{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:26:36Z","timestamp":1743114396893,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031286629"},{"type":"electronic","value":"9783031286636"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-28663-6_4","type":"book-chapter","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T07:03:02Z","timestamp":1678863782000},"page":"40-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preliminary Study on Gender Identification by Electrocardiography Data"],"prefix":"10.1007","author":[{"given":"Eduarda Sofia","family":"Bastos","sequence":"first","affiliation":[]},{"given":"Rui Pedro","family":"Duarte","sequence":"additional","affiliation":[]},{"given":"Francisco Alexandre","family":"Marinho","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds","family":"Pimenta","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio Jorge","family":"Gouveia","sequence":"additional","affiliation":[]},{"given":"Norberto Jorge","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Paulo Jorge","family":"Coelho","sequence":"additional","affiliation":[]},{"given":"Eftim","family":"Zdravevski","sequence":"additional","affiliation":[]},{"given":"Petre","family":"Lameski","sequence":"additional","affiliation":[]},{"given":"Nuno M.","family":"Garcia","sequence":"additional","affiliation":[]},{"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Alazzam, H., Alsmady, A., Shorman, A.A.: Supervised detection of IoT botnet attacks. In: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, pp. 1\u20136 (2019)","DOI":"10.1145\/3368691.3368733"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"733","DOI":"10.3390\/e23060733","volume":"23","author":"DA AlDuwaile","year":"2021","unstructured":"AlDuwaile, D.A., Islam, M.S.: Using convolutional neural network and a single heartbeat for ECG biometric recognition. Entropy 23, 733 (2021)","journal-title":"Entropy"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","volume":"63","author":"F Ali","year":"2020","unstructured":"Ali, F., et al.: A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf. Fus. 63, 208\u2013222 (2020)","journal-title":"Inf. Fus."},{"issue":"6","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1007\/s11704-016-5203-5","volume":"10","author":"S Almuhaideb","year":"2016","unstructured":"Almuhaideb, S., Menai, M.E.B.: Impact of preprocessing on medical data classification. Front. Comput. Sci. 10(6), 1082\u20131102 (2016). https:\/\/doi.org\/10.1007\/s11704-016-5203-5","journal-title":"Front. Comput. Sci."},{"key":"4_CR5","first-page":"435","volume":"3","author":"S Amarappa","year":"2014","unstructured":"Amarappa, S., Sathyanarayana, S.V.: Data classification using support vector machine (SVM), a simplified approach. Int. J. Electron. Comput. Sci. Eng. 3, 435\u2013445 (2014)","journal-title":"Int. J. Electron. Comput. Sci. Eng."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.phrs.2016.09.040","volume":"113","author":"P Balakumar","year":"2016","unstructured":"Balakumar, P., Maung-U, K., Jagadeesh, G.: Prevalence and prevention of cardiovascular disease and diabetes mellitus. Pharmacol. Res. 113, 600\u2013609 (2016)","journal-title":"Pharmacol. Res."},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1049\/htl.2018.5037","volume":"6","author":"D Batista","year":"2019","unstructured":"Batista, D., Pl\u00e1cido da Silva, H., Fred, A., Moreira, C., Reis, M., Ferreira, H.A.: Benchmarking of the BITalino biomedical toolkit against an established gold standard. Healthc. Technol. Lett. 6, 32\u201336 (2019)","journal-title":"Healthc. Technol. Lett."},{"key":"4_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1083-6","volume":"42","author":"S Celin","year":"2018","unstructured":"Celin, S., Vasanth, K.: ECG signal classification using various machine learning techniques. J. Med. Syst. 42, 1\u201311 (2018)","journal-title":"J. Med. Syst."},{"key":"4_CR9","unstructured":"Chio, C., Freeman, D.: Machine Learning and Security: Protecting Systems With data and Algorithms. O\u2019Reilly Media, Inc. (2018)"},{"key":"4_CR10","unstructured":"Da Silva, H.P., Guerreiro, J., Louren\u00e7o, A., Fred, A.L., Martins, R.: BITalino: a novel hardware framework for physiological computing. In: PhyCS, pp. 246\u2013253 (2014)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Escobar, L.J.V., Salinas, S.A.: e-Health prototype system for cardiac telemonitoring. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 4399\u20134402. IEEE (2016)","DOI":"10.1109\/EMBC.2016.7591702"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s10044-007-0087-5","volume":"11","author":"V Garc\u00eda","year":"2008","unstructured":"Garc\u00eda, V., Mollineda, R.A., S\u00e1nchez, J.S.: On the k-NN performance in a challenging scenario of imbalance and overlapping. Pattern Anal. Appl. 11, 269\u2013280 (2008)","journal-title":"Pattern Anal. Appl."},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Gautam, M.K., Giri, V.K.: A neural network approach and wavelet analysis for ECG classification. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India, pp. 1136\u20131141. IEEE (2016)","DOI":"10.1109\/ICETECH.2016.7569428"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Hastie, T., Rosset, S., Zhu, J., Zou, H.: Multi-class AdaBoost. Stat. Interface 2, 349\u2013360 (2009). https:\/\/doi.org\/10.4310\/SII.2009.v2.n3.a8","DOI":"10.4310\/SII.2009.v2.n3.a8"},{"key":"4_CR15","volume-title":"Neural Networks: A Comprehensive Foundation","author":"S Haykin","year":"1994","unstructured":"Haykin, S.: Neural Networks: A Comprehensive Foundation, 1st edn. Prentice Hall PTR,  Hoboken (1994)","edition":"1"},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"C-W Hsu","year":"2002","unstructured":"Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13, 415\u2013425 (2002)","journal-title":"IEEE Trans. Neural Netw."},{"key":"4_CR17","unstructured":"Pires, I.M., Garcia, N.M., Pires, I., Pinto, R., Silva, P.: ECG data related to 30-s seated and 30-s standing for 5P-Medicine project. Mendeley Data (2022). https:\/\/data.mendeley.com\/datasets\/z4bbj9rcwd\/1"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Jindal, H., Agrawal, S., Khera, R., Jain, R., Nagrath, P.: Heart disease prediction using machine learning algorithms. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, p. 012072 (2021)","DOI":"10.1088\/1757-899X\/1022\/1\/012072"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Kakria, P., Tripathi, N.K., Kitipawang, P.: A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int. J. Telemed. Appl., 1\u201311 (2015). https:\/\/doi.org\/10.1155\/2015\/373474","DOI":"10.1155\/2015\/373474"},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.cmpb.2006.01.009","volume":"82","author":"N Kannathal","year":"2006","unstructured":"Kannathal, N., Acharya, U.R., Ng, E.Y.K., Krishnan, S.M., Min, L.C., Laxminarayan, S.: Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals. Comput. Methods Programs Biomed. 82, 87\u201396 (2006). https:\/\/doi.org\/10.1016\/j.cmpb.2006.01.009","journal-title":"Comput. Methods Programs Biomed."},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587630"},{"key":"4_CR22","unstructured":"Pires, I.M., Garcia, N.M., Fl\u00f3rez-Revuelta, F.: Multi-sensor data fusion techniques for the identification of activities of daily living using mobile devices. In: Proceedings of the ECMLPKDD (2015)"},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1111\/j.0006-341X.2002.00454.x","volume":"58","author":"GJ Prescott","year":"2002","unstructured":"Prescott, G.J., Garthwaite, P.H.: A simple Bayesian analysis of misclassified binary data with a validation substudy. Biometrics 58, 454\u2013458 (2002)","journal-title":"Biometrics"},{"key":"4_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102779","volume":"68","author":"E Ramaraj","year":"2021","unstructured":"Ramaraj, E.: A novel deep learning based gated recurrent unit with extreme learning machine for electrocardiogram (ECG) signal recognition. Biomed. Signal Process. Control 68, 102779 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Suthaharan, S.: Support vector machine. In: Suthaharan, S. (ed.) Machine Learning Models and Algorithms for Big Data Classification. Integrated Series in Information Systems, vol. 36, pp. 207\u2013235. Springer, Boston (2016). https:\/\/doi.org\/10.1007\/978-1-4899-7641-3_9","DOI":"10.1007\/978-1-4899-7641-3_9"},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.engappai.2018.11.009","volume":"78","author":"TM Tran","year":"2019","unstructured":"Tran, T.M., Le, X.-M.T., Nguyen, H.T., Huynh, V.-N.: A novel non-parametric method for time series classification based on k-nearest neighbors and dynamic time warping barycenter averaging. Eng. Appl. Artif. Intell. 78, 173\u2013185 (2019)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1016\/S0140-6736(21)00684-X","volume":"397","author":"B Vogel","year":"2021","unstructured":"Vogel, B., et al.: The Lancet women and cardiovascular disease commission: reducing the global burden by 2030. Lancet 397, 2385\u20132438 (2021)","journal-title":"Lancet"},{"key":"4_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-005-4258-6","volume":"58","author":"GI Webb","year":"2005","unstructured":"Webb, G.I., Boughton, J.R., Wang, Z.: Not so Naive Bayes: aggregating one-dependence estimators. Mach. Learn. 58, 5\u201324 (2005). https:\/\/doi.org\/10.1007\/s10994-005-4258-6","journal-title":"Mach. Learn."},{"key":"4_CR29","unstructured":"Neurophysiological Data Analysis with NeuroKit2 \u2014 NeuroKit2 0.2.1 documentation. https:\/\/neuropsychology.github.io\/NeuroKit\/. Accessed 10 July 2022"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","IoT Technologies for HealthCare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28663-6_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T07:09:05Z","timestamp":1678864145000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28663-6_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031286629","9783031286636"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28663-6_4","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HealthyIoT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EAI International Conference on IoT Technologies for HealthCare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"healthyiot2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/healthyiot.eai-conferences.org\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EAI Confy+","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}