(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-10 Issue-101 April-2023
Full-Text PDF
Paper Title : A review of technologies for heart attack monitoring systems
Author Name : Rohana Abdul Karim, Noraizan Ibrahim and Nurul Wahidah Arshad
Abstract :

Every year, approximately 1.35 million people die in car accidents. One of the causes of traffic accidents is a heart attack while driving. Common heart attack warning signs are pain or discomfort in the chest or one or both arms or shoulders, light-headedness, faintness, cold sweat, and shortness of breath. When having a heart attack, a car driver has strong pain in the centre or left side of the chest. Current technology for heart attack detection is based on sensory signal properties such as the electrocardiogram (ECG), heart rate and oxygen saturation (SpO2). This paper is intended to give the readers an overview of technologies for heart attack monitoring system that has been used at the hospital, at the home and in the vehicle. The result shows that ECG, heart rate and SpO2 properties are mostly used by numerous researchers for heart attack monitoring systems at hospitals. Meanwhile, many researchers developed a system by using heart rate, ECG, SpO2 and images as properties for heart attack monitoring systems at home. Existing technologies for heart attack monitoring systems in the vehicle used heart rate and ECG as properties in a system. However, there are no review papers yet on heart attack monitoring systems using image processing in vehicles. We believe that researchers and practitioners will embrace this technology by addressing image processing in the heart attack monitoring system in vehicles.

Keywords : Heart attack, Heart rate, ECG, SpO2, Monitoring system.
Cite this article : Karim RA, Ibrahim N, Arshad NW. A review of technologies for heart attack monitoring systems. International Journal of Advanced Technology and Engineering Exploration. 2023; 10(101):395-425. DOI:10.19101/IJATEE.2022.10100250.
References :
[1]Bagul A, Gorde A, Gargade A, Amrutkar S, Mahajan M. Heart attack prediction and prevention using artificial neural network. Open Access International Journal of Science and Engineering. 2019; 4(4):1-5.
[Google Scholar]
[2]https://www.utusan.com.my/gaya/kesihatan/2022/01/kadar-kematian-sakit-jantung-dua-kali-ganda-di-musim-pandemik/. Accessed 23 January 2022.
[3]https://www.sinarharian.com.my/article/164246/BERITA/Nasional/23-peratus-rakyat-Malaysia-maut-akibat-penyakit-jantung. Accessed 21 September 2021.
[4]https://www.mot.gov.my/en/land/safety/malaysia-road-fatalities-index#:~:text=Approximately 1.35 million people die,to nations as a whole. Accessed 04 January 2022.
[5]https://www.data.gov.my/data/ms_MY/dataset/statistik-punca-kemalangan-maut/resource/7292b225-6165-48f0-bf28-2ddd92e8a7da. Accessed 05 April 2022.
[6]Rolison JJ, Regev S, Moutari S, Feeney A. What are the factors that contribute to road accidents? an assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accident Analysis & Prevention. 2018; 115:11-24.
[Crossref] [Google Scholar]
[7]Woodward MA, Musch DC, Hood CT, Greene JB, Niziol LM, Jeganathan VS, et al. Tele-ophthalmic approach for detection of corneal diseases: accuracy and reliability. Cornea. 2017; 36(10): 1159-65.
[Crossref] [Google Scholar]
[8]Xu Y, Shanthosh J, Zhou Z, Somerville E, Anderson CS, Glozier N, et al. Prevalence of driving and traffic accidents among people with seizures: a systematic review. Neuroepidemiology. 2019; 53(1-2):1-12.
[Crossref] [Google Scholar]
[9]Miao Q, Zhang YL, Miao QF, Yang XA, Zhang F, Yu YG, et al. Sudden death from ischemic heart disease while driving: cardiac pathology, clinical characteristics, and countermeasures. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research. 2021; 27:1-6.
[Crossref] [Google Scholar]
[10]Inkster B, Frier BM. Diabetes and driving. Diabetes, Obesity and Metabolism. 2013; 15(9):775-83.
[Crossref] [Google Scholar]
[11]https://newss.statistics.gov.my/newss-portalx/ep/epLogin.seam?lang=en. Accessed 04 January 2022.
[12]https://www.heart.org/en/health-topics/heart-attack/about-heart-attacks/silent-ischemia-and-ischemic-heart-disease. Accessed 04 January 2022.
[13]https://www.heart.org/en/about-us/heart-attack-and-stroke-symptoms. Accessed 04 January 2022.
[14]https://www.medicinenet.com/chest_pain_on_the_left_side_above_a_female_breast/article.htm. Accessed 04 January 2022.
[15]https://www.cdc.gov/heartdisease/heart_attack.htm. Accessed 04 January 2022.
[16]Van HJC, Rouse KL, Meyer ML, Siegler AM, Fruehauf BM, Ballance EH, et al. Knowledge of heart attack and stroke symptoms among US Native american adults: a cross-sectional population-based study analyzing a multi-year BRFSS database. BMC Public Health. 2020; 20:1-10.
[Crossref] [Google Scholar]
[17]https://www.yourheartflorida.com/news-8-warning-signs-your-body-gives-you-before-a-heart-attack.html. Accessed 04 January 2022.
[18]Birnbach B, Höpner J, Mikolajczyk R. Cardiac symptom attribution and knowledge of the symptoms of acute myocardial infarction: a systematic review. BMC Cardiovascular Disorders. 2020; 20(1):1-12.
[Crossref] [Google Scholar]
[19]Dineshkumar C, Subramanian M, Muthaya J, Deepan V. Health monitoring system for automobile vehicles to enhance safety. International Journal of Vehicle Structures & Systems. 2018; 10(6): 395-8.
[Google Scholar]
[20]Felix H, Narcisse MR, Rowland B, Long CR, Bursac Z, Mcelfish PA. Level of recommended heart attack knowledge among native hawaiian and pacific islander adults in the United States. Hawai i Journal of Medicine & Public Health. 2019; 78(2):61-5.
[Google Scholar]
[21]Rojas-albarracin G, Chaves MÁ, Fernandez-caballero A, Lopez MT. Heart attack detection in colour images using convolutional neural networks. Applied Sciences. 2019; 9(23):1-10.
[Crossref] [Google Scholar]
[22]Chowdhury ME, Alzoubi K, Khandakar A, Khallifa R, Abouhasera R, Koubaa S, et al. Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors. 2019; 19(12):1-22.
[Crossref] [Google Scholar]
[23]Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International Journal of Surgery. 2021; 88:1-4.
[Crossref] [Google Scholar]
[24]Yuce MR. Implementation of wireless body area networks for healthcare systems. Sensors and Actuators A: Physical. 2010; 162(1):116-29.
[Crossref] [Google Scholar]
[25]Mathew NA, Abubeker KM. IoT based real time patient monitoring and analysis using raspberry Pi 3. In international conference on energy, communication, data analytics and soft computing 2017 (pp. 2638-40). IEEE.
[Crossref] [Google Scholar]
[26]Baloglu UB, Talo M, Yildirim O, San TR, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters. 2019; 122:23-30.
[Crossref] [Google Scholar]
[27]Mishra S, Khatwani G, Patil R, Sapariya D, Shah V, Parmar D, et al. ECG paper record digitization and diagnosis using deep learning. Journal of Medical and Biological Engineering. 2021; 41(4):422-32.
[Crossref] [Google Scholar]
[28]Zhu H, Cheng C, Yin H, Li X, Zuo P, Ding J, et al. Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study. The Lancet Digital Health. 2020; 2(7):e348-57.
[Crossref] [Google Scholar]
[29]Huang JS, Chen BQ, Zeng NY, Cao XC, Li Y. Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks. Journal of Ambient Intelligence and Humanized Computing. 2020:1-8.
[Crossref] [Google Scholar]
[30]Ramachandran D, Thangapandian VP, Rajaguru H. Computerized approach for cardiovascular risk level detection using photoplethysmography signals. Measurement. 2020; 150:1-11.
[Crossref] [Google Scholar]
[31]Gogate U, Bakal J. Healthcare monitoring system based on wireless sensor network for cardiac patients. Biomedical & Pharmacology Journal. 2018; 11(3):1681-8.
[Crossref] [Google Scholar]
[32]Driscoll A, Grant MJ, Carroll D, Dalton S, Deaton C, Jones I, et al. The effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units: a systematic review and meta-analysis. European Journal of Cardiovascular Nursing. 2018; 17(1):6-22.
[Crossref] [Google Scholar]
[33]Rad MZ, Ghuchani SR, Bahaadinbeigy K, Khalilzadeh MM. Real time recognition of heart attack in a smart phone. Acta Informatica Medica. 2015; 23(3):151-4.
[Crossref] [Google Scholar]
[34]Shah WM, Yaakob MH, Harum N, Hassan A, Othman MF, Hamid IR. Internet of things based heart rate monitoring and alert system. Journal of Advanced Computing Technology and Application. 2020; 2(1):27-31.
[Google Scholar]
[35]Zahra IF, Wisana ID, Nugraha PC, Hassaballah HJ. Design a monitoring device for heart-attack early detection based on respiration rate and body temperature parameters. Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics. 2021; 3(3):114-20.
[Crossref] [Google Scholar]
[36]Gogate U, Marathe M, Mourya J, Mohan N. Android-based health monitoring system for cardiac patients. International research journal of engineering and technology. 2017; 4(4):1628-34.
[Google Scholar]
[37]Yang C, Veiga C, Rodriguez-andina JJ, Farina J, Iniguez A, Yin S. Using PPG signals and wearable devices for atrial fibrillation screening. IEEE Transactions on Industrial Electronics. 2019; 66(11):8832-42.
[Crossref] [Google Scholar]
[38]Subin EK, Renuka S, Chaitanya K, Sudheer AP. Implementation of signal processing filters for portable ECG devices using android mobile phone and Bluetooth. In 14th India council international conference 2017 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[39]Randazzo V, Pasero E, Navaretti S. VITAL-ECG: a portable wearable hospital. In IEEE sensors applications symposium 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[40]Simha A, Sharma S, Narayana S, Prasad RV. Heart watch: dynamical systems based real time data driven ECG Synthesis. In 7th world forum on internet of things 2021 (pp. 789-94). IEEE.
[Crossref] [Google Scholar]
[41]Randazzo V, Ferretti J, Pasero E. ECG WATCH: a real time wireless wearable ECG. In international symposium on medical measurements and applications 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[42]Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Computer Science. 2020; 1:1-11.
[Crossref] [Google Scholar]
[43]Gajbhiye S, Vyas B, Shrikhande S, Janbandhu A, Nagpure K, Agashe M. IoT based heart attack early prediction. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2019; 5:448-51.
[Google Scholar]
[44]Kazi SS, Bajantri G, Thite T. Remote heart rate monitoring system using IoT. International Research Journal of Engineering and Technology. 2018; 5(4):2956-63.
[Google Scholar]
[45]Sharma G, Kalra S. A lightweight user authentication scheme for cloud-IoT based healthcare services. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. 2019; 43:619-36.
[Crossref] [Google Scholar]
[46]Gusev M, Stojmenski A, Guseva A. ECGalert: a heart attack alerting system. In ICT innovations: data-driven innovation, 9th international conference, ICT Innovations, Skopje, Macedonia, Proceedings 2017 (pp. 27-36). Springer International Publishing.
[Crossref] [Google Scholar]
[47]Karajah EA, Ishaq I. Online monitoring health station using Arduino mobile connected to cloud service: “heart monitor” system. In international conference on promising electronic technologies 2020 (pp. 38-43). IEEE.
[Crossref] [Google Scholar]
[48]Alam MA, Shakir MB, Pavel MI. Early detection of coronary artery blockage using image processing: segmentation, quantification, identification of degree of blockage and risk factors of heart attack. In micro-and nanotechnology sensors, systems, and applications XI 2019 (pp. 80-9). SPIE.
[Crossref] [Google Scholar]
[49]Almathami HK, Win KT, Vlahu-gjorgievska E. Barriers and facilitators that influence telemedicine-based, real-time, online consultation at patients’ homes: systematic literature review. Journal of Medical Internet Research. 2020; 22(2):1-25.
[Crossref] [Google Scholar]
[50]Landers S, Madigan E, Leff B, Rosati RJ, Mccann BA, Hornbake R, et al. The future of home health care: a strategic framework for optimizing value. Home Health Care Management & Practice. 2016; 28(4):262-78.
[Crossref] [Google Scholar]
[51]Sorwar G, Ali M, Islam M, Miah MS. An integrated patient information and in-home health monitoring system using smartphones and web services. In the promise of new technologies in an age of new health challenges 2016 (pp. 119-26). IOS Press.
[Google Scholar]
[52]Kalyan SS, Baig NS, Tej DS, Mahendra A. Heart function monitoring and prevention of heart attack using internet of things. In journal of physics: conference series 2021 (pp. 1-10). IOP Publishing.
[Crossref] [Google Scholar]
[53]Jeong JW, Lee W, Kim YJ. A real-time wearable physiological monitoring system for home-based healthcare applications. Sensors. 2022; 22(1):1-14.
[Crossref] [Google Scholar]
[54]https://www.heart.org/en/news/2020/05/15/after-heart-attack-home-health-care-could-help-prevent-return-to-hospital#:~:text=Heart attack survivors who receive,month%2C according to preliminary research. Accessed 04 January 2022.
[55]Polu SK, Polu SK. Design of an IoT based heart attack detection system. International Journal for Innovative Research in Science & Technology. 2019; 5:53-7.
[Google Scholar]
[56]Jalal A, Kamal S, Kim D. A depth video-based human detection and activity recognition using multi-features and embedded hidden Markov models for health care monitoring systems. International Journal of Interactive Multimedia and Artificial Intelligence. 2017; 4(4):54-62.
[Crossref] [Google Scholar]
[57]Ghanadian H, Ghodratigohar M, Al OH. A machine learning method to improve non-contact heart rate monitoring using an RGB camera. IEEE Access. 2018; 6:57085-94.
[Crossref] [Google Scholar]
[58]https://www.hopkinsmedicine.org/health/conditions-and-diseases/vital-signs-body-temperature-pulse-rate-respiration-rate-blood-pressure. Accessed 04 January 2022.
[59]Sasidharan P, Rajalakshmi T, Snekhalatha U. Wearable cardiorespiratory monitoring device for heart attack prediction. In international conference on communication and signal processing 2019 (pp. 54-7). IEEE.
[Crossref] [Google Scholar]
[60]Patil M, Madankar A, Khandait PD. Heart rate monitoring system. In 5th international conference on advanced computing & communication systems 2019 (pp. 574-6). IEEE.
[Crossref] [Google Scholar]
[61]Li C, Hu X, Zhang L. The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science. 2017; 112:2328-34.
[Crossref] [Google Scholar]
[62]Prema S, Sankar T, Jhanani MM, Gnanamani M, Baskaran M. Heart attack intimation and smart traffic control system using IOT. International Advanced Research Journal in Science, Engineering and Technology. 2020; 7(5):108-12.
[Crossref] [Google Scholar]
[63]Kora P, Rajani A, Chinnaiah MC, Swaraja K, Meenakshi K. IOT based wearable monitoring structure for detecting abnormal heart. In international conference on sustainable energy and future electric transportation 2021(pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[64]Balakrishnand D, Rajkumart TD, Dhanasekaran S. An intelligent and secured heart rate monitoring system using IOT. Materials Today: Proceedings. 2020:1-4.
[Crossref] [Google Scholar]
[65]Gujale P, Kamble S, Mane A, Waman A, Mohite R. Heart attack detection and heart rate monitoring using IOT. International Research Journal of Innovations in Engineering and Technology. 2020; 4(5):132-5.
[Google Scholar]
[66]Kappiarukudil KJ, Ramesh MV. Real-time monitoring and detection of heart attack using wireless sensor networks. In fourth international conference on sensor technologies and applications 2010 (pp. 632-6). IEEE.
[Crossref] [Google Scholar]
[67]Teixeira E, Fonseca H, Diniz-sousa F, Veras L, Boppre G, Oliveira J, et al. Wearable devices for physical activity and healthcare monitoring in elderly people: a critical review. Geriatrics. 2021; 6(2):1-19.
[Crossref] [Google Scholar]
[68]Ahmed F. An internet of things (IoT) application for predicting the quantity of future heart attack patients. International Journal of Computer Applications. 2017; 164(6):36-40.
[Google Scholar]
[69]Prittopaul P, Sathya S, Jayasree K. Cyber physical system approach for heart attack detection and control using wireless monitoring and actuation system. In 9th international conference on intelligent systems and control 2015 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[70]Gupta K, Kaul P, Kaur A. An efficient algorithm for heart attack detection using fuzzy C-means and alert using IoT. In 4th international conference on computational intelligence & communication technology 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[71]Valliappan S, Mohan BP, Kumar SR. Design of low-cost, wearable remote health monitoring and alert system for elderly heart patients. In international conference on IoT and application 2017 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[72]Yu L, Chan WM, Zhao Y, Tsui KL. Personalized health monitoring system of elderly wellness at the community level in Hong Kong. IEEE Access. 2018; 6:35558-67.
[Crossref] [Google Scholar]
[73]Lee JV, Chuah YD, Chieng KT. Smart elderly home monitoring system with an android phone. International Journal of Smart Home. 2013; 7(3):17-32.
[Google Scholar]
[74]Kańtoch E, Jaworek J, Augustyniak P. Design of a wearable sensor network for home monitoring system. In federated conference on computer science and information systems 2011 (pp. 401-3). IEEE.
[Google Scholar]
[75]Elakkiya T. Wearable safety wristband device for elderly health monitoring with fall detect and heart attack alarm. In third international conference on science technology engineering & management 2017 (pp. 1018-22). IEEE.
[Crossref] [Google Scholar]
[76]Sugathan A, Roy GG, Kirthyvijay GJ, Thomson J. Application of Arduino based platform for wearable health monitoring system. In 1st international conference on condition assessment techniques in electrical systems 2013 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[77]Iqbal Z, Ilyas R, Shahzad W, Inayat I. A comparative study of machine learning techniques used in non-clinical systems for continuous healthcare of independent livings. In symposium on computer applications & industrial electronics (ISCAIE) 2018 (pp. 406-11). IEEE.
[Crossref] [Google Scholar]
[78]Savla DV, Parekh S, Gupta AR, Agarwal D, Shekokar NM. Resq-smart safety band automated heart rate and fall monitoring system. In fourth international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) 2020 (pp. 588-93). IEEE.
[Crossref] [Google Scholar]
[79]Chaudhary S, Selvakumar K, Scope V. Embedded system in passenger car with medical safety and alert system. International Research Journal in Advanced Engineering and Technology. 2018; 4(2):3092-5.
[Google Scholar]
[80]Abu-faraj ZO, Al CW, Al HA, Sraj Y, Tannous J. Design and development of a heart-attack detection steering wheel. In 11th international congress on image and signal processing, biomedical engineering and informatics 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[81]Singh RK, Sarkar A, Anoop CS. A health monitoring system using multiple non-contact ECG sensors for automotive drivers. In international instrumentation and measurement technology conference proceedings 2016 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[82]Hemaanand M, Rakesh CP, Darshan S, Jagadeeswaran S, Karthika R, Parameswaran L. Advanced driver assistance system using computer vision and IOT. In computational vision and bio-inspired computing 2020 (pp. 768-78). Springer International Publishing.
[Crossref] [Google Scholar]
[83]Emarose S, Asokan R, Valayaputtur D. Continuous monitoring of heart rate variability and haemodynamic stability of an automobile driver to prevent road accidents. In third international conference on computing, communication and networking technologies 2012 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[84]Lee JC, Liu H. Development of a real-time driver health detection system using a smart steering wheel. International Journal of Prognostics and Health Management. 2018; 9(3):1-5.
[Crossref] [Google Scholar]
[85]Nirbhavane M, Prabha S. Accident monitoring system using wireless application. International Journal of Advanced Research in Computer Engineering & Technology. 2014; 3(4):1532-5.
[Google Scholar]
[86]Sinnapolu G, Alawneh S. Integrating wearables with cloud-based communication for health monitoring and emergency assistance. Internet of Things. 2018; 1:40-54.
[Crossref] [Google Scholar]
[87]Jowkar AM, Dudhe R, Dsouza AA. Smart and safe cars using multi-sensor solution. In 2019 international conference on digitization 2019 (pp. 268-71). IEEE.
[Crossref] [Google Scholar]
[88]Leem SK, Khan F, Cho SH. Vital sign monitoring and mobile phone usage detection using IR-UWB radar for intended use in car crash prevention. Sensors. 2017; 17(6):1-25.
[Crossref] [Google Scholar]
[89]Fouad RM, Onsy A, Omer OA. Improvement of driverless cars passengers on board health and safety, using low-cost real-time heart rate monitoring system. In 24th international conference on automation and computing 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[90]Xiong Z, Wang J, Jin W, Liu J, Duan Y, Song Z, et al. Face2Statistics: user-friendly, low-cost and effective alternative to in-vehicle sensors/monitors for drivers. In HCI in mobility, transport, and automotive systems: 4th international conference, Proceedings 2022 (pp. 289-308). Cham: Springer International Publishing.
[Crossref] [Google Scholar]
[91]Yang PC, Cheng JH, Tu MS, Tseng CH. A smartphone-based heart rate variability analysis system for vehicle drivers. In 12th international conference on ITS telecommunications 2012 (pp. 827-31). IEEE.
[Crossref] [Google Scholar]
[92]Mekaladevi V, Mohankumar N. Real-time heart rate abnormality detection using ECG for vehicle safety. In third international conference on inventive systems and control 2019 (pp. 601-4). IEEE.
[Crossref] [Google Scholar]
[93]Wang Q, Wang Z, Dai X, Song S, Xing T. S-HRVM: smart watch-based heart rate variability monitoring system. In EWSN 2019 (pp. 178-83).
[Google Scholar]
[94]Lee BG, Lee BL, Chung WY. Wristband-type driver vigilance monitoring system using smartwatch. IEEE Sensors Journal. 2015; 15(10):5624-33.
[Crossref] [Google Scholar]
[95]Kumar KG, Arvind R, Keerthan PB, Kumar SA, Dass PA. Wireless methodology of heart attack detection. International Journal for Scientific Research and Development. 2014; 2:673-6.
[Google Scholar]
[96]Ali S, Ghazal M. Real-time heart attack mobile detection service (RHAMDS): an IoT use case for software defined networks. In 30th Canadian conference on electrical and computer engineering 2017 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[97]Dhanalakshmi G, Jeevana JK, Naveena B. Raspberry Pi-based heart attack and alcohol alert system over internet of things for secure transportation. In computer networks and inventive communication technologies 2022 (pp. 423-37). Springer Singapore.
[Crossref] [Google Scholar]
[98]Shaik A, Bowen N, Bole J, Kunzi G, Bruce D, Abdelgawad A, et al. Smart car: an IoT based accident detection system. In global conference on internet of things 2018 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[99]Yue W, Voronova LI, Voronov VI. Design and implementation of a remote monitoring human health system. In systems of signals generating and processing in the field of on board communications 2020 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[100]Francis A, Wilfred MW, Sekar R. Health monitoring with alcohol detection and ignition control system using IoT. International Journal of Innovative Technology and Exploring Engineering. 2019; 8:203-6.
[Google Scholar]
[101]Ananth C, Shalaysha S, Vaishnavi M, Rabiyathul SJ, Sangeetha AP, Santhi M. Realtime monitoring of cardiac patients at distance using tarang communication. International Journal of Innovative Research in Engineering & Science. 2014; 9(3):1-6.
[Crossref] [Google Scholar]
[102]Sankar PG, Ilamathy MR, Krithiga MA, Sripavathaarani MS, Vigneshwaran MK. Development of a driver-vehicle-interface system for detection of heart attack, alcohol &drowsiness detection and warning system. Wutan Hutan Jisuan Jishu. 2020; XVI(V):151–9.
[Google Scholar]
[103]Kavitha KC, Perumalraja R. Smart wireless healthcare monitoring for drivers community. In international conference on communication and signal processing 2014 (pp. 1105-8). IEEE.
[Crossref] [Google Scholar]
[104]Sinnapolu G, Alawneh S. Intelligent wearable heart rate sensor implementation for in-vehicle infotainment and assistance. Internet of Things. 2020; 12(2020):1-12.
[Crossref] [Google Scholar]
[105]Yadav Y, Gowda MS. Heart rate monitoring and heart attack detection using wearable device. International Journal for Technical Research and Application. 2016; 4(3):48-50.
[Google Scholar]
[106]Wartzek T, Eilebrecht B, Lem J, Lindner HJ, Leonhardt S, Walter M. ECG on the road: robust and unobtrusive estimation of heart rate. IEEE Transactions on Biomedical Engineering. 2011; 58(11):3112-20.
[Crossref] [Google Scholar]
[107]Murugan S, Selvaraj J, Sahayadhas A. Detection and analysis: driver state with electrocardiogram (ECG). Physical and Engineering Sciences in Medicine. 2020; 43(2):525-37.
[Crossref] [Google Scholar]
[108]Leicht L, Skobel E, Mathissen M, Leonhardt S, Weyer S, Wartzek T, et al. Capacitive ECG recording and beat-to-beat interval estimation after major cardiac event. In 37th annual international conference of the IEEE engineering in medicine and biology society 2015 (pp. 7614-7). IEEE.
[Crossref] [Google Scholar]
[109]Zaghouani EK, Benzina A, Attia R. ECG based authentication for e-healthcare systems: towards a secured ECG features transmission. In 13th international wireless communications and mobile computing conference 2017 (pp. 1777-83). IEEE.
[Crossref] [Google Scholar]
[110]Mazidi MH, Eshghi M. Detection of heart attack using cross wavelet transformation and support vector machine. Applied Medical Informatics. 2019; 41(3):77-92.
[Google Scholar]
[111]Kaur P, Saini HS, Kaur B. Wearable sensors for monitoring vital signs of patients. International Journal of Engineering & Technology. 2018; 7:62-5.
[Google Scholar]
[112]Lin KF, Lin SS, Chen PN. A low-cost physiological monitoring interface for intensive care unit. Research Square. 2022; 1-22.
[Crossref] [Google Scholar]
[113]Jayanthi G, Brindha S, Umamaheswari B, Gayathri R, Vishalakshi M. Smart heart health monitoring system using IoT. In international conference on communication, computing and internet of things 2022 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[114]Tariq T, Latif RM, Farhan M, Abbas A, Ijaz F. A smart heart beat analytics system using wearable device. In 2nd international conference on communication, computing and digital systems 2019 (pp. 137-42). IEEE.
[Crossref] [Google Scholar]
[115]Bansal M, Gandhi B. IoT based smart health care system using CNT electrodes (for continuous ECG monitoring). In international conference on computing, communication and automation 2017 (pp. 1324-9). IEEE.
[Crossref] [Google Scholar]
[116]Chandurkar S, Arote S, Chaudhari S, Kakade V. The system for early detection of heart-attack. International Journal of Computer Applications. 2018; 182(27):30-3.
[Google Scholar]
[117]Zhang J, Wu Y, Chen Y, Chen T. Health-radio: towards contactless myocardial infarction detection using radio signals. IEEE Transactions on Mobile Computing. 2020; 21(2):585-97.
[Crossref] [Google Scholar]
[118]Bhagchandani K, Augustine DP. IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India. International Journal of Electrical and Computer Engineering. 2019; 9(6):5068-74.
[Crossref] [Google Scholar]
[119]Masood A, Khan KB, Younas T, Khalid AR. Design of wearable prototype smart wristband for remote health monitoring using internet of things. In intelligent technologies and applications: second international conference, Bahawalpur, Pakistan 2020 (pp. 3-13). Springer Singapore.
[Crossref] [Google Scholar]
[120]Arppana AR, Reshmma NK, Raghu G, Mathew N, Nair HR, Aneesh RP. Real time heart beat monitoring using computer vision. In seventh international conference on bio signals, images, and instrumentation 2021 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[121]Deepika D, Shubhangi DC. The heart attack detection system and reporting over web sever using arduino mega & node MCU. International Journal for Research in Applied Science & Engineering Technology. 2020; 8(VII):722-6.
[Google Scholar]
[122]Kumari KV, Sarma G. IoT based heart attack and alcohol detection in smart transportation and accident prevention for vehicle drivers. International Journal for Recent Development in Science and Technology. 2020; 4:179-85.
[Google Scholar]
[123]Khan IA, Ahmed SZ, Iqbal M. Driver safety system for drowsiness, heart attack, object detection, and internal temperature control of car with real-time wireless communication. Turkish Online Journal of Qualitative Inquiry. 2021; 12(8):7356-63.
[Google Scholar]
[124]Kathirvelu K, Arif MK. Integrated machine learning framework for automated vehicle monitoring system for CVD patients. Mathematical Statistician and Engineering Applications. 2022; 71(4):4547-57.
[Google Scholar]
[125]Vyshnavi M, Narendar B. Hybrid driver safety, vigilance, security and alerting system for vehicle. International Journal of Scientific Engineering and Technology. 2017; 6(31):1-3.
[Google Scholar]
[126]Banoth R, Godishala AK, Veena R, Yassin H. A healthcare monitoring system for predicting heart disease through recurrent neural network. In 7th international conference for convergence in technology 2022 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[127]Mary TM, Ramanathan G, Sangamithra A, Soumya K. Design and enactment of heart attack deduction using IOT measuring device. In journal of physics: conference series 2020 (pp. 1-6). IOP Publishing.
[Crossref] [Google Scholar]
[128]Rahil MR, Waleed M, Almajid S, Bucheeri N, Bahri Z. Design and implementation of a cost-effective smart heart monitoring system. In 3rd smart cities symposium 2020 (pp. 512-5). IET.
[Crossref] [Google Scholar]
[129]Shihab AN, Mokarrama MJ, Karim R, Khatun S, Arefin MS. An IoT-based heart disease detection system using RNN. In image processing and capsule networks: ICIPCN 2021 (pp. 535-45). Springer International Publishing.
[Crossref] [Google Scholar]
[130]Mamun MI, Rahman A, Khaleque MA, Mridha MF, Hamid MA. Healthcare monitoring system inside self-driving smart car in 5g cellular network. In 17th international conference on industrial informatics 2019 (pp. 1515-20). IEEE.
[Crossref] [Google Scholar]
[131]Anjana KK, Kathriarachchi RP, Kulasekara DM. Internet of things based falls detection and heart attack detection system for adults: smart wearable. In 11th international research conference. 2018 (pp. 301-9).
[Google Scholar]
[132]Giri S, Kumar U, Sharma V, Kumar S, Kumari S, Kumar R, et al. IoT based heart attack detection & heart rate monitoring system. In international conference on recent trends in artificial intelligence, IoT, smart cities & application, Jharkhand, India 2020 (pp. 1-4).
[Google Scholar]
[133]Khamitkar SS, Rafi M. IoT based system for heart rate monitoring. International Journal of Engineering Research & Technology. 2020; 9(7):1563-71.
[Google Scholar]
[134]Buthelezi NM, Malele V, Owolawi PA, Mapayi T. IoT based health monitor for remote patient. In international conference on artificial intelligence, big data, computing and data communication systems 2022 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[135]Sarmah SS. An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. IEEE Access. 2020; 8:135784-97.
[Crossref] [Google Scholar]
[136]Darmawahyuni A, Nurmaini S. Deep learning with long short-term memory for enhancement myocardial infarction classification. In 6th international conference on instrumentation, control, and automation 2019 (pp. 19-23). IEEE.
[Crossref] [Google Scholar]
[137]Ray A, Ray H. Secured pulse rate monitoring system using IoT and cloud. In international conference on emerging frontiers in electrical and electronic technologies 2020 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[138]Badhon MR, Barai AR, Zhora F. A microcontroller based missing heartbeat detection and real time heart rate monitoring system. In international conference on electrical, computer and communication engineering 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[139]Gurjar A, Sarnaik NA. Heart attack detection by heartbeat sensing using internet of things: IoT. International Research Journal of Engineering and Technology. 2018; 5(3):3332-5.
[Google Scholar]
[140]Mihiranga A, Shane D, Indeewari B, Udana A, Nawinna D, Attanayaka B. Digital tool for prevention, identification and emergency handling of heart attacks. In 9th region 10 humanitarian technology conference 2021 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[141]Kaviya V, Suresh GR. Intelligent wearable device for early detection of myocardial infarction using IoT. In sixth international conference on bio signals, images, and instrumentation 2020 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[142]Sethuraman TV, Rathore KS, Amritha G, Kanimozhi G. IoT based system for heart rate monitoring and heart attack detection. International Journal of Engineering and Advanced Technology. 2019; 8(5):1459-64.
[Google Scholar]
[143]Patil C, Chaware A. Heart (pulse rate) monitoring using pulse rate sensor, piezo electric sensor and NodeMCU. In 8th international conference on computing for sustainable global development 2021 (pp. 337-40). IEEE.
[Google Scholar]
[144]Raihan M, Mondal S, More A, Boni PK, Sagor MO. Smartphone based heart attack risk prediction system with statistical analysis and data mining approaches. Advances in Science, Technology and Engineering Systems Journal. 2017; 2(3):1815-22.
[Google Scholar]
[145]Sahil M, Vasudev KL, Abhiram A, Basha SH. Design and fabrication of threat alerting system for continuous monitoring of patients with seizure and heart attack risk using IoT. In industrial electronics and applications conference 2022 (pp. 218-22). IEEE.
[Crossref] [Google Scholar]
[146]Yusuf SS, Fallon S, Cawley D, Jacob P. An IoT system for post-myocardial infarction patients during cardiac rehabilitation (CR) in Indonesia. In 33rd Irish signals and systems conference 2022 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[147]Sethi V, Katal A, Dabas S, Kumar S. Heart attack detector: an IoT based solution integrated with cloud. In international conference on computing communication and networking technologies 2022 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[148]Haritha A, Nazar S, Vannemreddy KSS, Vishwanandana S, Sowdhamini G. IoT based heart attack detection and heart rate monitoring system using raspberry pi. International Journal of Creative Research Thoughts. 2022; 10(5):82–5.
[149]Munagala NK, Langoju LR, Rani AD, Reddy DR. A smart IoT-enabled heart disease monitoring system using meta-heuristic-based fuzzy-LSTM model. Biocybernetics and Biomedical Engineering. 2022; 42(4):1183-204.
[Crossref] [Google Scholar]
[150]Qtaish A, Al-shrouf A. A portable IoT-cloud ECG monitoring system for healthcare. International Journal of Computer Science and Network Security. 2022; 22(1):269-75.
[Google Scholar]
[151]Kanumuri CR, Varma KP, Harishvarma A, Kumar MP, Chandra TR. Implementation of non-invasive glucose monitoring system along with ECG, SPO2 with the help of IoT. In 6th international conference on devices, circuits and systems 2022 (pp. 348-52). IEEE.
[Crossref] [Google Scholar]
[152]Mamun MM. Significance of features from biomedical signals in heart health monitoring. BioMed. 2022; 2(4):391-408.
[Crossref] [Google Scholar]
[153]Rizwan M, Arshad S, Aijaz H, Khan RA, Haque MZ. Heart attack prediction using machine learning approach. In third international conference on latest trends in electrical engineering and computing technologies 2022 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[154]Mazhar T, Nasir Q, Haq I, Kamal MM, Ullah I, Kim T, et al. A novel expert system for the diagnosis and treatment of heart disease. Electronics. 2022; 11(23):31-16.
[Crossref] [Google Scholar]
[155]Negi P, Bisht MK. Analysis and prediction of heart attack using machine learning models. In 7th international conference on computing, communication and security 2022 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[156]Janaraniani N, Divya P, Madhukiruba E, Santhosh R, Reshma R, Selvapandian D. Heart attack prediction using machine learning. In 4th international conference on inventive research in computing applications 2022 (pp. 854-60). IEEE.
[Crossref] [Google Scholar]
[157]Saxena A, Kumar M, Tyagi P, Sikarwar K, Pathak A. Machine learning based selection of myocardial complications to predict heart attack. In 9th Uttar Pradesh section international conference on electrical, electronics and computer engineering 2022 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[158]Ahmed Z, Irtaza A, Mehmood A, Saleem MF. An improved deep learning approach for heart attack detection from digital images. In international conference on frontiers of information technology 2022 (pp. 261-6). IEEE.
[Crossref] [Google Scholar]
[159]Zhu L, Goldsztein G. Predicting the chance of heart attack with a machine learning approach–supervised learning. Journal of Student Research. 2022; 11(3):1-6.
[Crossref] [Google Scholar]