Home / Current Issue / Paper 1704590
A Review of Health Monitoring Systems for Cardiovascular Related Diseases: Processes and Techniques
Subject area: Science,Engineering and Technology · Area of research: Health
Abstract
The increased number of elderly people and the growing population of cardiovascular-related diseases have put more strain on the already ill-fated health facilities in developing countries. One approach to alleviate this problem is to employ a sustainable healthcare system that monitors and manages the patient remotely and in a real-time manner. Many researchers across the globe have invested in the design and development of monitoring systems for cardiovascular-related diseases. However, designing such a system requires taking a holistic view of the design lifecycle. The paper aims to conduct a holistic review of the existing literature to identify techniques and discuss approaches used at each stage of development. Also, this paper examines existing research on a monitoring system for cardiovascular-related diseases to identify the stages used in the development cycle with the view to identifying gaps in the existing literature.
Keywords
Monitoring System, Cardiovascular, Technology, Processes
References
[1] S. Ike and C. Onyema, “Cardiovascular diseases in Nigeria: What has happened in the past 20 years?,” Niger. J. Cardiol., vol. 17, no. 1, p. 21, 2020, doi: 10.4103/njc.njc_33_19.
[2] S. Melville et al., “HHS Public Access,” vol. 21, no. 2, 2020, doi: 10.1007/s11906-019-0921-3.Personalized.
[3] J. Lin et al., “Review Wearable sensors and devices for real-time cardiovascular disease monitoring,” Cell Reports Phys. Sci., vol. 2, no. 8, p. 100541, 2021, doi: 10.1016/j.xcrp.2021.100541.
[4] B. Singh, “Blood Pressure Monitoring System using Wireless technologies Blood Pressure Monitoring System using Wireless technologies,” Procedia Comput. Sci., vol. 152, pp. 267–273, 2019, doi: 10.1016/j.procs.2019.05.017.
[5] B. B. P. Measurement, “Wearable Piezoelectric-Based System for Continuous Beat-to-Beat Blood Pressure Measurement,” pp. 1–12, 2020.
[6] E. Adejumo, “Health Monitoring System for Post-Stroke Management,” no. January, pp. 1–10, 2019, doi: 10.5815/ijieeb.2019.01.01.
[7] I. Boukhennoufa, X. Zhai, V. Utti, J. Jackson, and K. D. Mcdonald-maier, “Biomedical Signal Processing and Control Wearable sensors and machine learning in post-stroke rehabilitation assessment : A systematic review,” Biomed. Signal Process. Control, vol. 71, no. PB, p. 103197, 2022, doi: 10.1016/j.bspc.2021.103197.
[8] C. Carrarini et al., “OPEN ECG monitoring of post ‑ stroke occurring arrhythmias : an observational study using 7 ‑ day Holter ECG,” Sci. Rep., pp. 1–7, 2022, doi: 10.1038/s41598-021-04285-6.
[9] P. Chen et al., “Sleep Monitoring during Acute Stroke Rehabilitation : Toward Automated Measurement Using Multimodal Wireless Sensors,” sensor, pp. 1–15, 2022.
[10] V. Prakash, “Heart rate monitoring system,” no. May 2018, 2019.
[11] S. Fiber, N. Irawati, A. M. Hatta, Y. Gita, and Y. Yhuwana, “Heart Rate Monitoring Sensor Based on,” vol. 10, no. 2, pp. 186–193, 2020, doi: 10.1007/s13320-019-0572-7.
[12] G. M. Esther, A. Sobitha, and K. P. Hari, “Coronary Heart Disease ( Cad ) Monitoring System Based On Wireless Sensors,” J. Phys., pp. 0–9, 2019, doi: 10.1088/1742-6596/1362/1/012045.
[13] P. R. Sumant, F. V. Jesse, and J. B. Jasper, “Invasive Devices and Sensors for Remote Care of Heart Failure Patients,” sensors Rev., 2021.
[14] A. A. E. Shoka, M. M. Dessouky, and A. El-sayed, “Literature Review on EEG Preprocessing , Feature Extraction , and Classifications Techniques,” no. January 2020, 2019, doi: 10.21608/mjeer.2019.64927.
[15] S. B. Wesam, “Review of Data Preprocessing Techniques in Data Mining,” J. Eng. Appl. Sci., vol. 12, no. 16, pp. 4102–4107, 2017.
[16] M. A. Khan and F. Algarni, “A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS,” IEEE Access, vol. 8, pp. 122259–122269, 2020, doi: 10.1109/ACCESS.2020.3006424.
[17] Y. Choi et al., “Machine-Learning-Based Elderly Stroke Monitoring System Using Electroencephalography Vital Signals,” 2021.
[18] A. A. Nancy, D. Ravindran, P. M. D. R. Vincent, K. Srinivasan, and D. G. Reina, “IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning,” 2022.
[19] M. B. Alazzam, F. Alassery, and A. Almulihi, “Research Article A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things,” vol. 2021, 2021.
[20] F. Miao et al., “Continuous Blood Pressure Measurement from One-Channel Electrocardiogram Signal Using Deep-Learning Techniques,” 2020, doi: 10.1016/j.artmed.2020.101919.
[21] F. Ali et al., “An Intelligent Healthcare Monitoring Framework Using Wearable Sensors and Social Networking Data,” Futur. Gener. Comput. Syst., 2020, doi: 10.1016/j.future.2020.07.047.
[22] F. Ali et al., “A Smart Healthcare Monitoring System for Heart Disease Prediction Based On Ensemble Deep Learning and Feature Fusion,” Inf. Fusion, 2020, doi: 10.1016/j.inffus.2020.06.008.
[23] H. Banaee, M. Uddin Ahmed, and A. Loutfi, “Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges,” Sensors, vol. 13, pp. 17472–17500, 2013, doi: 10.3390/s131217472.
[24] M. Pourhomayoun, L. Angeles, M. Shakibi, and L. Angeles, “Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical,” vol. 19, no. February, 2020.
[25] W. Chang, Y. Liu, Y. Xiao, X. Yuan, X. Xu, and S. Zhang, “A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data Wenbing,” Diagnostics, vol. 9, no. 178, 2019.
[26] Y. Liang, Z. Chen, R. Ward, and M. Elgendi, “Hypertension Assessment Using Photoplethysmography : A Risk Stratification Approach,” 2018, doi: 10.3390/jcm8010012.
[27] Q. Pan, D. Brulin, and E. Campo, “Home sleep monitoring based on wrist movement data processing,” Procedia Comput. Sci., vol. 183, pp. 696–705, 2021, doi: 10.1016/j.procs.2021.02.117.
[28] Y. A. Choi et al., “Machine-learning-based elderly stroke monitoring system using electroencephalography vital signals,” Appl. Sci., vol. 11, no. 4, pp. 1–18, 2021, doi: 10.3390/app11041761.
[29] S. Akhbarifar, H. Haj, and S. Javadi, “A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment,” Pers. Ubiquitous Comput., 2020.
[30] F. Amitrano et al., “Design and validation of an e-textile-based wearable sock for remote gait and postural assessment,” Sensors (Switzerland), vol. 20, no. 22, pp. 1–20, 2020, doi: 10.3390/s20226691.
[31] S. H. Chae, Y. Kim, K. S. Lee, and H. S. Park, “Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: Prospective comparative study,” JMIR mHealth uHealth, vol. 8, no. 7, 2020, doi: 10.2196/17216.
[32] C. N. Egejuru, O. Ogunlade, and A. P. Idowu, “Development of a Mobile-Based Hypertension Risk Monitoring System,” Int. J. Inf. Eng. Electron. Bus., vol. 11, no. 4, pp. 11–23, 2019, doi: 10.5815/ijieeb.2019.04.02.
[33] Y. Karaca, M. Moonis, Y. D. Zhang, and C. Gezgez, “Mobile cloud computing based stroke healthcare system,” Int. J. Inf. Manage., vol. 45, no. November 2017, pp. 250–261, 2019, doi: 10.1016/j.ijinfomgt.2018.09.012.
[34] B. D. Satoto, A. Yasid, M. A. Syakur, and M. Yusuf, “Wireless health monitoring with fuzzy decision tree for the community patients of chronic hypertension,” J. Phys. Conf. Ser., vol. 1211, no. 1, 2019, doi: 10.1088/1742-6596/1211/1/012041.
[35] D. J. S. Sako, J. Palimote, and P. Harcourt, “A Medical Document Classification System for Heart Disease Diagnosis Using Naïve Bayesian Classifier,” vol. 4, no. 1, pp. 69–79, 2018.
[36] S. P. Chatrati et al., “Smart home health monitoring system for predicting type 2 diabetes and hypertension,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020, doi: 10.1016/j.jksuci.2020.01.010.
[37] S. Nashif, R. Raihan, R. Islam, and M. H. Imam, “Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System,” pp. 854–873, 2018, doi: 10.4236/wjet.2018.64057.
[38] M. Umer, S. Sadiq, H. Karamti, W. Karamti, R. Majeed, and M. Nappi, “IoT Based Smart Monitoring of Patients ’ with Acute Heart Failure,” pp. 1–18, 2022.
[39] F. Miao et al., “Continuous Blood Pressure Measurement from One-Channel Electrocardiogram Signal Using Deep-Learning Techniques,” 2020, doi: 10.1016/j.artmed.2020.101919.
[40] D. D. Miller, “Machine Intelligence in Cardiovascular Medicine,” Cardiol. Rev., vol. 28, no. 2, 2020, doi: 10.1097/CRD.0000000000000294.
[41] M. A. Thar, B. Carl, M. Asim, H. Kolivand, M. Fahim, and A. Waraich, “Remote health monitoring of elderly through wearable sensors,” pp. 24681–24706, 2019.
[42] C. Raj, C. Jain, and W. Arif, “HEMAN: Health monitoring and nous: An IoT based e-health care system for remote telemedicine,” Proc. 2017 Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2017, vol. 2018-Janua, pp. 2115–2119, 2018, doi: 10.1109/WiSPNET.2017.8300134.
[43] A. H. Ali, A. H. Duhis, N. A. Lafta Alzurfi, and M. J. Mnati, “Smart monitoring system for pressure regulator based on IOT,” Int. J. Electr. Comput. Eng., vol. 9, no. 5, pp. 3450–3456, 2019, doi: 10.11591/ijece.v9i5.pp3450-3456.
How to cite this paper
@article{1704590,
author = {ATABO Onuche Gideon},
title = {A Review of Health Monitoring Systems for Cardiovascular Related Diseases: Processes and Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {1335-1342},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704590.pdf},
abstract = {The increased number of elderly people and the growing population of cardiovascular-related diseases have put more strain on the already ill-fated health facilities in developing countries. One approach to alleviate this problem is to employ a sustainable healthcare system that monitors and manages the patient remotely and in a real-time manner. Many researchers across the globe have invested in the design and development of monitoring systems for cardiovascular-related diseases. However, designing such a system requires taking a holistic view of the design lifecycle. The paper aims to conduct a holistic review of the existing literature to identify techniques and discuss approaches used at each stage of development. Also, this paper examines existing research on a monitoring system for cardiovascular-related diseases to identify the stages used in the development cycle with the view to identifying gaps in the existing literature.},
keywords = {Monitoring System, Cardiovascular, Technology, Processes},
month = {June},
}