A Framework for the Analysis of Acoustical Cardiac Signals

Abstract

Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.

Classification of Congenital Heart Diseases By SVM-MFCC Using Phonocardiograph

“…Therefore, the methods such as signal averaging will make no favorable results [1]; thus we used the noise removal method based on the wavelet (in this method we used the command 'wden' in MATLAB software).The correct selection of cases such as type of Wavelet, number of decomposition levels, and thresholding method is necessary. Using a wavelet with 5 levels will make favorite results.…”

An Algorithm for Heart Rate Extraction From Acoustic Recordings at the Neck

“…An abnormal blood flow in the left ventricle or an increase in its corresponding stiffness can also give rise to S3 and S4 sounds respectively. An automated processing and detection of these sounds in the PCG using an algorithm can also assist with manual auscultation and reduce the number of cases where an incorrect interpretation can lead to an expensive and advanced examination of the subject [7] [8].…”

Assessing normality of heart sound by matching pursuit residue with frequency-domain-based templates

“…First, it is friendly to parallel computation, which fits adequately in current prevailing computation architecture, for example, generic homogeneous processors and cloud computing with balanced peer computing capability. Compared to other common computing schemes [5][6] adopted in medical applications which are mainly composed of feature extraction and classification processes, the computational load and bottleneck of MP are more predictable. The computation time of feature extraction processes vary from feature to feature.…”

“…In a more broad point of view, MP could be regarded as only a single feature to the mentioned schemes in [5] [6]. Though owing to the computational complexity, MP is not generally used as feature extraction means.…”