This paper presents a new method to detect and to delineate phonocardiogram (PCG) sounds. Toward this objective, after preprocessing the PCG signal, two windows were moved on the preprocessed signal, and in each analysis window, two frequency-and amplitude-based features were calculated from the excerpted segment. Then, a synthetic decision making basis was devised by combining these two features for being used as an efficient detection-delineation decision statistic, (DS). Next, local extremums and locations of minimum slopes of the DS were determined by conducting forward–backward local investigations with the purpose of detecting sound incidences and their boundaries. In order to recognize the delineated PCG sounds, first, S1 and S2 were detected. Then, a new DS was regenerated from the signal whose S1 and S2 were eliminated to detect occasional S3 and S4 sounds. Finally, probable murmurs and souffles were spotted. The proposed algorithm was applied to 52 min PCG signals gathered from patients with different valve diseases. The provided database was annotated by some cardiology experts equipped by echocardiography and appropriate computer interfaces. The acquisition landmarks were in 2R (aortic), 2L (pulmonic), 4R (apex) and 4L (tricuspid) positions. The acquisition sensor was an electronic stethoscope (3 M Littmann® 3200, 4 kHz sampling frequency). The operating characteristics of the proposed method have an average sensitivity Se = 99.00% and positive predictive value PPV = 98.60% for sound type recognition (i.e., S1, S2, S3 or S4).
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Adaptive Smoothing Filtering
Phonocardiogram
Gaussian Smoothing Filtering
Decision Statistic
Subject to
Time-Frequency
Short-Time Frequency Amplifier
Inverse Packet Wavelet Transform
Packet Wavelet Transform
Aortic Stenosis
Aortic Regurgitation
Mitral Regurgitation
Mitral Stenosis
Accuracy
Frequency amplifier feature
Amplitude-based feature (envelope)
Frequency-amplitude-based feature
Window length
Segmentation threshold
A generic PCG signal
Normalized PCG signal
Wavelet denoising threshold
Smoothing parameter
Periodicity time
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Department of Mechanical Engineering, Faculty of Mechanical Engineering, K. N. Toosi University of Technology, P.O. Box 19395-1999, Tehran, Iran
H. Naseri & M. R. Homaeinezhad
CardioVascular Research Group (CVRG), K. N. Toosi University of Technology, Tehran, Iran
H. Naseri & M. R. Homaeinezhad
Correspondence to H. Naseri.
Additional informationAssociate Editor Tingrui Pan oversaw the review of this article.
About this article Cite this articleNaseri, H., Homaeinezhad, M.R. Detection and Boundary Identification of Phonocardiogram Sounds Using an Expert Frequency-Energy Based Metric. Ann Biomed Eng 41, 279–292 (2013). https://doi.org/10.1007/s10439-012-0645-x
Received: 29 May 2012
Accepted: 22 August 2012
Published: 07 September 2012
Issue Date: February 2013
DOI: https://doi.org/10.1007/s10439-012-0645-x
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