Assessment of Severity of Aortic Stenosis Through Time-Frequency Analysis of Murmur *

Kim, Dosik; Tavel, Morton E.

doi:10.1378/chest.124.5.1638

Cited by 39 publications

(20 citation statements)

References 17 publications

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“…All of these parameters are located in the spectra at different frequencies were correlated to the Doppler echocardiogram-derived mean and peak pressure gradients as an indicator for an aortic valve stenosis. 18 All selected parameters (Table 2) are extracted from recordings of auscultation area 1, 2 or 3. Parameters of auscultation area 4 or 5 did not contribute significantly to the discrimination between patients.…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

Voss

Mix

Hübner³

2005

Ann Biomed Eng

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The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.

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Section: Discussionmentioning

confidence: 99%

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

Voss

Mix

Hübner³

2005

Ann Biomed Eng

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“…Early work looked at analysis using the short time Fourier transform [8], but short time Fourier transform methods require large amounts of processing power and memory, which is a practical disadvantage. Other researchers have manually selected features and then characterised them using FFT [7,9]. Finally, some approaches include the use of a second measurement, such as ECG to locate a particular feature by 'triangulation' [10], which is effective, but introduces complexity into the data recording procedure.…”

Section: Automatic Recognition Of Medical Acoustic Soundsmentioning

confidence: 99%

Signal analysis of medical acoustic sounds with applications to chest medicine

Marshall

Boussakta

2007

Journal of the Franklin Institute

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“…This method achieves high accuracy and is therefore useful for differential diagnosis but is not suitable for a first screening by a general practitioner. Other studies showed that timefrequency analysis of heart murmur is applicable for the evaluation and monitoring of patients with suspected and proven aortic valve stenoses (KIM and TAVEL, 2003).…”

Section: Fig 4 Mean Correlation Coefficients Of All Groups Against Amentioning

confidence: 99%

Diagnosing aortic valve stenosis by correlation analysis of wavelet filtered heart sounds

Voss

Herold

Schroeder

et al. 2005

Med. Biol. Eng. Comput.

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Traditional auscultation performed by the general practitioner remains problematic and often gives significant results only in a late stage of heart valve disease. Valve stenoses and insufficiencies are nowadays diagnosed with accurate but expensive ultrasonic devices. This study aimed to develop a new heart sound analysis method for diagnosing aortic valve stenoses (AVS) based on a wavelet and correlation technique approach. Heart sounds recorded from 373 patients (107 AVS patients, 61 healthy controls (REF) and 205 patients with other valve diseases (OVD)) with an electronic stethoscope were wavelet filtered, and envelopes were calculated. Three correlations on the basis of these envelopes were performed: within the AVS group, between the groups AVS and REF and between the groups AVS and OVD, resulting in the mean correlation coefficients rAVS, rAVSv.REF and rAVSv.OVD. These results showed that rAVS (0.783 +/- 0.097) is significantly higher (p < 0.0001) than rAVSv.REF (0.590 +/- 0.056) and rAVSv.OVD (0.516 +/- 0.056), leading to a highly significant discrimination between the groups. The wavelet and correlation-based heart sound analysis system should be useful to general practitioners for low-cost, easy-to-use automatic diagnosis of aortic valve stenoses.

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Assessment of Severity of Aortic Stenosis Through Time-Frequency Analysis of Murmur *

Cited by 39 publications

References 17 publications

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

Signal analysis of medical acoustic sounds with applications to chest medicine

Diagnosing aortic valve stenosis by correlation analysis of wavelet filtered heart sounds

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