Key result
An IoT system utilizing a Decision Tree algorithm achieved over 95% accuracy in detecting arrhythmias from ECG signals for real-time remote monitoring.
Why the study?
With increasing epidemics, the concept of telemedicine combined with the Internet of Things has become more prominent, prompting implementation of an IoT system to sense ECG signals, detect arrhythmia, and transmit data to physicians or relatives.
An IoT-based telemedicine system using a Decision Tree algorithm can detect cardiac arrhythmias from ECG signals with over 95% accuracy and transmit real-time alerts to mobile devices.
In recent years, by increasing the number of epidemics, the concept of telemedicine and its combination with the Internet of Things (IoT), has become a more prominent subject in engineering. The purpose of this study is to implement an IoT system that can sense the heart signal and detect the arrhythmia in a heartbeat by using smart signal processing and send them through the internet to a relative of the patient or doctor. In this system, Electrocardiogram (ECG) signals are received by electrodes and sent to the AD8232 chip. Then, by connecting to the ESP32 module, information through the Wi-Fi network and Message Queuing Telemetry Transport (MQTT) protocol, is sent to the broker created on Raspberry Pi. Then, using Python and through the Decision Tree algorithm, the heartbeat's performance is evaluated as a smart process. The accuracy of the algorithm in detecting arrhythmias is over 95%. Finally, with an android application and through the TCP/IP protocol, the health or arrhythmia of the heartbeat will be informed on the doctor's or one of the patient's relative's cell phone, and its real-time display will be provided. Also, the possibility of sending a command from a mobile to the broker has been provided.
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mansoori et al. (2021) studied Arrhythmia. IoT system with Decision Tree algorithm was evaluated on Accuracy in detecting arrhythmias. An IoT system utilizing a Decision Tree algorithm achieved over 95% accuracy in detecting arrhythmias from ECG signals for real-time remote monitoring.
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