Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.
翻译:孟加拉国农村地区近30%的人口处于贫困线以下,此外,由于缺少现代化的保健相关技术,护理和诊断设施对农村人民来说是有限的,因此农村人民得不到适当的保健,因此,农村人民被剥夺了适当的保健;从这个角度,可以便利现代技术,减轻他们的健康问题;ECG遥感工具与人的胸部相互连接,通过IoT装置收集必要的心血管数据;这些数据与MQTT和HTTP服务器一起储存在云中;本研究报告建议了一种基于ECG的心血管或心脏病人监测系统创新的IoT方法;ECG信号参数P、Q、R、S、T得到收集、预先处理,并预测监测心血管状况,以便进一步管理保健;机器学习算法用于确定ECG信号参数和错误率的重要性;后勤回归模型符合火车和测试数据之间更好的协议;进行了预测,以确定PQRST质量的变异性及其在ECG监测系统中的适合性;考虑到质量参数的价值,取得了令人满意的结果;拟议的心血管护理系统复杂程度。