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Recognition of Heart Murmur Based on Machine Learning and Visual Based Analysis of Phonocardiography

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Intelligent Computing (SAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 857))

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Abstract

Heart sounds recognition is essential for heart defects diagnosis. Diagnosis of structural heart defects is not always possible using the contemporary stethoscope and need further assessment using high cost devices such as X-ray, electrocardiogram (ECG), echocardiography (ECHO) and computed tomography (CT)). Automatic computer assisted auscultation may be used as a clinical decision support tool. In an attempt to develop an automatic computer aided diagnostic modality for heart conditions that is sensitive, specific, non-invasive we created two automatic computer cardiac auscultation (ACCA) models that provide heart sound analysis and we aimed to improve the sensitivity and correct classification rate (CCR) of recognition of heart sounds, thus we developed model A ACCA recognition system (machine learning (interpreter independent)) and model B ACCA recognition system (machine learning and interpreter dependent visual analysis). We used machine learning based on mel frequency cepestral coefficients as a feature and Hidden Markov Model (HMM) as a classifier. We performed visual analysis based on phonocardiography (PCG) and spectrogram image. Model A ACCA demonstrated 97% CCR, 99.2% sensitivity and specificity 100% and model B ACCA demonstrated 98.2% CCR and 99.2% sensitivity and specificity 100%. ACCA models A and B allow reliable interpretation of recognised sounds to detect underlying structural defects.

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Abbreviations

ECG:

Electrocardiogram

ECHO:

Echocardiography

CT:

Computed tomography

ACCA:

Automatic Computer Cardiac Auscultation

CCR:

Correct Classification Rate

HMM:

Hidden Markov Model

PCG:

Phonocardiography

MFCC:

Mel Frequency Cepestral Coefficient

AS:

Aortic Stenosis

VSD:

Ventricular Septal Defect

MR:

Mitral regurgitation

PR:

Pulmonary Regurgitation

PS:

Pulmonary Stenosis

PDA:

Patent Ductus Arteriosus

FFT:

Fast Fourier Transform

SVM:

Support Vector Machine

DWT:

Discrete Wavelet Transform

ANN:

Artificial Neural Network

BPNN:

Back Propagation Neural Network

ANFI:

Adaptive Neuro-Fuzzy Inference

WT:

Wavelet Transform

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Author information

Authors and Affiliations

  1. Department of Pediatrics, Faculty of Medicine, Cairo University, Cairo, Egypt

    Magd Ahmed Kotb, Fatma El Zahraa Mostafa & Christine William Shaker

  2. Department of Information Technology, Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Hesham Nabih Elmahdy, Mohamed Ahmed Refaey & Khaled Waleed Younis Rjoob

Authors
  1. Magd Ahmed Kotb
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  2. Hesham Nabih Elmahdy
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  3. Fatma El Zahraa Mostafa
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  4. Christine William Shaker
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  5. Mohamed Ahmed Refaey
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  6. Khaled Waleed Younis Rjoob
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Corresponding author

Correspondence to Magd Ahmed Kotb .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Kotb, M.A., Elmahdy, H.N., Mostafa, F.E.Z., Shaker, C.W., Refaey, M.A., Rjoob, K.W.Y. (2019). Recognition of Heart Murmur Based on Machine Learning and Visual Based Analysis of Phonocardiography. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_14

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