Diagnosis of patients with chronic heart failure implementing wavelet transform and machine learning techniques

Chronic heart failure (CHF) is a significant public health concern due to its increasing prevalence, high number of hospital admissions, and associated mortality. Its prevalence is progressively increasing due to the aging of the population and the decrease in mortality from acute myocardial infarction, among other medical advancements. Consequently, the incidence of CHF predominantly affects older age groups, doubling its prevalence every decade, becoming one of the main causes of mortality in patients older than 65 years. The main objective of this study is to apply machine learning based techniques to determine the best models to classify patients with chronic heart failure through their respiratory pattern. These patterns have been characterized from time series such as inspiratory and expiratory times, breathing duration, and tidal volume obtained from the respiratory flow signal. Based on the behavior of the respiratory pattern, CHF patients were classified into patients with non-periodic breathing, with periodic breathing, and with Cheyene-Stokes respiration (CSR). Time-frequency and statistical techniques have been implemented to analyze these features, and then various classification methods have been applied to define the optimal model with the best accuracy rates. These models could help to better understand the evolution of this disease and in early diagnosis.