ECG Classification

• Tech Stack: Python, Deep Learning, Keras, CNN
• Github URL: Project Link

ECG classification using deep learning is to improve the accuracy and speed of diagnosis, reduce human error, and enable early detection and treatment of heart diseases. This can lead to better patient outcomes and overall improvements in public health. I used CNN algorithm to train the dataset.

Dataset:
The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.