title: 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.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. zhs@csail.mit.edu
authors.forename: Zeeshan
authors.initials: Z
authors.lastname: Syed
authors.forename: Daniel
authors.initials: D
authors.lastname: Leeds
authors.forename: Dorothy
authors.initials: D
authors.lastname: Curtis
authors.forename: Francesca
authors.initials: F
authors.lastname: Nesta
authors.forename: Robert A
authors.initials: RA
authors.lastname: Levine
authors.forename: John
authors.initials: J
authors.lastname: Guttag
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006326:Heart Auscultation / Q000379:methods*; D006337:Heart Murmurs / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008944:Mitral Valve Insufficiency / Q000175:diagnosis* / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*