English

Abstract

Cardiac auscultation is a traditional, yet highly sensitive and specific diagnosis technique for cardiovascular diseases. We present a Matlab framework for cardiac signals processing and analysis, which includes a new toolbox specifically designed for the main processing tasks related to heart sound analysis. Existing frameworks for acoustic cardiac signal analysis usually limit themselves to noise contamination detection, S1 and S2 segmentation and murmur diagnosis. Besides these operations, the proposed framework includes algorithms developed for segmentation of the main heart sound components capable of handling situations with high-grade murmur, S3 detection and identification, S2 split identification as well as systolic time intervals (STI) measurement using heart sound. Methods for cardiac function parameter extraction based on STI are also included. Most of the algorithms outlined in the paper have been extensively evaluated using data collected from patients with several types of cardiovascular diseases under real-life conditions. The achieved results suggest that the algorithms developed for the framework exhibit performances that are comparable and, in most cases, surpass existing state of the art methods.