IoT based ECG Signal Feature Extraction and Analysis for Heart Disease Risk Assessment

Digital healthcare solutions are being created to meet the demands of the healthcare industry. Advances in IoT and cloud computing have led to the development of numerous smart healthcare solutions for elderly people and people with chronic diseases. Healthcare centres and physicians get the opportunity to assess the health of patients from anywhere through the developed smart systems. In this work, an IoT and cloud-based healthcare system is developed for ECG signal parameter extraction and machine learning-based analysis that will be helpful in heart disease risk assessment. The minimal local signal processing required by IoT-based healthcare equipment is made possible by cloud computing. However, there are certain concerns related to service quality issues while employing a cloud framework for real-time monitoring and signal processing. This work proposes ECG beat rate detection and machine learning-based analysis in an IoT-based framework. Finding the precise location of the QRS complex during ECG data processing facilitates the detection of heart rate variability (HRV) parameters, which will play a crucial role in the risk assessment of heart diseases.