Analysis of heartbeat time series via machine learning for detection of illnesses

The heart, a component of the cardiovascular system, is responsible for pumping oxygenated and deoxygenated blood. It does not behave like a metronome and normally there is a variation in the duration of the intervals between each heartbeat, called Heart Rate Variability (HRV). In the presence of diseases or with the progression of aging, there is a reduction in HRV due to dysfunction of the autonomic nervous system. The objective of this work is to show, using machine learning techniques, that these techniques are able to relate directly the variability of the heart with the degree of the disease. Producing, as a practical result, the use of these techniques in the prediction of different types of diseases by only analyzing their time series. One of the first techniques used in our work is the unsupervised learning algorithm (t-Stochastic Neighbor Embedding). We show that this algorithm is able to differentiate the type and degree of the disease just by analyzing time series, we demonstrate that it is possible to design a neural network architecture capable of learning these characteristics, relating cardiac variability and the disease. In a complementary analysis, we check that cardiac variability can be directly related to permutation entropy, proving that the healthier an individual is, the more stochastic his cardiac time series is. We build a classification algorithm, using deep learning, from the confusion matrix and the ROC curve. This algorithm can be used as an entry point in diagnosing patients by measuring their HRV.