{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T02:52:14Z","timestamp":1792464734657,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"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":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s13246-023-01293-w","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T08:01:38Z","timestamp":1688371298000},"page":"1259-1269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated myocardial infarction and angina detection using second derivative of photoplethysmography"],"prefix":"10.1007","volume":"46","author":[{"family":"Neha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6072-3305","authenticated-orcid":false,"given":"H. K.","family":"Sardana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Dahiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Dogra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Kanawade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Y. P.","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"1293_CR1","unstructured":"W. H. Organization. Cardiovascular diseases. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)"},{"key":"1293_CR2","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s12013-015-0553-4","volume":"72","author":"L Lu","year":"2015","unstructured":"Lu L, Liu M, Sun R, Zheng Y, Zhang P (2015) Myocardial infarction: symptoms and treatments. Cell Biochem Biophys 72:865\u2013867","journal-title":"Cell Biochem Biophys"},{"issue":"2","key":"1293_CR3","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1097\/00006199-200303000-00007","volume":"52","author":"HA DeVon","year":"2003","unstructured":"DeVon HA, Zerwic JJ (2003) The symptoms of unstable angina: do women and men differ? Nurs Res 52(2):108\u2013118","journal-title":"Nurs Res"},{"key":"1293_CR4","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.cmpb.2017.09.015","volume":"152","author":"P Kora","year":"2017","unstructured":"Kora P (2017) ECG based myocardial infarction detection using hybrid firefly algorithm. Comput Methods Programs Biomed 152:141\u2013148","journal-title":"Comput Methods Programs Biomed"},{"key":"1293_CR5","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"},{"issue":"6","key":"1293_CR6","doi-asserted-by":"publisher","first-page":"2993","DOI":"10.3390\/s23062993","volume":"23","author":"B-T Pham","year":"2023","unstructured":"Pham B-T, Le PT, Tai T-C, Hsu Y-C, Li Y-H, Wang J-C (2023) Electrocardiogram heartbeat classification for arrhythmias and myocardial infarction. Sensors 23(6):2993","journal-title":"Sensors"},{"key":"1293_CR7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1093\/ehjdh\/ztad015","volume":"4","author":"S Sengupta","year":"2023","unstructured":"Sengupta S et al (2023) A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction. Eur Heart J-Digit Health 4:145","journal-title":"Eur Heart J-Digit Health"},{"issue":"6","key":"1293_CR8","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/0002-9343(77)90646-5","volume":"62","author":"H Feigenbaum","year":"1977","unstructured":"Feigenbaum H (1977) Principles of echocardiography. Am J Med 62(6):805\u2013812","journal-title":"Am J Med"},{"issue":"1","key":"1293_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1038\/s41598-022-27211-w","volume":"13","author":"B Liu","year":"2023","unstructured":"Liu B et al (2023) A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection. Sci Rep 13(1):3","journal-title":"Sci Rep"},{"issue":"14","key":"1293_CR10","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.jacc.2017.01.046","volume":"69","author":"ME J\u00f8rgensen","year":"2017","unstructured":"J\u00f8rgensen ME et al (2017) Functional testing or coronary computed tomography angiography in patients with stable coronary artery disease. J Am Coll Cardiol 69(14):1761\u20131770","journal-title":"J Am Coll Cardiol"},{"issue":"1","key":"1293_CR11","doi-asserted-by":"publisher","first-page":"e002237","DOI":"10.1136\/openhrt-2022-002237","volume":"10","author":"T Mahendiran","year":"2023","unstructured":"Mahendiran T et al (2023) Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study. Open Heart 10(1):e002237","journal-title":"Open Heart"},{"key":"1293_CR12","first-page":"1","volume":"1","author":"H Sardana","year":"2022","unstructured":"Sardana H, Dogra N, Kanawade R (2022) Dynamic time warping based arrhythmia detection using photoplethysmography signals. Signal Image Video Process 1:1\u20139","journal-title":"Signal Image Video Process"},{"issue":"4","key":"1293_CR13","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1007\/s13246-021-01072-5","volume":"44","author":"H Sardana","year":"2021","unstructured":"Sardana H, Kanwade R, Tewary S (2021) Arrhythmia detection and classification using ECG and PPG techniques: a review. Phys Eng Sci Med 44(4):1027\u20131048","journal-title":"Phys Eng Sci Med"},{"key":"1293_CR14","doi-asserted-by":"crossref","unstructured":"R. Kanawade, S. Tewary, and H. Sardana, Photoplethysmography based arrhythmia detection and classification. In 2019 6th International conference on signal processing and integrated networks (SPIN), 2019, pp. 944\u2013948: IEEE","DOI":"10.1109\/SPIN.2019.8711737"},{"issue":"21","key":"1293_CR15","doi-asserted-by":"publisher","first-page":"633","DOI":"10.5897\/SRE2015.6322","volume":"10","author":"YK Qawqzeh","year":"2015","unstructured":"Qawqzeh YK, Uldis R, Alharbi M (2015) Photoplethysmogram second derivative review: analysis and applications. Sci Res Essays 10(21):633\u2013639","journal-title":"Sci Res Essays"},{"issue":"8","key":"1293_CR16","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1097\/01.hjh.0000239277.05068.87","volume":"24","author":"SC Millasseau","year":"2006","unstructured":"Millasseau SC, Ritter JM, Takazawa K, Chowienczyk PJ (2006) Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens 24(8):1449\u20131456","journal-title":"J Hypertens"},{"issue":"2","key":"1293_CR17","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.cmpb.2014.08.001","volume":"117","author":"M Elgendi","year":"2014","unstructured":"Elgendi M (2014) Detection of c, d, and e waves in the acceleration photoplethysmogram. Comput Methods Programs Biomed 117(2):125\u2013136","journal-title":"Comput Methods Programs Biomed"},{"issue":"25","key":"1293_CR18","first-page":"2230","volume":"7","author":"Y Qawqzeh","year":"2012","unstructured":"Qawqzeh Y, Reaz M, Ali M, Gan KB, Zulkifli S, Noraidatulakma A (2012) Assessment of atherosclerosis in erectile dysfunction subjects using second derivative of photoplethysmogram. Sci Res Essays 7(25):2230\u20132236","journal-title":"Sci Res Essays"},{"key":"1293_CR19","doi-asserted-by":"publisher","first-page":"104814","DOI":"10.1016\/j.bspc.2023.104814","volume":"25","author":"A Hernando","year":"2023","unstructured":"Hernando A, Pel\u00e1ez-Coca MD, Gil E (2023) Decomposing photoplethysmogram waveforms into systolic and diastolic waves, with application to hyperbaric environments. Biomed Signal Process Control 25:104814","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"1293_CR20","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3390\/bios11040120","volume":"11","author":"X Sun","year":"2021","unstructured":"Sun X, Zhou L, Chang S, Liu Z (2021) Using CNN and HHT to predict blood pressure level based on photoplethysmography and its derivatives. Biosensors 11(4):120","journal-title":"Biosensors"},{"issue":"4","key":"1293_CR21","doi-asserted-by":"publisher","first-page":"155","DOI":"10.3109\/03091902.2016.1153740","volume":"40","author":"N Mahri","year":"2016","unstructured":"Mahri N, Gan KB, Mohd MA, Ali MH (2016) Analysis of myocardial infarction signals using optical technique. J Med Eng Technol 40(4):155\u2013161","journal-title":"J Med Eng Technol"},{"issue":"4","key":"1293_CR22","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1080\/03091902.2017.1299229","volume":"41","author":"N Mahri","year":"2017","unstructured":"Mahri N, Gan KB, Meswari R, Jaafar MH, Mohd.Ali MA (2017) Utilization of second derivative photoplethysmographic features for myocardial infarction classification. J Med Eng Technol 41(4):298\u2013308","journal-title":"J Med Eng Technol"},{"issue":"6","key":"1293_CR23","doi-asserted-by":"publisher","first-page":"2881","DOI":"10.1109\/TIM.2019.2930438","volume":"69","author":"D Sadhukhan","year":"2019","unstructured":"Sadhukhan D, Dhar S, Pal S, Mitra M (2019) Automated screening of myocardial infarction based on statistical analysis of photoplethysmographic data. IEEE Trans Instrum Meas 69(6):2881\u20132890","journal-title":"IEEE Trans Instrum Meas"},{"key":"1293_CR24","doi-asserted-by":"publisher","first-page":"101747","DOI":"10.1016\/j.bspc.2019.101747","volume":"57","author":"A Chakraborty","year":"2020","unstructured":"Chakraborty A, Sadhukhan D, Pal S, Mitra M (2020) Automated myocardial infarction identification based on interbeat variability analysis of the photoplethysmographic data. Biomed Signal Process Control 57:101747","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"1293_CR25","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","volume":"39","author":"M Saeed","year":"2011","unstructured":"Saeed M et al (2011) Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med 39(5):952","journal-title":"Crit Care Med"},{"issue":"5","key":"1293_CR26","doi-asserted-by":"publisher","first-page":"e0217288","DOI":"10.1371\/journal.pone.0217288","volume":"14","author":"IT Hettiarachchi","year":"2019","unstructured":"Hettiarachchi IT, Hanoun S, Nahavandi D, Nahavandi S (2019) Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities. PLoS ONE 14(5):e0217288","journal-title":"PLoS ONE"},{"issue":"03","key":"1293_CR27","doi-asserted-by":"publisher","first-page":"E67","DOI":"10.1055\/a-0631-0920","volume":"2","author":"MM Schubert","year":"2018","unstructured":"Schubert MM, Clark A, Annie B (2018) The polar\u00ae OH1 optical heart rate sensor is valid during moderate-vigorous exercise. Sports Med Int Open 2(03):E67\u2013E70","journal-title":"Sports Med Int Open"},{"issue":"4","key":"1293_CR28","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1093\/ckj\/sfy135","volume":"12","author":"T Saritas","year":"2019","unstructured":"Saritas T et al (2019) Non-invasive evaluation of coronary heart disease in patients with chronic kidney disease using photoplethysmography. Clin Kidney J 12(4):538\u2013545","journal-title":"Clin Kidney J"},{"key":"1293_CR29","doi-asserted-by":"crossref","unstructured":"J. Lee, K. Matsumura, K.-i. Yamakoshi, P. Rolfe, S. Tanaka, and T. Yamakoshi, Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion. In 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2013, pp. 1724\u20131727: IEEE.","DOI":"10.1109\/EMBC.2013.6609852"},{"issue":"1","key":"1293_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-13552-4","volume":"7","author":"MV Volkov","year":"2017","unstructured":"Volkov MV et al (2017) Video capillaroscopy clarifies mechanism of the photoplethysmographic waveform appearance. Sci Rep 7(1):1\u20138","journal-title":"Sci Rep"},{"issue":"5","key":"1293_CR31","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1109\/TBCAS.2013.2295102","volume":"8","author":"D-G Jang","year":"2014","unstructured":"Jang D-G, Farooq U, Park S-H, Hahn M (2014) A robust method for pulse peak determination in a digital volume pulse waveform with a wandering baseline. IEEE Trans Biomed Circ Syst 8(5):729\u2013737","journal-title":"IEEE Trans Biomed Circ Syst"},{"issue":"5","key":"1293_CR32","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1109\/TBCAS.2015.2477437","volume":"9","author":"A Solo\u0161enko","year":"2015","unstructured":"Solo\u0161enko A, Petr\u0117nas A, Marozas V (2015) Photoplethysmography-based method for automatic detection of premature ventricular contractions. IEEE Trans Biomed Circuits Syst 9(5):662\u2013669","journal-title":"IEEE Trans Biomed Circuits Syst"},{"issue":"1","key":"1293_CR33","doi-asserted-by":"publisher","first-page":"14","DOI":"10.2174\/157340312801215782","volume":"8","author":"M Elgendi","year":"2012","unstructured":"Elgendi M (2012) On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 8(1):14\u201325","journal-title":"Curr Cardiol Rev"},{"issue":"2","key":"1293_CR34","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.bbe.2020.02.004","volume":"40","author":"A Asgharzadeh-Bonab","year":"2020","unstructured":"Asgharzadeh-Bonab A, Amirani MC, Mehri A (2020) Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Engineering 40(2):691\u2013700","journal-title":"Biocybern Biomed Engineering"},{"issue":"10","key":"1293_CR35","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1109\/TBME.2006.889772","volume":"54","author":"HM Al-Angari","year":"2007","unstructured":"Al-Angari HM, Sahakian AV (2007) Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome. IEEE Trans Biomed Eng 54(10):1900\u20131904","journal-title":"IEEE Trans Biomed Eng"},{"issue":"5","key":"1293_CR36","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s11741-008-0511-3","volume":"12","author":"S-Y Li","year":"2008","unstructured":"Li S-Y, Yang M, Li C-C, Cai P (2008) Analysis of heart rate variability based on singular value decomposition entropy. J Shanghai Univ (Eng Ed) 12(5):433","journal-title":"J Shanghai Univ (Eng Ed)"},{"issue":"4","key":"1293_CR37","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10916-016-0441-5","volume":"40","author":"R Tripathy","year":"2016","unstructured":"Tripathy R, Sharma L, Dandapat S (2016) Detection of shockable ventricular arrhythmia using variational mode decomposition. J Med Syst 40(4):79","journal-title":"J Med Syst"},{"issue":"3","key":"1293_CR38","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/JBHI.2020.3004032","volume":"25","author":"J Liu","year":"2020","unstructured":"Liu J et al (2020) PCA-based multi-wavelength photoplethysmography algorithm for cuffless blood pressure measurement on elderly subjects. IEEE J Biomed Health Inform 25(3):663\u2013673","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"1293_CR39","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1017\/S1481803500013336","volume":"8","author":"J Fan","year":"2006","unstructured":"Fan J, Upadhye S, Worster A (2006) Understanding receiver operating characteristic (ROC) curves. Can J Emerg Med 8(1):19\u201320","journal-title":"Can J Emerg Med"},{"key":"1293_CR40","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3389\/fphys.2023.1072273","volume":"14","author":"X Hu","year":"2023","unstructured":"Hu X et al (2023) Blood pressure stratification using photoplethysmography and light gradient boosting machine. Front Physiol 14:231","journal-title":"Front Physiol"},{"issue":"4","key":"1293_CR41","first-page":"195","volume":"4","author":"D Castaneda","year":"2018","unstructured":"Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H (2018) A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosensors Bioelectron 4(4):195","journal-title":"Int J Biosensors Bioelectron"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01293-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-023-01293-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-023-01293-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T14:46:14Z","timestamp":1729694774000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-023-01293-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,3]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1293"],"URL":"https:\/\/doi.org\/10.1007\/s13246-023-01293-w","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,3]]},"assertion":[{"value":"24 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"Self-generated data Ethical Number (NK\/7816\/PhD\/763).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Before the self-generated data collection, informed consent was received from the respective participant.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}