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Table of Contents

    Topic review

    ECG Monitoring Systems

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    Submitted by: Mohamed Adel Serhani

    Definition

    Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

    1. Introduction

    The last decade has witnessed an increasing number of deaths caused by chronic and cardiovascular diseases (CVDs) in all countries across the world. These include, for instance, stroke, heart failure, heart attack, arrhythmia, coronary artery disease, cardiomyopathy, rheumatic heart disease, vascular disease, and others. According to a study from the World Health Organization (WHO), CVDs are the number one cause of death globally, with 17.9 million deaths every year [[1]]. It remains the number one cause of death of all Americans, claiming more than 840,000 lives in 2016 [[2]]. Furthermore, the European Health Network 2017 statistics revealed that CVDs cause 3.9 million deaths in Europe and over 1.8 million deaths in the European Union (EU) yearly. This accounts for 45% of all deaths in Europe and 37% of all deaths in the EU [[3]].

    2. Processes, and Key Challenges

    Continuous heart rate monitoring and immediate heartbeat detection are primary concerns in contemporary healthcare. Experimental evidence has shown that many of the CVDs could be better diagnosed, controlled, and prevented through continuous monitoring, as well as analysis of electrocardiogram (ECG) signals [[4][5][6][7][8][9]]. Hence, the monitoring of physiological signals, such as electrocardiogram (ECG) signals, offers a new holistic paradigm for the assessment of CVDs, supporting disease control and prevention. With advances in sensor technology, communication infrastructure, data processing, and modeling as well as analytics algorithms the risk of impairments could be better addressed more than ever done before. This, in turn, would introduce a new era of smart, proactive healthcare especially with the great challenge of limited medical resources.

    As a result, ECG monitoring systems have been developed and widely used in the healthcare sector for the past few decades and have significantly evolved over time due to the emergence of smart enabling technologies [[10][11][12][13]]. Nowadays, ECG monitoring systems are used in hospitals [[14][15][16][17]], homes [[18][19][20]], outpatient ambulatory settings [[21][22][23]], and in remote contexts [[24]]. They also employ a wide range of technologies such as IoT [[25][26][27]], edge computing [[28][29]], and mobile computing [[30][31][32]]. In addition, they implement various computational settings in terms of processing frequencies, as well as monitoring schemes. They have also evolved to serve purposes and targets other than disease diagnosis and control, including daily activities [[33][34][35]], sports [[36][37][38]], and even mode-related purposes [[39][40][41]].

    This massive diversity in ECG monitoring systems’ contexts, technologies, computational schemes, and purposes makes it hard for researchers and professionals to design, classify, and analyze ECG monitoring systems. Some efforts attempted to provide a common understanding of ECG monitoring systems’ processes [[42][43][44][45][46][47]], guiding the design of efficient monitoring systems. However, these studies lack comprehensiveness and completeness. They work for specific contexts, serve specific targets, or are suitable for specific technologies. This makes the available ECG monitoring system processes and architectures hard to generalize and reuse. On the other hand, some studies attempted to analyze ECG monitoring systems’ attributes and provide classification taxonomies, supporting better analysis and understanding of the ECG systems reported in the literature. However, exiting reviews related to ECG monitoring in the literature can be intuitive and incomprehensive [[48]]. They do not consider the latest technological trends [[49][50][51]], and they target very narrow research niches, such as wearable sensors [[52][53][54][55]], mobile sensors [[56]], disease diagnosis [[57]], heartbeat detection [[58]], emotion recognition [[59]], or ECG compression methods [[60]]. Hence, there is a need to provide a comprehensive, expert-verified taxonomy of ECG monitoring systems, a common architecture, and a complete set of processes to guide the classification, analysis, and design of these systems.

    Therefore, in this work, we propose an expert-verified taxonomy of ECG monitoring systems, a generic architectural model, and a complete, general set of processes to support better understanding, analysis, design, and validation of ECG monitoring systems from a broader perspective. Our experts’ taxonomy is composed of five distinct, cohesive clusters. Each cluster focuses on one dimension of ECG monitoring systems, detailing the features and attributes of these systems in that dimension. These include monitoring contexts, technologies, schemes, targets, and futuristic monitoring systems. In addition to our experts’ taxonomy, the proposed ECG monitoring systems’ layered architecture depicts essential structural components and elements of ECG monitoring systems, their interfaces, and the data inputs/outputs of each layer. We also complement our experts’ taxonomy and the generic architecture with a comprehensive ECG monitoring process model, highlighting the major processes, sub-processes, and supporting processes, emphasizing factors adding value to each process. Based on the proposed taxonomy, architecture, and common process model, we conduct an extensive, thorough analysis of the literature surrounding ECG monitoring systems, highlighting systems’ categories, attributes, functions, challenges, and current trends, leading to a panorama of ECG monitoring systems. To our best knowledge, this is the most comprehensive, expert-verified review of ECG monitoring systems to date.

    This entry is adapted from 10.3390/s20061796

    References

    1. World Health Organization. Cardiovascular Diseases. Available online: www.who.int/health-topics/cardiovascular-diseases/#tab=tab_1. (accessed on 16 March 2020)
    2. Scutti, S. Nearly Half of US Adults Have Cardiovascular Disease, Study Says. CNN, Cable News Network. 31 January 2019. Available online: www.cnn.com/2019/01/31/health/heart-disease-statistics-report/index.html (accessed on 15 March 2020)
    3. European Cardiovascular Disease Statistics 2017; The European Heart Network (EHN): Brussels, Belgium, 2017; Available online: http://www.ehnheart.org/cvd-statistics.html (accessed on 15 March 2020)
    4. Muhammad Faiz Ul Hassan; Dakun Lai; Yuxiang Bu; Characterization of Single Lead Continuous ECG Recording with Various Dry Electrodes. Proceedings of the 2019 3rd International Conference on Computational Biology and Bioinformatics - ICCBB '19 2019, , 76-79, 10.1145/3365966.3365974.
    5. S P Preejith; R Dhinesh; Jayaraj Joseph; M Sivaprakasam; Wearable ECG platform for continuous cardiac monitoring. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2016, , 623-626, 10.1109/embc.2016.7590779.
    6. Majdi Bsoul; Hlaing Minn; Lakshman Tamil; Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG. IEEE Transactions on Information Technology in Biomedicine 2010, 15, 416-427, 10.1109/TITB.2010.2087386.
    7. Dimitra Azariadi; Vasileios Tsoutsouras; Sotirios Xydis; Dimitrios Soudris; ECG signal analysis and arrhythmia detection on IoT wearable medical devices. 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST) 2016, , 1-4, 10.1109/mocast.2016.7495143.
    8. Udit Satija; M Sabarimalai Manikandan; Barathram. Ramkumar; Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring. IEEE Internet of Things Journal 2017, 4, 1-1, 10.1109/JIOT.2017.2670022.
    9. Matteo Antonio Scrugli; Daniela Loi; Luigi Raffo; Paolo Meloni; A runtime-adaptive cognitive IoT node for healthcare monitoring. Proceedings of the 16th ACM International Conference on Computing Frontiers - CF '19 2019, , 350-357, 10.1145/3310273.3323160.
    10. Kazi Abu Zilani; Rubyea Yeasmin; Kazi Abu Zubair; Redwan Sammir; Samia Sabrin; R3HMS, An IoT Based Approach for Patient Health Monitoring. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2) 2018, , 1-4, 10.1109/ic4me2.2018.8465482.
    11. Kim, K. Europace Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography. Europace 2018, 1–8.
    12. Bansal, M.; Gandhi, B. IoT big data in smart healthcare (ECG monitoring). In Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon, Faridabad, India, 14–16 February 2019.
    13. Nada Chendeb Taher; Imane Mallat; Nazim Agoulmine; Nour El-Mawass; An IoT-Cloud Based Solution for Real-Time and Batch Processing of Big Data: Application in Healthcare. 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART) 2019, , 1-8, 10.1109/biosmart.2019.8734185.
    14. B. Chamadiya; K. Mankodiya; M. Wagner; Ulrich G. Hofmann; Textile-based, contactless ECG monitoring for non-ICU clinical settings. Journal of Ambient Intelligence and Humanized Computing 2012, 4, 791-800, 10.1007/s12652-012-0153-8.
    15. Ionel Zagan; Vasile Gheorghiţă Găitan; Nicolai Iuga; Adrian Brezulianu; m-GreenCARDIO embedded system designed for out-of-hospital cardiac patients. 2018 International Conference on Development and Application Systems (DAS) 2018, , 11-17, 10.1109/daas.2018.8396063.
    16. Alramzana Nujum Navaz; Elfadil Mohammed; Mohamed Adel Serhani; Nazar Zaki; The use of data mining techniques to predict mortality and length of stay in an ICU. 2016 12th International Conference on Innovations in Information Technology (IIT) 2016, , 1-5, 10.1109/innovations.2016.7880045.
    17. Arnaud S. R. M. Ahouandjinou; Kokou Assogba; Cina Motamed; Smart and pervasive ICU based-IoT for improving intensive health care. 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART) 2016, , 1-4, 10.1109/biosmart.2016.7835599.
    18. Steven R. Steinhubl; Jill Waalen; Alison M Edwards; Lauren M Ariniello; Rajesh R Mehta; Gail S Ebner; Chureen Carter; Katie Baca-Motes; Elise Felicione; Troy Sarich; et al. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial.. JAMA 2018, 320, 146-155, .
    19. Benhamida, A.; Zouaoui, A.; Szócska, G.; Karóczkai, K.; Slimani, G.; Kozlovszky, M. Problems in archiving long-term continuous ECG data - A review. In Proceedings of the SAMI 2019 - IEEE 17th World Symposium on Applied Machine Intelligence and Informatics, Herlany, Slovakia, 24–26 January 2019.
    20. Shakthi Murugan K.H.; Sriram T.J.; Melvin Jerold P.; Syed Akif Peeran A.; Abisheik P.; Wearable ECG Electrodes for Detection of Heart Rate and Arrhythmia Classification. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) 2019, , 1-6, 10.1109/icecct.2019.8869143.
    21. Andrius Petrėnas; Vaidotas Marozas; G. Jarusevicius; L. Sörnmo; A modified Lewis ECG lead system for ambulatory monitoring of atrial arrhythmias. Journal of Electrocardiology 2015, 48, 157-163, 10.1016/j.jelectrocard.2014.12.005.
    22. Erik Fung; Marjo-Riitta Jarvelin; Rahul N. Doshi; Jerold S. Shinbane; Steven K. Carlson; Luanda P. Grazette; Philip M. Chang; Rajbir S. Sangha; Heikki V. Huikuri; Nicholas S. Peters; et al. Electrocardiographic patch devices and contemporary wireless cardiac monitoring. Frontiers in Physiology 2015, 6, , 10.3389/fphys.2015.00149.
    23. H. Mamaghanian; N. Khaled; D. Atienza; P. VanderGheynst; Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Transactions on Biomedical Engineering 2011, 58, 2456-2466, 10.1109/TBME.2011.2156795.
    24. Kai Guan; Minggang Shao; Shuicai Wu; A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway. Journal of Healthcare Engineering 2017, 2017, 5843504-9, 10.1155/2017/5843504.
    25. Orestis Akrivopoulos; Dimitrios Amaxilatis; Athanasios Antoniou; Ioannis Chatzigiannakis; Design and Evaluation of a Person-Centric Heart Monitoring System over Fog Computing Infrastructure. Proceedings of the first international workshop on Network-aware data management - NDM '11 2017, , , 10.1145/3144730.3144736.
    26. Shreshth Tuli; Nipam Basumatary; Sukhpal Singh Gill; Mohsen Kahani; Rajesh Chand Arya; Gurpreet Singh Wander; Rajkumar Buyya; HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments. 2019, , , .
    27. Maryem Neyja; Shahid Mumtaz; Kazi Mohammed Saidul Huq; Sherif Adeshina Busari; Jonathan Rodriguez; Zhenyu Zhou; An IoT-Based E-Health Monitoring System Using ECG Signal. GLOBECOM 2017 - 2017 IEEE Global Communications Conference 2017, , 1-6, 10.1109/glocom.2017.8255023.
    28. Orestis Akrivopoulos; Dimitrios Amaxilatis; Irene Mavrommati; Ioannis Chatzigiannakis; Utilising Fog Computing for Developing a Person-Centric Heart Monitoring System. 2018 14th International Conference on Intelligent Environments (IE) 2018, , 9-16, 10.1109/ie.2018.00010.
    29. Wanqing Wu; Sandeep Pirbhulal; Arun Kumar Sangaiah; S.C. Mukhopadhyay; Guanglin Li; Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Generation Computer Systems 2018, 86, 515-526, 10.1016/j.future.2018.04.024.
    30. M. Smolen; Eliasz Kantoch; Piotr Augustyniak; P. Kowalski; Wearable Patient Home Monitoring Based on ECG and ACC Sensors. World Congress on Medical Physics and Biomedical Engineering 2006 2011, 37, 941-944, 10.1007/978-3-642-23508-5_244.
    31. Yusof, M.A.; Hau, Y.W. Mini home-based vital sign monitor with android mobile application (myVitalGear). In Proceedings of the 2018 IEEE EMBS Conference on Biomedical Engineering and Science IECBES, Sarawak, Malaysia, 3–6 December 2018.
    32. Hsieh, S.-T.; Lin, C.-L. Intelligent healthcare system using an arduino microcontroller and an android-based smartphone. BioMed Res. 2017, 28, 9940–9946.
    33. Young-Dong Lee; Wan-Young Chung; Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring. Sensors and Actuators B: Chemical 2009, 140, 390-395, 10.1016/j.snb.2009.04.040.
    34. M. Pirozzi; F. Pietroni; S. Casaccia; L. Scalise; G.M. Revel; Cardiac Activity Classification using an E-Health App for a Wearable Device. 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2018, , 1-6, 10.1109/memea.2018.8438674.
    35. Ellora Sen-Gupta; Donald E. Wright; James W. Caccese; John A. Wright Jr.; Elise Jortberg; Viprali Bhatkar; Melissa Ceruolo; Roozbeh Ghaffari; Dennis L. Clason; James P. Maynard; et al. A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments. Digital Biomarkers 2019, 3, 1-13, 10.1159/000493642.
    36. Gerardo Bosco; Elena De Marzi; Pierantonio Michieli; Hesham R. Omar; Enrico Camporesi; Johnny Padulo; Antonio Paoli; Devanand Mangar; Maurizio Schiavon; 12-lead Holter monitoring in diving and water sports: a preliminary investigation.. Diving and Hyperbaric Medicine Journal 2014, 44, , .
    37. Predrag Raković; Budimir Lutovac; Rakovic Predrag; A cloud computing architecture with wireless body area network for professional athletes health monitoring in sports organizations Case study of Montenegro. 2015 4th Mediterranean Conference on Embedded Computing (MECO) 2015, , 387-390, 10.1109/meco.2015.7181950.
    38. Vika Octaviani; Arief Kurniawan; Yoyon Kusnendar Suprapto; Ahmad Zaini; Alerting system for sport activity based on ECG signals using proportional integral derivative. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) 2017, , 1-6, 10.1109/eecsi.2017.8239104.
    39. Robert M. Carney; Kenneth E. Freedland; Brian C. Steinmeyer; Eugene H. Rubin; Phyllis K. Stein; Michael W. Rich; Nighttime heart rate predicts response to depression treatment in patients with coronary heart disease.. Journal of Affective Disorders 2016, 200, 165-71, 10.1016/j.jad.2016.04.051.
    40. Gaetano Valenza; Mimma Nardelli; Antonio Lanatà; Claudio Gentili; G. Bertschy; Markus Kosel; Enzo Pasquale Scilingo; Predicting Mood Changes in Bipolar Disorder Through Heartbeat Nonlinear Dynamics. IEEE Journal of Biomedical and Health Informatics 2016, 20, 1034-1043, 10.1109/jbhi.2016.2554546.
    41. Migliorini, M.; Mariani, S.; Bertschy, G.; Kosel, M.; Bianchi, A.M. Can home-monitoring of sleep predict depressive episodes in bipolar patients? In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Milano, Italy, 25–29 August 2015; pp. 2215–2218.
    42. Jan Gierałtowski; Kamil Ciuchcinski; Iga Grzegorczyk; Katarzyna Kosna; Mateusz Soliński; Piotr Podziemski; RS slope detection algorithm for extraction of heart rate from noisy, multimodal recordings. Physiological Measurement 2015, 36, 1743-1761, 10.1088/0967-3334/36/8/1743.
    43. Ghosh, S.; Feng, M.; Nguyen, H.; Li, J. Predicting heart beats using co-occurring constrained sequential patterns. In Proceedings of the Computing in Cardiology 2014, Cambridge, MA, USA, 7–10 September 2014; pp. 265–268.
    44. Yang, B.; Teo, S.K.; Hoeben, B.; Monterola, C.; Su, Y. Robust identification of heartbeats with blood pressure signals and noise detection. In Proceedings of the Computing in Cardiology 2014, Cambridge, MA, USA, 7–10 September 2014; pp. 565–568.
    45. Gierałtowski, J.J.; Ciuchciński, K.; Grzegorczyk, I.; Kośna, K.; Soliński, M.; Podziemski, P. Heart rate variability discovery: Algorithm for detection of heart rate from noisy, multimodal recordings. In Proceedings of the Computing in Cardiology 2014, Cambridge, MA, USA, 7–10 September 2014; pp. 253–256.
    46. Nadi Sadr; Madhuka Jayawardhana; Thuy T Pham; Rui Tang; Asghar Tabatabaei Balaei; Philip De Chazal; A low-complexity algorithm for detection of atrial fibrillation using an ECG. Physiological Measurement 2018, 39, 064003, 10.1088/1361-6579/aac76c.
    47. Udit Satija; Barathram. Ramkumar; M Sabarimalai Manikandan; Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics 2017, 22, 722-732, 10.1109/jbhi.2017.2686436.
    48. Bhattacherjee, P.; Ganguly, D.; Chatterjee, K. A review of Steganography techniques suitable for ECG signal. In Proceedings of the International Conference on Emerging Technologies for Sustainable Development (ICETSD ’19), Kolkata, India, 5–6 March 2019; pp. 1–7
    49. Mora, F.A.; Passariello, G.; Carrault, G.; Le Pichon, J.-P. Intelligent patient monitoring and management systems: A review. IEEE Eng. Med. Biol. Mag. 1993, 12, 23–33. [Google Scholar] [CrossRef]Kundu, M.; Nasipuri, M.; Basu, D.K. Knowledge-based ECG interpretation: A critical review. Pattern Recognit. 2000, 33, 351–373. [Google Scholar] [CrossRef]Sohn, H.; Farrar, C.R.; Hemez, F.M.; Shunk, D.D.; Stinemates, D.W.; Nadler, B.R.; Czarnecki, J.J. A Review of Structural Health Monitoring Literature: 1996–2001; Los Alamos National Laboratory: Los Alamos, NM USA, 2003; pp. 1–7
    50. Mahantapas Kundu; Mita Nasipuri; Dipak Kumar Basu; Knowledge-based ECG interpretation: a critical review. Pattern Recognition 2000, 33, 351-373, 10.1016/s0031-3203(99)00065-5.
    51. Sohn, H.; Farrar, C.R.; Hemez, F.M.; Shunk, D.D.; Stinemates, D.W.; Nadler, B.R.; Czarnecki, J.J. A Review of Structural Health Monitoring Literature: 1996–2001; Los Alamos National Laboratory: Los Alamos, NM USA, 2003; pp. 1–7.
    52. Amay J. Bandodkar; Joseph Wang; Non-invasive wearable electrochemical sensors: a review. Trends in Biotechnology 2014, 32, 363-371, 10.1016/j.tibtech.2014.04.005.
    53. Mirza Mansoor Baig; Hamid GholamHosseini; Martin Joseph Connolly; A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Medical & Biological Engineering & Computing 2013, 51, 485-495, 10.1007/s11517-012-1021-6.
    54. Mirza Mansoor Baig; Hamid GholamHosseini; Aasia A. Moqeem; Farhaan Mirza; Maria Lindén; A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption. Journal of Medical Systems 2017, 41, , 10.1007/s10916-017-0760-1.
    55. Hadi Banaee; Mobyen Uddin Ahmed; Amy Loutfi; Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges. Sensors 2013, 13, 17472-17500, 10.3390/s131217472.
    56. Hannah R. Marston; Robin Hadley; Duncan Banks; María Del Carmen Miranda-Duro; Mobile Self-Monitoring ECG Devices to Diagnose Arrhythmia that Coincide with Palpitations: A Scoping Review.. Healthcare 2019, 7, 96, 10.3390/healthcare7030096.
    57. Luciano A. Sposato; Lauren E. Cipriano; Gustavo Saposnik; Estefanía Ruíz Vargas; Patricia Riccio; Vladimir Hachinski; Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology 2015, 14, 377-387, 10.1016/s1474-4422(15)70027-x.
    58. Javier Tejedor; Constantino A. García; David G. Márquez; Rafael Raya; Abraham Otero; Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review. Sensors 2019, 19, 4708, 10.3390/s19214708.
    59. Maria Egger; Matthias Ley; Sten Hanke; Emotion Recognition from Physiological Signal Analysis: A Review. Electronic Notes in Theoretical Computer Science 2019, 343, 35-55, 10.1016/j.entcs.2019.04.009.
    60. M. Sabarimalai Manikandan; S. Dandapat; Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomedical Signal Processing and Control 2014, 14, 73-107, 10.1016/j.bspc.2014.07.002.
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