WAPrFX: A protocol to derive simple signals from complex heart-rate data and its application to train an AI model to predict wether the signal contains anomalies or not.
Deep Learning has been the choosen tool to analyze complex data in many fields. Heart anomalies is no exception. There is a plethora of articles working on deep learning models capable of identifying anomalies in heart-rate data. This article present a different approach: instead of working with the raw data, we will process the signal to extract a simplified one that will contain enough information to train a simple model to identify anomalies. This approach will allow the simple signal to be recreated using other sensors and monitoring tools that won provide ECG data.
