Toward An IoT-based Expert System for Heart Disease Diagnosis

IoT technology has been recently adopted in the healthcare system to collect Electrocardiogram (ECG) signals for heart disease diagnosis and prediction. However, noises in collected ECG signals make the diagnosis and prediction system unreliable and imprecise. In this work, we have proposed a new lightweight approach to removing noises in collected ECG signals to perform precise diagnosis and prediction. First, we have used a revised Sequential Recursive (SR) algorithm to transform the signals into digital format. Then, the digital data is proceeded using a revised Discrete Wavelet Transform (DWT) algorithm to detect peaks in the data to remove noises. Finally, we extract some key features from the data to perform diagnosis and prediction based on a feature dataset. Redundant features are removed by using Fishers Linear Discriminant (FLD). We have used an ECG dataset from MIT-BIH (PhisioNet) to build a knowledge-base diagnosis features. We have implemented a proof-ofconcept system that collects and processes real ECG signals to perform heart disease diagnosis and prediction based on the built knowledgebase.