Heart diseases diagnosis using heart sounds

Abdel-Alim, O.; Hamdy, N.; El-Hanjouri, M.A.

doi:10.1109/nrsc.2002.1022675

Cited by 13 publications

(3 citation statements)

References 2 publications

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“…Neural Networks (NN) are used for classifying 20 samples after being trained with 65, obtaining an accuracy of 85%. NNs are also used by Abdel-Alim (Onsy Abdel-Alim and El-Hanjouri, 2002) for the automatic diagnostics of heart valves using wavelets feature vectors and stethoscope location information. They use two NNs: one for systolic diseases and the other for diastolic diseases.…”

Section: Automatic Pathology Classificationmentioning

confidence: 99%

A Survey of Audio Processing Algorithms for Digital Stethoscopes

Hedayioglu

Coimbra

Mattos³

2009

Proceedings of the International Conference on Health Informatics

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Digital stethoscopes have been drawing the attention of the biomedical engineering community for some time now, as seen from patent applications and scientific publications. In the future, we expect 'intelligent stethoscopes' to assist the clinician in cardiac exam analysis and diagnostic, potentiating functionalities such as the teaching of auscultation, telemedicine, and personalized healthcare. In this paper we review the most recent heart sound processing publications, discussing their adequacy for implementation in digital stethoscopes. Our results show a body of interesting and promising work, although we identify three important limitations of this research field: lack of a set of universally accepted heart-sound features, badly described experimental methodologies and absence of a clinical validation step. Correcting these flaws is vital for creating convincing next-generation 'intelligent' digital stethoscopes that the medical community can use and trust.

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Section: Automatic Pathology Classificationmentioning

confidence: 99%

A Survey of Audio Processing Algorithms for Digital Stethoscopes

Hedayioglu

Coimbra

Mattos³

2009

Proceedings of the International Conference on Health Informatics

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“…The author concluded from his work that the fractal dimensions of a APB patients lies in the range of 1.54-1.58, whereas that of LBBB patient is expected in the range of 1.71-1.74, for PVC patient it is found that the range will be 1.48-1.53 and the fractal dimensions of a normal person is in the range of 1.65-1.67. [6] Likewise, Onsy Abdel-Alini in [13] has developed an algorithm for the identification of various valve related heart diseases based upon the heart beat sounds obtained by traditional Stethoscope. Each of the First Heart Sound (FHS) and Second Heart Sound (SHS) is identified after separation.…”

Section: Introductionmentioning

confidence: 99%

Developing Resource Efficient Heart Arrhythmia Classifier

Jan¹,

Khurshid²,

Khattak³

2015

IJCA

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This paper presents development of optimal digital circuit for the Heart Disease Classification using Cartesian Genetic Programming (CGP) for different types of arrhythmia. Extensive research work has already been carried out in this domain but non-linear nature of the technique remained one of the hurdles in its hardware prototyping. Efficient circuit development for resource constraint environment of the classifier remained an unsolved problem due to its algorithmic complexity. CGP system is trained to generate a classifier circuit based upon the fiducial points extracted out of the Electrocardiography (ECG) signals of dataset. Experimental results reported on heart disease data from machine learning repository of MIT-BIH showed satisfactory results as compare to other contemporary methods used in the field..

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“…In the course of analysis of these sounds, discrete wavelet transform, FFT, and linear prediction coding methods were employed. In the end, the classification success was found out to be %95.7 (Abdel-Alim et al, 2002). In this study, the heart sounds achieved through a stethoscope were initially computed, and they were subjected to Discrete Fourier Transform (DFT) and next the graphics and the frequency spectrum in the time domain that belonged to the heart sounds were drawn on the pocket computer.…”

Section: Introductionmentioning

confidence: 99%