Classification of cardiovascular diseases using dysphonia measurement in speech

Abstract

Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but it is far from being comfortable, rapid, and accessible to everyone. Speech analysis is an emerging non-invasive diagnostic tool, and a lot of researches have shown that it is efficient in speech recognition and in detecting Parkinson's disease, so can it be effective for differentiating between patients with cardiovascular disease and healthy people? This present work answers the question posed, by collecting a database of 75 people, 35 of whom suffering from cardiovascular diseases, and 40 are healthy. We took from each one three vocal recordings of sustained vowels (aaaaa…, ooooo….. and iiiiiiii…..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers: the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier. The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier.