Time-frequency analysis of heart sounds

El-Asir, B.; Khadra, L.; Al-Abbasi, A. H.; Mohammed, M.

doi:10.1109/tencon.1996.608401

Cited by 36 publications

(23 citation statements)

References 6 publications

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“…These sounds have their maxima in the frequency range 80-120 Hz, whereas this study shows that the main frequency content of the third heart sound is in the range of 10-100 Hz. This finding is consistent with other studies that found the main third heart sound frequency content to be below 60 Hz (EWING et al, 1984;LONGHINI et al, 1988;AGGIO et al, 1990;GLOVER et al, 1992;MANSON et al, 1995;EL-ASIR et al, 1996).…”

Section: Discussionsupporting

confidence: 93%

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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: Discussionsupporting

confidence: 93%

“…The EGG signal was used only during the analysis phase and was not needed for automatic detection of the third heart sound. The heart sounds have a frequency content mainly in the range 20-500 Hz (LuISADA and PORTALUPPI 1982;EL-ASIR et al, 1996). The sample rate was 2.5 kHz, which covers well the frequency content of the heart sounds.…”

Section: Equipmentmentioning

confidence: 94%

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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“…Efforts to date have met with limited success. 7,11,12,21,[27][28][29][30][31][32] One of these studies examined the sounds from prosthetic heart valves. 11 Several of these studies are simply observations of the time frequency analysis of innocent and pathological heart murmurs, with no emphasis on developing automated diagnostic algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…11 Several of these studies are simply observations of the time frequency analysis of innocent and pathological heart murmurs, with no emphasis on developing automated diagnostic algorithms. 29,30 All of these previous studies (including those using ANNs) have generally focused on determining if a heart murmur exists, with a lack of emphasis on developing diagnostic algorithms differentiating between innocent versus pathological murmurs. 7,12,21,27,28,31,32 In this study, 100% sensitivity and specificity were obtained for the ANN distinguishing between the 69 innocent and pathological heart murmurs presented to it using a spectral resolution of 1 Hz and a spectrum of 0 to 210 Hz.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

et al. 2001

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Background-Early recognition of heart disease is an important goal in pediatrics. Efforts in developing an inexpensive screening device that can assist in the differentiation between innocent and pathological heart murmurs have met with limited success. Artificial neural networks (ANNs) are valuable tools used in complex pattern recognition and classification tasks. The aim of the present study was to train an ANN to distinguish between innocent and pathological murmurs effectively. Methods and Results-Using an electronic stethoscope, heart sounds were recorded from 69 patients (37 pathological and 32 innocent murmurs). Sound samples were processed using digital signal analysis and fed into a custom ANN. With optimal settings, sensitivities and specificities of 100% were obtained on the data collected with the ANN classification system developed. For future unknowns, our results suggest the generalization would improve with better representation of all classes in the training data. Conclusion-We demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes. This technology offers great promise for the development of a device for high-volume screening of children for heart disease.

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“…The abrupt frequency changes, the complex and highly nonstationary nature of the heart sound signals make the heart sound signal analysis a tedious job. The FFT and wavelet approaches have been applied to this in various studies and the work of correlating the heart sounds with the heart defects has been done [5][6][7][8][9][10]. Some related works to this study are as follows: where T1 and T2 are the widths of the first sound S1 and the second sound S2, T11 is the time interval between two abutted S1, which indicates the heart beat rhythm condition and T12 is the time interval between S1 and S2, which is an indicator to express the heart valvular murmurs [14].…”

Section: Previous Workmentioning

confidence: 99%

Heart Sounds Classification using Feature Extraction of Phonocardiography Signal

Singh¹,

Cheema²

2013

IJCA

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The Phonocardiogram (PCG) signals contain very useful information about the condition of the heart. By analyzing these signals, early detection and diagnosis of heart diseases can be done. It is also very useful in the case of infants, where ECG recording and other techniques are difficult to implement. In this paper, a classification method is proposed to classify normal and abnormal heart sound signals having murmurs without getting into the cumbersome process of segmenting fundamental heart sounds (FHS) using Electrocardiogram (ECG) gating. The proposed algorithm can be easily implemented on latest electronic stethoscopes, and therefore the unnecessary ECG can be avoided. General TermsClassification algorithm.

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Time-frequency analysis of heart sounds

Cited by 36 publications

References 6 publications

Detection of the third heart sound using a tailored wavelet approach

Detection of the third heart sound using a tailored wavelet approach

Artificial Neural Network–Based Method of Screening Heart Murmurs in Children

Heart Sounds Classification using Feature Extraction of Phonocardiography Signal

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