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Automated selection of measures of heart rate variability for detection of early cardiac autonomic neuropathy

Heart rate variability (HRV) analysis begins with the relatively non-invasive and easily obtained process of ECG recording, yet provides a wealth of information on cardiovascular health. Measures obtained from HRV use time-domain, frequency-domain and non-linear approaches. These measures can be used to detect disease, yet from the large number of possible measures, it is difficult to know which to select, in order to provide the best separation between disease and health. This work reports on a case study using a variety of measures to detect the early stages of Cardiac Autonomic Neuropathy (CAN), a disease that affects the correct operation of the heart and in turn leads to associated co-morbidities. We examined time- and frequency-domain measures, and also non-linear measures. In all, 80 variables were extracted from the RR interval time series. We applied machine learning methods to separate participants with early CAN from healthy aged-matched controls, while using a Genetic Algorithm to search for the subset of measures that provided the maximum separation between these two classes. Using this subset the best performance was an accuracy of 70% achieved on unseen data.