Autonomous auscultation of the human heart employing a precordial electro-phonocardiogram and ensemble empirical mode decomposition.

The research presented in this paper serves to provide a tool to autonomously screen for cardiovascular disease in the rural areas of Africa. With this tool, cardiovascular disease can potentially be detected in its initial stages, which is essential for effective treatment. The autonomous auscultation system proposed here utilizes recorded heart sounds and electrocardiogram signals to automatically distinguish between normal and abnormal heart conditions. Patients that are identified as abnormal by the system can then be referred to a specialist consultant, which will save a lot of unnecessary referrals. In this study, heart sound and electrocardiogram signals were recorded with the prototype precordial electro-phonocardiogram device, as part of a clinical study to screen patients for cardiovascular disease. These volunteers consisted of 28 patients with a diagnosed cardiovascular disease and, for control purposes, 34 persons diagnosed with healthy hearts. The proposed system employs wavelets to first denoise the recorded signals, which is then followed by segmentation of heart sounds. Frequency spectrum information was extracted as diagnostic features from the heart sounds by means of ensemble empirical mode decomposition and auto regressive modelling. The respective features were then classified with an ensemble artificial neural network. The performance of the autonomous auscultation system used in concert with the precordial electro-phonocardiogram prototype showed a sensitivity of 82% and a specificity of 88%. These results demonstrate the potential benefit of the precordial electro-phonocardiogram device and the developed autonomous auscultation software as a screening tool in a rural healthcare environment where large numbers of patients are often cared for by a small number of inexperienced medical personnel.