal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"2827_CR1","doi-asserted-by":"publisher","unstructured":"CN Gupta, R Palaniappan, S Rajan, S Swaminathan, SM Krishnan (2005) \u201cSegmentation and classification of heart sounds\u201d. International Conference: Canadian Conference on Electrical and Computer Engineering. https:\/\/doi.org\/10.1109\/CCECE.2005.1557305","DOI":"10.1109\/CCECE.2005.1557305"},{"issue":"7","key":"2827_CR2","first-page":"76","volume":"3","author":"SK Anju","year":"2014","unstructured":"Anju SK et al (2014) Detection of cardiac murmur. Int J Comput Sci Mob Comput 3(7):76\u201380","journal-title":"Int J Comput Sci Mob Comput"},{"key":"2827_CR3","first-page":"609","volume":"2","author":"T Ahmad","year":"2009","unstructured":"Ahmad T, Ali H, Khan S (2009) Classification of phonocardiogram using an adaptive fuzzy inference system. Comput Sci 2:609\u2013614","journal-title":"Comput Sci"},{"issue":"7","key":"2827_CR4","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TIM.2014.2383071","volume":"64","author":"S Barma","year":"2015","unstructured":"Barma S, Chen B-W, Ji W, Jiang F, Wang J-F (2015) Measurement of duration, the energy of instantaneous-frequencies, and splits of subcomponents of the second heart sound. IEEE Trans Instrum Meas 64(7):1958\u20131967. https:\/\/doi.org\/10.1109\/TIM.2014.2383071","journal-title":"IEEE Trans Instrum Meas"},{"key":"2827_CR5","doi-asserted-by":"crossref","unstructured":"Mandeep Singh, Amandeep Cheema, \u201cHeart sounds classification using feature extraction of phonocardiography signal,\u201d International Journal of Computer Applications, Volume 77\u2013 No.4, September 2013, ISSN NO:0975 \u2013 8887","DOI":"10.5120\/13381-1001"},{"key":"2827_CR6","unstructured":"Ajay Kumar Roy, Abhishek Misal, G. R. Sinha, \u201cClassification of PCG signals: a survey,\u201d International Journal of Computer Applications, Recent Advances in Information Technology, 2014, ISSN No: 0975 \u2013 8887"},{"issue":"22","key":"2827_CR7","doi-asserted-by":"publisher","first-page":"9393","DOI":"10.1109\/JSEN.2018.2870759","volume":"18","author":"S Latif","year":"2018","unstructured":"Latif S, Usman M, Rana R, Qadir J (2018) Phonocardiographic sensing using deep learning for abnormal heartbeat detection. Sens J IEEE 18(22):9393\u20139400","journal-title":"Sens J IEEE"},{"key":"2827_CR8","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.18517\/ijaseit.8.5.7192","volume":"8","author":"NK Dewangan","year":"2018","unstructured":"Dewangan NK, Shukla SP, Dewangan K (2018) PCG signal analysis using discrete wavelet transform. Int J Adv Manag, Technol, Eng Sci 8:2249\u20137455","journal-title":"Int J Adv Manag, Technol, Eng Sci"},{"issue":"4","key":"2827_CR9","first-page":"2319","volume":"2","author":"G Mishra","year":"2013","unstructured":"Mishra G, Biswal K, Mishra AK (2013) Denoising of heart sound signal using wavelet transform. Int J Res Eng Technol 2(4):2319\u20131163","journal-title":"Int J Res Eng Technol"},{"key":"2827_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.procs.2015.08.045","volume":"58","author":"SK Randhawa","year":"2015","unstructured":"Randhawa SK, Singh M (2015) \u201cClassification of heart sound signals using multimodal features\u201d Second International Symposium on Computer Vision and the internet, Elsevier. Procedeia Comput Sci 58:165\u2013171","journal-title":"Procedeia Comput Sci"},{"key":"2827_CR11","doi-asserted-by":"publisher","first-page":"3956","DOI":"10.3390\/app10113956","volume":"10","author":"F Li","year":"2020","unstructured":"Li F, Tang H, Shang S, Mathiak K, Cong F (2020) Classification of heart sounds using convolutional neural network. Appl Sci 10:3956. https:\/\/doi.org\/10.3390\/app10113956","journal-title":"Appl Sci"},{"issue":"9","key":"2827_CR12","first-page":"2393","volume":"12","author":"Matin Z Othman","year":"2017","unstructured":"Othman Matin Z, Khaleel Asmaa N (2017) Phono cardiogram signal analysis for murmur diagnosing using Shannon energy envelop and sequenced DWT decomposition. J Eng Sci Technol 12(9):2393\u20132402","journal-title":"J Eng Sci Technol"},{"key":"2827_CR13","doi-asserted-by":"publisher","unstructured":"JK Roy & TS Roy (2017) \u201cA simple technique for heart sound detection and real-time analysis\u201d Proceedings of ICST 2017 held at Macquarie University Sidney, Sensing Technology (ICST), 2017. Eleventh International Conference, 4\u20136. https:\/\/doi.org\/10.1109\/ICSensT.2017.8304502.","DOI":"10.1109\/ICSensT.2017.8304502"},{"key":"2827_CR14","doi-asserted-by":"crossref","unstructured":"JK Roy, TS Roy, N Mandal & OA Postolache (2018) \u201cA simple technique for heart sound detection and identification using Kalman filter in real-time analysis\u201d. International Symposium Sensing And Instrumentation IoT Era (ISSI), 1-8","DOI":"10.1109\/ISSI.2018.8538255"},{"issue":"8","key":"2827_CR15","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1109\/TNB.2017.2769671","volume":"16","author":"A Mdhaffar","year":"2017","unstructured":"Mdhaffar A, Bouassida Rodriguez I, Charfi K, Abid L, Freisleben B (2017) CEP4HFP: complex event processing for heart failure prediction. IEEE Trans NanoBiosci 16(8):708\u2013717. https:\/\/doi.org\/10.1109\/TNB.2017.2769671","journal-title":"IEEE Trans NanoBiosci"},{"key":"2827_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-018-0062-0","volume":"7","author":"D Femir","year":"2019","unstructured":"Femir D, S\u00b8eng\u00fcr A, Bajaj V, Polat K (2019) Towards the classification of Heart sounds based on convolutional deep neural network. Health Inf. Sci Syst 7:1\u20139","journal-title":"Health Inf. Sci Syst"},{"key":"2827_CR17","unstructured":"Your First Deep Learning Project in Python with Keras Step. https:\/\/machinelearningmastery.com\/tutorial-first-neural-network-python-keras\/"},{"key":"2827_CR18","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/JIOT.2019.2961132","volume":"7","author":"B Xiao","year":"2020","unstructured":"Xiao B, Xu Y, Bi X, Li W, Ma Z, Zhang J, Ma X (2020) Follow the sound of children\u2019s heart: a deep-learning-based computer-aided pediatric CHDs diagnosis system. IEEE Internet Things J 7:1994\u20132004","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"2827_CR19","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.artmed.2004.07.008","volume":"33","author":"SR Bhatikar","year":"2005","unstructured":"Bhatikar SR, DeGroff C, Mahajan RL (2005) A classifier based on the artificial neural network approach for cardiologic Auscultation in pediatrics. Artificial intelligence in medicine 33(3):251\u2013260","journal-title":"Artificial intelligence in medicine"},{"key":"2827_CR20","unstructured":"Kaggle heartbeat sounds. https:\/\/www.kaggle.com\/datasets\/kinguistics\/heartbeat-sounds"},{"key":"2827_CR21","doi-asserted-by":"publisher","unstructured":"Prasad GV, Kumar PR (2015) Analysis of various DWT methods for feature extracted PCG signals. Int J Eng Res Technol 4(04). https:\/\/doi.org\/10.17577\/IJERTV4IS041236","DOI":"10.17577\/IJERTV4IS041236"},{"key":"2827_CR22","doi-asserted-by":"publisher","first-page":"124417","DOI":"10.1109\/ACCESS.2019.2934827","volume":"7","author":"X Cheng","year":"2019","unstructured":"Cheng X, Huang J, Li Y, Gui G (2019) Design and application of a laconic heart sound neural network. IEEE Access 7:124417\u2013124425","journal-title":"IEEE Access"},{"key":"2827_CR23","doi-asserted-by":"crossref","unstructured":"Alafif T, Boulares M, Barnawi A, Alafif T, Althobaiti H, Alferaidi A (2020) Normal and abnormal heart rates recognition using transfer learning. In Proceedings of the 2020 12th International Conference on Knowledge and Systems Engineering (KSE), 2020 275\u2013280","DOI":"10.1109\/KSE50997.2020.9287514"},{"key":"2827_CR24","doi-asserted-by":"publisher","first-page":"055006","DOI":"10.1088\/1361-6579\/ab8770","volume":"41","author":"FA Khan","year":"2020","unstructured":"Khan FA, Abid A, Khan MS (2020) Automatic heart sound classification from segmented\/unsegmented phonocardiogram signals using time and frequency features. Physiol Meas 41:055006. https:\/\/doi.org\/10.1088\/1361-6579\/ab8770","journal-title":"Physiol Meas"},{"issue":"21","key":"2827_CR25","doi-asserted-by":"publisher","first-page":"4819","DOI":"10.3390\/s19214819","volume":"19","author":"A Raza","year":"2019","unstructured":"Raza A, Mehmood A, Ullah S, Ahmad M, Choi GS, On BW (2019) Heartbeat sound signal classification using deep learning. Sensors 19(21):4819. https:\/\/doi.org\/10.3390\/s19214819","journal-title":"Sensors"},{"key":"2827_CR26","doi-asserted-by":"crossref","unstructured":"Ryu H, Park J, Shin H (2016) Classification of heart sound recordings using convolution neural network. In Proceedings of the 2016 Computing in Cardiology Conference (CinC). 1153\u20131156","DOI":"10.22489\/CinC.2016.329-134"},{"key":"2827_CR27","unstructured":"DB Springer, L Tarassenko, and GD Clifford (2014) Support vector machine hidden semi Markov model-based heart sound segmentation. Comput Cardiol, 625\u2013628"},{"key":"2827_CR28","doi-asserted-by":"crossref","unstructured":"El-Segaier M,\u00a0Lilja O,\u00a0Lukkarinen S,\u00a0S\u00f6rnmo L,\u00a0Seppanen R,\u00a0Pesonen E (2005) Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng 33(7):937\u201342. http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16060534","DOI":"10.1007\/s10439-005-4053-3"},{"key":"2827_CR29","first-page":"1","volume":"9","author":"Z Abduh","year":"2019","unstructured":"Abduh Z, Nehary EA, Wahed MA, Kadah YM (2019) Classification of Heart sounds using fractional fourier transform based mel-frequency spectral coefficients and traditional classifiers. Biomed Signal Process Control 9:1\u20138","journal-title":"Biomed Signal Process Control"},{"key":"2827_CR30","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neunet.2020.06.015","volume":"130","author":"M Deng","year":"2020","unstructured":"Deng M, Meng T, Cao J, Wang S, Zhang J, Fan H (2020) Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw 130:22\u201332","journal-title":"Neural Netw"},{"key":"2827_CR31","doi-asserted-by":"crossref","unstructured":"Rubin J, Abreu R, Ganguli A, Nelaturi S, Matei I, Sricharan K (2016) Classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral coefficients. In Proceedings of the 2016 Computing in Cardiology Conference (CinC), 813\u2013816","DOI":"10.22489\/CinC.2016.236-175"},{"key":"2827_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/4205027","volume":"2018","author":"H Tang","year":"2018","unstructured":"Tang H, Dai Z, Jiang Y, Li T, Liu C (2018) PCG classification using multidomain features and SVM classifier. BioMed Res Int 2018:1\u201314. https:\/\/doi.org\/10.1155\/2018\/4205027","journal-title":"BioMed Res Int"},{"issue":"12","key":"2827_CR33","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1088\/0967-3334\/37\/12\/2181","volume":"37","author":"C Liu","year":"2016","unstructured":"Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AE, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD (2016) An open-access database for the evaluation of heart sound algorithms. Physiol Meas 37(12):2181\u20132213. https:\/\/doi.org\/10.1088\/0967-3334\/37\/12\/2181","journal-title":"Physiol Meas"},{"issue":"4","key":"2827_CR34","first-page":"454","volume":"2","author":"H Nygaard","year":"1993","unstructured":"Nygaard H et al (1993) Assessing the severity of aortic valve stenosis by spectral analysis of cardiac murmurs (spectral vibrocardiography) Part I: Technical aspects. J Heart Valve Dis. 2(4):454\u2013467","journal-title":"J Heart Valve Dis."},{"key":"2827_CR35","doi-asserted-by":"crossref","unstructured":"Gomes EF, Bentley PJ, Coimbra M, Pereira E, Deng Y (2013) Classifying heart sounds: approaches to the PASCAL challenge. In Proceedings of the HEALTHINF 2013- Proceedings of the International Conference on Health Informatics, Barcelona, Spain, pp 337\u2013340","DOI":"10.5220\/0004234403370340"},{"key":"2827_CR36","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1088\/1361-6579\/aa7841","volume":"38","author":"V Maknickas","year":"2017","unstructured":"Maknickas V, Maknickas A (2017) Recognition of normal, abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiol Meas 38:1671\u20131684","journal-title":"Physiol Meas"},{"key":"2827_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-019-0651-3","volume":"59","author":"F Li","year":"2019","unstructured":"Li F, Liu M, Zhao Y, Kong L, Dong L, Liu X, Hui M (2019) Feature extraction and classification of heart sound using 1D convolutional neural networks. EURASIP J Adv Signal Process 59:1\u201311. https:\/\/doi.org\/10.1186\/s13634-019-0651-3","journal-title":"EURASIP J Adv Signal Process"},{"issue":"4","key":"2827_CR38","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/TNB.2018.2870331","volume":"17","author":"MA Quiroz-Ju\u00e1rez","year":"2018","unstructured":"Quiroz-Ju\u00e1rez MA, Jim\u00e9nez-Ram\u00edrez O, V\u00e1zquez-Medina R, Ryzhii E, Ryzhii M, Arag\u00f3n JL (2018) Cardiac conduction model for generating 12 lead ECG signals with realistic heart rate dynamics. IEEE Trans Nanobiosci 17(4):525\u2013532. https:\/\/doi.org\/10.1109\/TNB.2018.2870331","journal-title":"IEEE Trans Nanobiosci"},{"key":"2827_CR39","doi-asserted-by":"crossref","unstructured":"Yang TC, Hsieh H (2016) Classification of acoustic physiological signals based on deep learning neural networks with augmented features. In Proceedings of the 2016 Computing in Cardiology Conference (CinC), 569\u2013572","DOI":"10.22489\/CinC.2016.163-228"},{"issue":"1","key":"2827_CR40","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1088\/1742-6596\/34\/1\/181","volume":"34","author":"F Javed","year":"2006","unstructured":"Javed F, Venkatachalam PA, Ahmad Fadzil MH (2006) A signal processing module for the analysis of heart sounds and heart murmurs. J Phys: Conf Ser 34(1):1098\u20131105. https:\/\/doi.org\/10.1088\/1742-6596\/34\/1\/181","journal-title":"J Phys: Conf Ser"},{"key":"2827_CR41","doi-asserted-by":"publisher","unstructured":"Lubaib P, Ahammed Muneer KV (2016) The heart defect analysis based on PCG signals using pattern recognition techniques. Procedia Technol 24:1024\u20131031.\nhttps:\/\/doi.org\/10.1016\/j.protcy.2016.05.225","DOI":"10.1016\/j.protcy.2016.05.225"},{"key":"2827_CR42","unstructured":"Muruganantham. Methods for classification of phonocardiogram. TENCON, (2003)"},{"key":"2827_CR43","doi-asserted-by":"publisher","unstructured":"Wu JB, Zhou S, Wu Z, Wu XM (2012) Research on the method of characteristic extraction and classification of Phonocardiogram. In:\u00a02012 International Conference on Systems and Informatics. ICSAI 2012. https:\/\/doi.org\/10.1109\/ICSAI.2012.6223377","DOI":"10.1109\/ICSAI.2012.6223377"},{"issue":"7","key":"2827_CR44","first-page":"2921","volume":"4","author":"A Cheema","year":"2013","unstructured":"Cheema A, Singh M (2013) Steps involved in heart sound analysis- a review of existing trends. Int J Eng Trends Technol 4(7):2921\u20132925","journal-title":"Int J Eng Trends Technol"},{"key":"2827_CR45","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.asoc.2019.01.019","volume":"78","author":"JM-T Wu","year":"2019","unstructured":"Wu JM-T, Tsai M-H, Huang YZ, Islam SH, Hassan MM, Alelaiwi A, Fortino G (2019) Applying an ensemble convolutional neural network with Savitzky-Golay filter to construct a phonocardiogram prediction model. Appl Soft Comput 78:29\u201340","journal-title":"Appl Soft Comput"},{"key":"2827_CR46","doi-asserted-by":"publisher","unstructured":"JK Roy, TS Roy, SC Mukhopadhyay (2019) \u201cHeart sound: detection and analytical approach towards diseases\u201d. Modern Sensing Technologies, 103\u2013145. https:\/\/doi.org\/10.1007\/978-3-319-99540-3_7","DOI":"10.1007\/978-3-319-99540-3_7"},{"issue":"3","key":"2827_CR47","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1109\/TNB.2018.2837622","volume":"17","author":"Q Suo","year":"2018","unstructured":"Suo Q et al (2018) Deep patient similarity learning for personalized healthcare. IEEE Trans Nanobiosci 17(3):219\u2013227. https:\/\/doi.org\/10.1109\/TNB.2018.2837622","journal-title":"IEEE Trans Nanobiosci"},{"key":"2827_CR48","doi-asserted-by":"crossref","unstructured":"S Romiti, M Vinciguerra, W Saade, I Ansocortajarena and E Cresco (2020) \u201cArtificial intelligence and cardiovascular diseases: an unexpected alliance\u201d. Cardiol Res Pract, 2020","DOI":"10.1155\/2020\/4972346"},{"issue":"3","key":"2827_CR49","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TNB.2018.2841053","volume":"17","author":"D Li","year":"2018","unstructured":"Li D, Huang M, Li X, Ruan Y, Yao L (2018) MfeCNN: mixture feature embedding convolutional neural network for data mapping. IEEE Trans Nanobiosci 17(3):165\u2013171. https:\/\/doi.org\/10.1109\/TNB.2018.2841053","journal-title":"IEEE Trans Nanobiosci"},{"key":"2827_CR50","doi-asserted-by":"publisher","first-page":"2344","DOI":"10.3390\/app8122344","volume":"8","author":"G-Y Yaseen","year":"2018","unstructured":"Yaseen G-Y, Kwon S (2018) Classification of heart sound signal using multiple features. Appl Sci 8:2344. https:\/\/doi.org\/10.3390\/app8122344","journal-title":"Appl Sci"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02827-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02827-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02827-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:53:38Z","timestamp":1744214018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02827-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["2827"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02827-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,27]]},"assertion":[{"value":"7 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2023","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}