{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:21:15Z","timestamp":1742912475276,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819990047"},{"type":"electronic","value":"9789819990054"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9005-4_57","type":"book-chapter","created":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T16:02:03Z","timestamp":1711814523000},"page":"453-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review of ECG Biometrics: Generalization in Deep Learning with Attention Mechanisms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3304-657X","authenticated-orcid":false,"given":"Aini Hafizah Mohd","family":"Saod","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4392-2895","authenticated-orcid":false,"given":"Dzati Athiar","family":"Ramli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"57_CR1","volume-title":"The ECG made easy","author":"J Hampton","year":"2019","unstructured":"Hampton J, Hampton J (2019) The ECG made easy, 9th edn. Elsevier, Amsterdam","edition":"9"},{"key":"57_CR2","doi-asserted-by":"publisher","first-page":"34746","DOI":"10.1109\/ACCESS.2018.2849870","volume":"6","author":"JR Pinto","year":"2020","unstructured":"Pinto JR, Member S (2020) Evolution, current challenges, and future possibilities in ECG biometrics. IEEE Access 6:34746\u201334776","journal-title":"IEEE Access"},{"key":"57_CR3","doi-asserted-by":"publisher","first-page":"117853","DOI":"10.1109\/ACCESS.2020.3004464","volume":"8","author":"M Ingale","year":"2020","unstructured":"Ingale M, Cordeiro R, Thentu S, Park Y, Karimian N (2020) ECG biometric authentication: a comparative analysis. IEEE Access 8:117853\u2013117866","journal-title":"IEEE Access"},{"issue":"1","key":"57_CR4","first-page":"1","volume":"14","author":"A Fratini","year":"2015","unstructured":"Fratini A, Sansone M, Bifulco P, Cesarelli M (2015) Individual identification via electro-cardiogram analysis. Biomed Eng 14(1):1\u201323","journal-title":"Biomed Eng"},{"key":"57_CR5","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.future.2019.06.008","volume":"101","author":"M Hammad","year":"2019","unstructured":"Hammad M, Zhang S, Wang K (2019) A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication. Fut Gener Comput Syst 101:180\u2013196","journal-title":"Fut Gener Comput Syst"},{"key":"57_CR6","doi-asserted-by":"publisher","first-page":"97760","DOI":"10.1109\/ACCESS.2021.3095248","volume":"9","author":"AN Uwaechia","year":"2021","unstructured":"Uwaechia AN, Ramli DA (2021) A comprehensive survey on ECG signals as new bio-metric modality for human authentication: recent advances and future challenges. IEEE Access 9:97760\u201397802","journal-title":"IEEE Access"},{"key":"57_CR7","first-page":"1","volume":"14","author":"S Mannam","year":"2019","unstructured":"Mannam S (2019) Artificial intelligence, machine learning, and deep learning: are they all the same? J Young Invest 14:1\u20133","journal-title":"J Young Invest"},{"key":"57_CR8","volume-title":"Deep learning with Python","author":"F Chollet","year":"2018","unstructured":"Chollet F (2018) Deep learning with Python. Manning Publications, New York"},{"doi-asserted-by":"crossref","unstructured":"Yang S, Yu X (2020) LSTM and GRU neural network performance comparison study. In: Proceedings of the international workshop electronic communications AI. IEEE, p 98","key":"57_CR9","DOI":"10.1109\/IWECAI50956.2020.00027"},{"issue":"3","key":"57_CR10","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.3390\/app11031125","volume":"11","author":"HM Lynn","year":"2021","unstructured":"Lynn HM, Kim P, Pan SB (2021) Data independent acquisition based bi-directional deep networks for biometric ECG authentication. Appl Sci 11(3):1125","journal-title":"Appl Sci"},{"key":"57_CR11","first-page":"105","volume-title":"AI and ML for healthcare: image and data analytics","author":"ESY Ern","year":"2022","unstructured":"Ern ESY, Ramli DA (2022) Classification of arrhythmia signals using hybrid convolutional neural network (CNN) model. AI and ML for healthcare: image and data analytics. Springer, Cham, pp 105\u2013132"},{"doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder\u2013decoder approaches","key":"57_CR12","DOI":"10.3115\/v1\/W14-4012"},{"unstructured":"Bahdanau D, Cho KH, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd international conference on learning representation, pp 1\u201315. arXiv preprint arXiv:1409.1259","key":"57_CR13"},{"key":"57_CR14","first-page":"5999","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5999\u20136009","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"57_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv 53(3):1\u201334","journal-title":"ACM Comput Surv"},{"key":"57_CR16","doi-asserted-by":"publisher","first-page":"103493","DOI":"10.1016\/j.bspc.2022.103493","volume":"74","author":"SW Chen","year":"2022","unstructured":"Chen SW, Wang SL, Qi XZ, Samuri SM, Yang C (2022) Review of ECG detection and classification based on deep learning: coherent taxonomy, motivation, open challenges, and recommendations. Biomed Sig Process Control 74:103493","journal-title":"Biomed Sig Process Control"},{"issue":"9","key":"57_CR17","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.3390\/s22093446","volume":"22","author":"KJ Chee","year":"2022","unstructured":"Chee KJ, Ramli DA (2022) Electrocardiogram biometrics using transformer\u2019s self-attention mechanism for sequence pair feature extractor and flexible enrollment scope identification. Sensors 22(9):3446","journal-title":"Sensors"},{"key":"57_CR18","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.patrec.2018.03.028","volume":"126","author":"RD Labati","year":"2019","unstructured":"Labati RD, Mu\u00f1oz E, Piuri V, Sassi R, Scotti F (2019) Deep-ECG: convolutional neural networks for ECG biometric recognition. Pattern Recogn Lett 126:78\u201385","journal-title":"Pattern Recogn Lett"},{"key":"57_CR19","first-page":"e11107","volume":"14","author":"SC Wu","year":"2021","unstructured":"Wu SC, Wei SY, Chang CS, Swindlehurst AL, Chiu JK (2021) A scalable open-set ECG identification system based on compressed CNNs. IEEE Trans Neural Netw Learn Syst 14:e11107","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"16","key":"57_CR20","doi-asserted-by":"publisher","first-page":"13371","DOI":"10.1007\/s00521-022-07366-3","volume":"34","author":"D Soydaner","year":"2022","unstructured":"Soydaner D (2022) Attention mechanism in neural networks: where it comes and where it goes. Neural Comput Appl 34(16):13371\u201313385","journal-title":"Neural Comput Appl"},{"key":"57_CR21","first-page":"577","volume":"28","author":"JD Chorowski","year":"2015","unstructured":"Chorowski JD, Bahdanau S, Serdyuk D, Cho K, Bengio Y (2015) Attention-based models for speech recognition. Adv Neural Inf Process Syst 28:577\u2013585","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"57_CR22","doi-asserted-by":"publisher","first-page":"6052","DOI":"10.1109\/JSEN.2021.3139135","volume":"22","author":"D Jyotishi","year":"2022","unstructured":"Jyotishi D, Dandapat S (2022) An ECG biometric system using hierarchical LSTM with attention mechanism. IEEE Sens J 22(6):6052\u20136061","journal-title":"IEEE Sens J"},{"key":"57_CR23","first-page":"217","volume-title":"The 2021 intelligent systems conference","author":"A Katrompas","year":"2022","unstructured":"Katrompas A, Metsis V (2022) Enhancing LSTM models with self-attention and stateful training. The 2021 intelligent systems conference. Springer, New York, pp 217\u2013235"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9005-4_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T16:06:15Z","timestamp":1711814775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9005-4_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819990047","9789819990054"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9005-4_57","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RoViSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Robotics, Vision, Signal Processing and Power Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rovisp2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}