Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor


  • Aqeel M.Hamad University of Thi-Qar, Iraq
  • Ammar D. Jasim Al-Nahrain University, Iraq


IOT, ECG, CNN, Cloud.



Real time monitoring with IOT is developed in the industry of health care , this can enable the doctors and specialist to diagnosis the patient status in quick, smart and efficient methods. Although, there is a lot of research and studies are designed methods for observing the ECG signal remotely, there are no proposed methods for classifying these signals with monitoring, and therefore , to design complete health care system, classification techniques should be used to classify the extracted signal. In this paper , We have proposed ECG monitoring and classification system. The proposed system is extracted  ECG signal based on AD8232 sensor  with the ardunino nodeMcu, analog to digital converter and its communication is used to convert the signal to more precision , then  the extracted signal is transmitted to cloud to be used at anywhere by using cloud, the signal is pre-processed to remove the noise and QRS complex is detected to determine the other characteristics of the signal such as heart rate, also to determine one cycle of ECG signal, later the signal is classified by using proposed  convolution neural network model  to detect the signal status. The extracted ECG signal is transmitted in real time to cloud (Ubidots cloud is used) through ESP8266 over to the cloud using WiFi based on MQTT publishing method. The experimental results are performed on different signals and the different stage of de-noising and QRS detection are applied and  different pooling layers are used in the proposed CNN model.  The results show that the proposed classification model is achieved  accuracy (94.94%) with  ( 94.56%), (94.56% ) and ( 5.06) for sensitivity, specificity and error rate (ERR)  respectively




How to Cite

M.Hamad , A. ., & D. Jasim, A. . (2021). Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor. University of Thi-Qar Journal for Engineering Sciences, 11(2), 95–101. Retrieved from