Artificial Intelligence Can Diagnose any Disease from the Data of an Electrocardiogram

Abstract

The electrocardiogram is a test that records the electrical activity of the heart, and has recently been shown that it can also detect other non-cardiovascular conditions, such as diabetes, measles, Alzheimer’s, arterial hypertension, fatty liver, hiperpotasemia, hypothyroidism, malaria, etc. For this reason, this paper proposes a computational technique to analyze and detect patterns based on the position and length of the segments between the peaks that make up the electrocardiogram signals, using deep learning techniques created by Artificial Intelligence. The program started by evaluating a database of heart arrhythmia signals, which included more than 120 electrocardiograms grouped between signals from normal and arrhythmia patients, employing convolutional neural network (CNN). The program had a prediction accuracy rate of 94.3%.