A Framework for the Analysis of Acoustical Cardiac Signals

• Published in: IEEE transactions on biomedical engineering Vol. 54; no. 4; pp. 651 - 662
• Main Authors: Syed, Zeeshan, Leeds, Daniel, Curtis, Dorothy, Nesta, Francesca, Levine, Robert A., Guttag, John
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Artificial Intelligence; Auscultation; automated diagnosis; cardiac screening; Cardiology; Cluster Analysis; Computer aided diagnosis; Data mining; Diagnosis, Computer-Assisted - methods; Frequency; Heart; Heart Auscultation - methods; heart murmurs; Heart Murmurs - diagnosis; Heart Murmurs - physiopathology; Humans; Information analysis; Mitral Valve Insufficiency - diagnosis; Mitral Valve Insufficiency - physiopathology; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Signal analysis; Software tools; Sound Spectrography - methods; Tuning
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2006.889189

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