A neural network approach to classify carotid disorders from Heart Rate Variability analysis

Atherosclerosis is a progressive process responsible for most heart diseases and ischemic stroke. It constitutes, in fact, the most common cause of stroke in middle-aged people. To avoid or, at least, limit the disabling deficits that may derive from a carotid disease, a prompt and early diagnosis is necessary. The diagnostic technique used to detect a carotid disease is the eco-color Doppler. Unfortunately, this method is not free from errors, due to manufacturer mistakes or its operator dependence.

In this study, we propose an automated methodology capable of identifying the presence of a carotid disease from the Heart Rate Variability analysis of electrocardiographic signals. A Correlation-based Feature Selector for data reduction and Artificial Neural Networks are used to distinguish between pathological and healthy subjects.

A series of tests has been realized to evaluate the proposed approach by using electrocardiographic signals selected from an available database in order to analyse the classification ability in comparison with other algorithms existing in literature. The results obtained show that the proposed approach provides values of accuracy, sensitivity, specificity, precision, F-measure and ROC area, respectively equal to 90.5%, 97.7%, 72.9%, 89.7%, 93.5% and 0.957, better than those achieved by other algorithms.

Considering the achieved accuracy, our methodology is more effective than any of the main algorithm existing in literature. It is important to note that this approach is proposed as a support for the diagnosis of a carotid disorder through a non-invasive approach.