The wavelet transform and its applications to phonocardiogram signal analysis

Khadra, L.; Matalgah, Mustafa M.; El-Asir, B.; Mawagdeh, S

doi:10.3109/14639239109025301

Cited by 48 publications

(21 citation statements)

References 2 publications

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“…The use of wavelet transformations is advantageous for the analysis of transient signals such as those in phonocardiograms (CHAN et al, 1977;BENTLEY and Mc DONNEL, 1994;EL-ASIR et al, 1996;KHADRA et al, 1991;LIANa and HARTIMO, 1998).…”

Section: Discussionmentioning

confidence: 99%

Detection of the third heart sound using a tailored wavelet approach

Hult

Fjallbrant

Wranne

et al. 2004

Med. Biol. Eng. Comput.

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The third heart sound is normally heard during auscultation of younger individuals but disappears with increasing age. However, this sound can appear in patients with heart failure and is thus of potential diagnostic use in these patients. Auscultation of the heart involves a high degree of subjectivity. Furthermore, the third heart sound has low amplitude and a low-frequency content compared with the first and second heart sounds, which makes it difficult for the human ear to detect this sound. It is our belief that it would be of great help to the physician to receive computer-based support through an intelligent stethoscope, to determine whether a third heart sound is present or not. A precise, accurate and low-cost instrument of this kind would potentially provide objective means for the detection of early heart failure, and could even be used in primary health care. In the first step, phonocardiograms from ten children, all known to have a third heart sound, were analysed, to provide knowledge about the sound features without interference from pathological sounds. Using this knowledge, a tailored wavelet analysis procedure was developed to identify the third heart sound automatically, a technique that was shown to be superior to Fourier transform techniques. In the second step, the method was applied to phonocardiograms from heart patients known to have heart failure. The features of the third heart sound in children and of that in patients were shown to be similar. This resulted in a method for the automatic detection of third heart sounds. The method was able to detect third heart sounds effectively (90%), with a low false detection rate (3.7%), which supports its clinical use. The detection rate was almost equal in both the children and patient groups. The method is therefore capable of detecting, not only distinct and clearly visible/audible third heart sounds found in children, but also third heart sounds in phonocardiograms from patients suffering from heart failure.

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Section: Discussionmentioning

confidence: 99%

Detection of the third heart sound using a tailored wavelet approach

Hult

Fjallbrant

Wranne

et al. 2004

Med. Biol. Eng. Comput.

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“…In addition, it is important to remark that the plateau of k 1 is determined automatically according to the algorithm proposed in Rosenstein et al 28 The value of H is defined as the slope that is obtained when calculating the average of the R(N)/r(N) relationship given by (20), for different scales in the data length of the analyzed time series. The algorithm for estimating the Hurst exponent, designed in this work, does not overlap data regions, basically because overlapping regions does not give exact results.…”

Section: Fractal Featuresmentioning

confidence: 99%

Digital Auscultation Analysis for Heart Murmur Detection

et al. 2008

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This work presents a comparison of different approaches for the detection of murmurs from phonocardiographic signals. Taking into account the variability of the phonocardiographic signals induced by valve disorders, three families of features were analyzed: (a) time-varying & time-frequency features; (b) perceptual; and (c) fractal features. With the aim of improving the performance of the system, the accuracy of the system was tested using several combinations of the aforementioned families of parameters. In the second stage, the main components extracted from each family were combined together with the goal of improving the accuracy of the system. The contribution of each family of features extracted was evaluated by means of a simple k-nearest neighbors classifier, showing that fractal features provide the best accuracy (97.17%), followed by time-varying & time-frequency (95.28%), and perceptual features (88.7%). However, an accuracy around 94% can be reached just by using the two main features of the fractal family; therefore, considering the difficulties related to the automatic intrabeat segmentation needed for spectral and perceptual features, this scheme becomes an interesting alternative. The conclusion is that fractal type features were the most robust family of parameters (in the sense of accuracy vs. computational load) for the automatic detection of murmurs. This work was carried out using a database that contains 164 phonocardiographic recordings (81 normal and 83 records with murmurs). The database was segmented to extract 360 representative individual beats (180 per class).

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“…The Continuous Wavelet Transform CWT, is frequently used [4][5][6][7][8] in the analysis of various heart sounds and murmurs. From these studies, it was confirmed that Morlet wavelet was the best suited for the analysis of heart sounds.…”

Section: Wavelet Representationmentioning

confidence: 99%