Detection of Arrhythmia using ECG waves with Deep Convolutional Neural Networks

If there is an availability of technological medical electronic devices to classify heart disease, it would absolutely change the future in terms of making it more economical and qualitative for all the people suffering from heart-related ailments. With the increasing medical expenses and non-affordability of the poor families, it becomes logical to design a system that can detect heart disease in particular Arrhythmia, without higher expense. Recently, the Cardiovascular systems are evaluated more reliably by using Electrocardiogram (ECG) waves. This project in particular is designed to check for any irregularities in heart beats, which is represented in the variations of an ECG wave, and then compared it with normal beats to detect Arrhythmia. The electronics behind this project is Raspberry Pi and ADS1115, an ADC, which converts the real-time, analog ECG wave signal into a digital wave with the help of heart rate sensor-AD8232, and a three-lead system. A normalized wave is fed into the deep convolutional neural network to predict the output into one of the 5 different categories. Furthermore, the ADASYN - Adaptive Synthetic Sampling - algorithm is used to effectively classify the disease in accordance with the MIT-BIH dataset.