Arrhythmia Detection Using ECG Signal Processing

The implementation of machine learning in bioinformatics and medical analysis has brought about great improvement in the health care delivery sector of the economy. As the heart remains the pivotal organ of the human body pumping blood to all parts of the body the need for adequate care of this vital organ is required. This paper therefore reviews the different approach to electrical signal process of the heart through ECG analysis and proposed the development of an arrhythmia detection model using artificial neural network (ANN). The model developed in Python on Google Colab environment was trained and tested with dataset contains 3640 sample heart beats. The dataset was preprocessed and split in the ration of 70:30 for the training and the test dataset respectively and the model performance impressively well with a record 97.43 % accuracy showing the possibility of improvement after more training. The system indicates the average heartbeat of 72.0 and 12 peaks from the sample dataset showing no trace of arrhythmia.