{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T23:09:02Z","timestamp":1778886542493,"version":"3.51.4"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["CNS-1750679, CNS-2210133"],"award-info":[{"award-number":["CNS-1750679, CNS-2210133"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.<\/jats:p>","DOI":"10.1145\/3616020","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T15:45:09Z","timestamp":1696607109000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7876-3206","authenticated-orcid":false,"given":"Asiful","family":"Arefeen","sequence":"first","affiliation":[{"name":"Arizona State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1387-1174","authenticated-orcid":false,"given":"Ali","family":"Akbari","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1586-0700","authenticated-orcid":false,"given":"Seyed Iman","family":"Mirzadeh","sequence":"additional","affiliation":[{"name":"Washington State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6358-0458","authenticated-orcid":false,"given":"Roozbeh","family":"Jafari","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6504-4651","authenticated-orcid":false,"given":"Behrooz A.","family":"Shirazi","sequence":"additional","affiliation":[{"name":"Washington State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-1416","authenticated-orcid":false,"given":"Hassan","family":"Ghasemzadeh","sequence":"additional","affiliation":[{"name":"Arizona State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Mobyen Uddin Ahmed and S. Begum. 2010. Heart rate and inter-beat interval computation to diagnose stress using ECG sensor signal."},{"key":"e_1_3_2_3_2","first-page":"176","article-title":"Noise removal from ecg signal based on filtering techniques","author":"Almalchy Mohammed Tali","year":"2019","unstructured":"Mohammed Tali Almalchy, V. Ciobanu, and N. Popescu. 2019. Noise removal from ecg signal based on filtering techniques. In Proceedings of the 2019 22nd International Conference on Control Systems and Computer Science (CSCS) (2019), 176\u2013181.","journal-title":"Proceedings of the 2019 22nd International Conference on Control Systems and Computer Science (CSCS)"},{"key":"e_1_3_2_4_2","first-page":"5632","article-title":"Noise detection in electrocardiography signal for robust heart rate variability analysis: A deep learning approach","author":"Ansari S.","year":"2018","unstructured":"S. Ansari, Jonathan Gryak, and K. Najarian. 2018. Noise detection in electrocardiography signal for robust heart rate variability analysis: A deep learning approach. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2018), 5632\u20135635.","journal-title":"Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)"},{"key":"e_1_3_2_5_2","first-page":"4491","article-title":"A generative adversarial approach to ECG synthesis and denoising","author":"Antczak K.","year":"2020","unstructured":"K. Antczak. 2020. A generative adversarial approach to ECG synthesis and denoising. International Business Information Management Association (IBIMA) Conference (2020), 4491\u20134501.","journal-title":"International Business Information Management Association (IBIMA) Conference"},{"key":"e_1_3_2_6_2","article-title":"Accuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients","volume":"11","author":"Antink Christoph Hoog","year":"2021","unstructured":"Christoph Hoog Antink, Yen Mai, Mikko Peltokangas, Steffen Leonhardt, Niku Oksala, and Antti Vehkaoja. 2021. Accuracy of heart rate variability estimated with reflective wrist-PPG in elderly vascular patients. Scientific Reports 11, 1 (2021), 8123.","journal-title":"Scientific Reports"},{"key":"e_1_3_2_7_2","first-page":"1","article-title":"Forewarning postprandial hyperglycemia with interpretations using machine learning","author":"Arefeen Asiful","year":"2022","unstructured":"Asiful Arefeen, Samantha N. Fessler, Carol Johnston, and Hassan Ghasemzadeh. 2022. Forewarning postprandial hyperglycemia with interpretations using machine learning. In Proceedings of the 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) (2022), 1\u20134.","journal-title":"Proceedings of the 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)"},{"key":"e_1_3_2_8_2","volume-title":"All About Heart Rate (Pulse)","author":"Association American Heart","year":"2015","unstructured":"American Heart Association. 2015. All About Heart Rate (Pulse). Retrieved from https:\/\/www.heart.org\/en\/health-topics\/high-blood-pressure\/the-facts-about-high-blood-pressure\/all-about-heart-rate-pulse"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2962627"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"Robust heart rate variability and interbeat interval detection algorithm in the presence of motion artifacts","author":"Aygun Ayca","year":"2019","unstructured":"Ayca Aygun and R. Jafari. 2019. Robust heart rate variability and interbeat interval detection algorithm in the presence of motion artifacts. In Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (2019), 1\u20135.","journal-title":"Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2010.2087386"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-009-0447-z"},{"key":"e_1_3_2_13_2","volume-title":"Why Do Athletes Have a Lower Resting Heart Rate?","author":"Chertoff Jane","year":"2020","unstructured":"Jane Chertoff. 2020. Why Do Athletes Have a Lower Resting Heart Rate? Retrieved from https:\/\/www.healthline.com\/health\/athlete-heart-rate"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00059-002-2340-4"},{"key":"e_1_3_2_15_2","first-page":"6302","volume-title":"Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Fallahzadeh Ramin","year":"2016","unstructured":"Ramin Fallahzadeh, Mahdi Pedram, and Hassan Ghasemzadeh. 2016. SmartSock: A wearable platform for context-aware assessment of ankle edema. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 6302\u20136306."},{"key":"e_1_3_2_16_2","volume-title":"Heart Disease Facts","author":"Control Centre for Disease","year":"2020","unstructured":"Centre for Disease Control and Prevention (CDC). 2020. Heart Disease Facts. Retrieved from https:\/\/www.cdc.gov\/heartdisease\/facts.htm"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00421-019-04142-5"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","first-page":"E215\u201320","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.","volume":"101","author":"Goldberger A.","year":"2000","unstructured":"A. Goldberger, L. Amaral, L. Glass, Jeffrey M. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. Peng, and H. Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101 23 (2000), E215\u201320.","journal-title":"Circulation"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"065007","DOI":"10.1088\/1361-6579\/aac9a9","article-title":"Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation.","volume":"39","author":"Harju J.","year":"2018","unstructured":"J. Harju, A. Tarniceriu, Jakub Par\u00e1k, A. Vehkaoja, A. Yli-Hankala, and I. Korhonen. 2018. Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation. Physiological Measurement 39 6 (2018), 065007.","journal-title":"Physiological Measurement"},{"key":"e_1_3_2_20_2","first-page":"15","article-title":"Portable real time ECG monitor and disease diagnostics","author":"Hasan M.","year":"2019","unstructured":"M. Hasan, S. Rahman, Asiful Arefeen, T. Ahmed, Mohtasim Nakib, C. Shahnaz, and Arik Subhana. 2019. Portable real time ECG monitor and disease diagnostics. In Proceedings of the 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) (2019), 15\u201319.","journal-title":"Proceedings of the 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)"},{"issue":"1","key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/JBHI.2017.2709333","article-title":"Speech2Health: A mobile framework for monitoring dietary composition from spoken data","volume":"22","author":"Hezarjaribi Niloofar","year":"2017","unstructured":"Niloofar Hezarjaribi, Sepideh Mazrouee, and Hassan Ghasemzadeh. 2017. Speech2Health: A mobile framework for monitoring dietary composition from spoken data. IEEE Journal of Biomedical and Health Informatics 22, 1 (2017), 252\u2013264.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC49033.2022.9700682"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","first-page":"12844","DOI":"10.3390\/s120912844","article-title":"A real-time cardiac arrhythmia classification system with wearable sensor networks","volume":"12","author":"Hu Sheng","year":"2012","unstructured":"Sheng Hu, H. Wei, Y. Chen, and J. Tan. 2012. A real-time cardiac arrhythmia classification system with wearable sensor networks. Sensors (Basel, Switzerland) 12, 9 (2012), 12844\u201312869.","journal-title":"Sensors (Basel, Switzerland)"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/SPIN.2017.8049956","article-title":"Real-time detection of electrocardiograph peaks: A genetic algorithm based approach","author":"Jain Shweta","year":"2017","unstructured":"Shweta Jain, Anil Kumar, and Varun Bajaj. 2017. Real-time detection of electrocardiograph peaks: A genetic algorithm based approach. In Proceedings of the 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN) (2017), 262\u2013266.","journal-title":"Proceedings of the 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1109\/CVPRW.2017.156","article-title":"The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation","author":"J\u00e9gou S.","year":"2017","unstructured":"S. J\u00e9gou, M. Drozdzal, David V\u00e1zquez, A. Romero, and Yoshua Bengio. 2017. The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017), 1175\u20131183.","journal-title":"Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)"},{"key":"e_1_3_2_26_2","unstructured":"Eric Jones Travis Oliphant Pearu Peterson et\u00a0al. 2001. SciPy: Open source scientific tools for Python. Retrieved from http:\/\/www.scipy.org\/"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/978-3-642-13039-7_26","article-title":"On the empirical mode decomposition performance in white gaussian noise biomedical signals","author":"Karagiannis A.","year":"2010","unstructured":"A. Karagiannis and P. Constantinou. 2010. On the empirical mode decomposition performance in white gaussian noise biomedical signals. XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 (2010), 101\u2013106.","journal-title":"XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010"},{"key":"e_1_3_2_28_2","first-page":"1","article-title":"Signal processing techniques for removing noise from ECG signals","author":"Kher R.","year":"2019","unstructured":"R. Kher. 2019. Signal processing techniques for removing noise from ECG signals. J Biomed Eng 1 (2019), 1\u20139.","journal-title":"J Biomed Eng"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"4804","DOI":"10.1109\/IEMBS.2011.6091190","article-title":"Using DWT for ECG motion artifact reduction with noise-correlating signals","author":"Kirst M.","year":"2011","unstructured":"M. Kirst, Bastian Glauner, and J. Ottenbacher. 2011. Using DWT for ECG motion artifact reduction with noise-correlating signals. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2011), 4804\u20134807.","journal-title":"Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1049\/htl.2019.0096"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","DOI":"10.1145\/3341105.3373945","article-title":"Robust ECG R-peak detection using LSTM","author":"Laitala Juho","year":"2020","unstructured":"Juho Laitala, Mingzhe Jiang, Elise Syrj\u00e4l\u00e4, Emad Kasaeyan Naeini, Antti Airola, A. Rahmani, N. Dutt, and P. Liljeberg. 2020. Robust ECG R-peak detection using LSTM. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (2020).","journal-title":"Proceedings of the 35th Annual ACM Symposium on Applied Computing"},{"key":"e_1_3_2_32_2","first-page":"1","volume-title":"Proceedings of the 4th Conference on Wireless Health","author":"Lee Sunghoon Ivan","year":"2013","unstructured":"Sunghoon Ivan Lee, Hassan Ghasemzadeh, Bobak Mortazavi, Mars Lan, Nabil Alshurafa, Michael Ong, and Majid Sarrafzadeh. 2013. Remote patient monitoring: What impact can data analytics have on cost?. In Proceedings of the 4th Conference on Wireless Health. 1\u20138."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2893225"},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.ijcard.2016.09.026","article-title":"The changing face of cardiovascular disease 2000-2012: An analysis of the world health organisation global health estimates data","volume":"224","author":"McAloon C.","year":"2016","unstructured":"C. McAloon, Luke M. Boylan, T. Hamborg, N. Stallard, F. Osman, P. Lim, and S. Hayat. 2016. The changing face of cardiovascular disease 2000-2012: An analysis of the world health organisation global health estimates data. International Journal of Cardiology 224, 1 (2016), 256\u2013264.","journal-title":"International Journal of Cardiology"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2022.100328"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"e_1_3_2_37_2","first-page":"381","article-title":"A noise stress test for arrhythmia detectors","author":"Moody G.","year":"1984","unstructured":"G. Moody, We Muldrow, and R. Mark. 1984. A noise stress test for arrhythmia detectors. Computers in Cardiology 11 (1984), 381\u2013384.","journal-title":"Computers in Cardiology"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2010.05.074"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.1985.325532"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/ICETEESES.2016.7581383","article-title":"High frequency noise removal from ECG using moving average filters","author":"Pandey V.","year":"2016","unstructured":"V. Pandey and V. K. Giri. 2016. High frequency noise removal from ECG using moving average filters. In Proceedings of the 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES) (2016), 191\u2013195.","journal-title":"Proceedings of the 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES)"},{"key":"e_1_3_2_41_2","first-page":"1","article-title":"Evaluation of the effectiveness of post-filtration smoothing using lossless compression for heart rate variability obtained from a very noisy ECG","author":"Pulavskyi A.","year":"2020","unstructured":"A. Pulavskyi, S. Krivenko, and L. S. Kryvenko. 2020. Evaluation of the effectiveness of post-filtration smoothing using lossless compression for heart rate variability obtained from a very noisy ECG. In Proceedings of the 2020 9th Mediterranean Conference on Embedded Computing (MECO) (2020), 1\u20135.","journal-title":"Proceedings of the 2020 9th Mediterranean Conference on Embedded Computing (MECO)"},{"key":"e_1_3_2_42_2","first-page":"93","article-title":"ECG heartbeat classification: A comparative performance analysis between one and two dimensional convolutional neural network","author":"Qayyum Alif Bin Abdul","year":"2019","unstructured":"Alif Bin Abdul Qayyum, Tanveerul Islam, and M. Haque. 2019. ECG heartbeat classification: A comparative performance analysis between one and two dimensional convolutional neural network. In Proceedings of the 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON) (2019), 93\u201396.","journal-title":"Proceedings of the 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)"},{"key":"e_1_3_2_43_2","article-title":"A two-stage ECG signal denoising method based on deep convolutional network","author":"Qiu Lishen","year":"2020","unstructured":"Lishen Qiu, Wenqiang Cai, Jie Yu, J. Zhong, Yan Wang, Wanyue Li, Y. Chen, and L. Wang. 2020. A two-stage ECG signal denoising method based on deep convolutional network. bioRxiv (2020).","journal-title":"bioRxiv"},{"key":"e_1_3_2_44_2","first-page":"176","article-title":"A new efficient approach for designing FIR low-pass filter and its application on ECG signal for removal of AWGN noise","author":"Rakshit Hrishi","year":"2016","unstructured":"Hrishi Rakshit and M. A. Ullah. 2016. A new efficient approach for designing FIR low-pass filter and its application on ECG signal for removal of AWGN noise. IAENG International Journal of Computer Science 43 (2016), 176\u2013183.","journal-title":"IAENG International Journal of Computer Science"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.12.004"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","DOI":"10.3390\/s20164611","article-title":"Using the redundant convolutional encoder\u2013decoder to Denoise QRS complexes in ECG signals recorded with an armband wearable device","volume":"20","author":"Reljin N.","year":"2020","unstructured":"N. Reljin, J. L\u00e1zaro, Md Billal Hossain, Yeon Sik Noh, C. Cho, and K. Chon. 2020. Using the redundant convolutional encoder\u2013decoder to Denoise QRS complexes in ECG signals recorded with an armband wearable device. Sensors (Basel, Switzerland) 20, 16 (2020), 4611.","journal-title":"Sensors (Basel, Switzerland)"},{"key":"e_1_3_2_47_2","first-page":"929","article-title":"Inter-beat (R-R) intervals analysis using a new time delay estimation technique","author":"Rezk S.","year":"2012","unstructured":"S. Rezk, C. Join, and S. E. Asmi. 2012. Inter-beat (R-R) intervals analysis using a new time delay estimation technique. In Proceedings of the 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO) (2012), 929\u2013933.","journal-title":"Proceedings of the 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)"},{"key":"e_1_3_2_48_2","first-page":"1","volume-title":"Proceedings of the 53rd Annual Design Automation Conference","author":"Rokni Seyed Ali","year":"2016","unstructured":"Seyed Ali Rokni and Hassan Ghasemzadeh. 2016. Plug-n-learn: Automatic learning of computational algorithms in human-centered internet-of-things applications. In Proceedings of the 53rd Annual Design Automation Conference. 1\u20136."},{"key":"e_1_3_2_49_2","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger O.","year":"2015","unstructured":"O. Ronneberger, P. Fischer, and T. Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention - (MICCAI) (2015), 234\u2013241.","journal-title":"Medical Image Computing and Computer-Assisted Intervention - (MICCAI)"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/2638728.2641313"},{"key":"e_1_3_2_51_2","first-page":"4658","volume-title":"Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"Sah Ramesh Kumar","year":"2022","unstructured":"Ramesh Kumar Sah, Michael John Cleveland, Assal Habibi, and Hassan Ghasemzadeh. 2022. Stressalyzer: Convolutional neural network framework for personalized stress classification. In Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 4658\u20134663."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.22266\/ijies2016.0930.12"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","DOI":"10.1093\/oxfordjournals.eurheartj.a060332","article-title":"The european ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography.","volume":"13","author":"Taddei A.","year":"1992","unstructured":"A. Taddei, G. Distante, M. Emdin, P. Pisani, G. Moody, C. Zeelenberg, and C. Marchesi. 1992. The european ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal 13, 9 (1992), 1164\u201372.","journal-title":"European Heart Journal"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519415500827"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.jelectrocard.2015.08.034","article-title":"Frequency content and characteristics of ventricular conduction.","volume":"48","author":"Tereshchenko L.","year":"2015","unstructured":"L. Tereshchenko and M. Josephson. 2015. Frequency content and characteristics of ventricular conduction. Journal of Electrocardiology 48, 6 (2015), 933\u20137.","journal-title":"Journal of Electrocardiology"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2422378"},{"key":"e_1_3_2_57_2","first-page":"345","article-title":"RPnet: A deep learning approach for robust r peak detection in noisy ecg","author":"Vijayarangan Sricharan","year":"2020","unstructured":"Sricharan Vijayarangan, Vignesh Ravichandran, Balamurali Murugesan, S. Preejith, J. Joseph, and M. Sivaprakasam. 2020. RPnet: A deep learning approach for robust r peak detection in noisy ecg. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2020), 345\u2013348.","journal-title":"Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)"},{"key":"e_1_3_2_58_2","volume-title":"Cardiovascular Diseases (CVDs)","author":"(WHO) World Health Organization","year":"2017","unstructured":"World Health Organization (WHO). 2017. Cardiovascular Diseases (CVDs). Retrieved from https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0644-9"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012904"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2359372"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616020","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3616020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:30Z","timestamp":1750178190000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":60,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,31]]}},"alternative-id":["10.1145\/3616020"],"URL":"https:\/\/doi.org\/10.1145\/3616020","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"value":"2691-1957","type":"print"},{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"2021-07-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}