Detection and Classification of Cardiac Murmurs using Segmentation Techniques and Artificial Neural Networks

Strunic, Spencer; Rios-Gutiérrez, Fernando; Alba-Flores, Rocio; Nordehn, Glenn; Burns, S.G.

doi:10.1109/cidm.2007.368902

Cited by 42 publications

(34 citation statements)

References 9 publications

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“…This machine learning model successfully recognized other types of murmur as VSD, PDA, and PR which were not recognized by others. Table 6 compares all previously reported studies and types of murmur recognized [5][6][7][8].…”

Section: B Heart Model Validationmentioning

confidence: 99%

“…This research did not study simulated heart sounds, while all previous reports used simulated sounds. This research need to emphasize that simulated heart sounds models were not validated against real heart sounds thus the reported accuracy of systems based on simulated heart sounds should be cautiously interpreted [5][6][7][8]. The accuracy of this machine learning heart model for recognition of heart sounds has future implications in heart sound recognition using simpler devices compared to the more complex operator dependent ECHO machines, and promises new role in clinical education.…”

Section: B Heart Model Validationmentioning

confidence: 99%

“…Researchers built a databank with 110 sounds from 28 patients with feature vector extraction from spectrogram using average single cycle. For model testing they used 7 examples for normal sound, 4 examples for aortic www.ijacsa.thesai.org stenosis and 4 examples for aortic regurgitation [5]. Back propagation neural network (BPNN) and Hidden Markov model (HMM) were also employed in murmur recognition, with extraction based upon Mel Frequency Cepestral coefficient (MFCC) as a feature.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Recognition of Heart Murmur

Kotb¹,

Elmahdy²,

Mostafa³

et al. 2016

ijacsa

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Abstract-Diagnosis of congenital cardiac defects is challenging, with some being diagnosed during pregnancy while others are diagnosed after birth or later on during childhood. Prompt diagnosis allows early intervention and best prognosis. Contemporary diagnosis relies upon the history, clinical examination, pulse oximetery, chest X-ray, electrocardiogram (ECG), echocardiography (ECHO), computed tomography (CT) and cardiac catheterization. These diagnostic modalities reliable upon recording electrical activity or sound waves or upon radiation. Yet, congenital heart diseases are still liable to misdiagnosis because of level of operator expertise and other multiple factors. In an attempt to minimize effect of operator expertise this paper built a classification model for heart murmur recognition using Hidden Markov Model (HMM). This paper used Mel Frequency Cepestral coefficient (MFCC) as a feature and 13 MFCC coefficients. The machine learning model built by studying 1069 different heart sounds covering normal heart sounds, ventricular septal defect (VSD), mitral regurgitation (MR), aortic stenosis (AS), aortic regurgitation (AR), patent ductus arteriosus (PDA), pulmonary regurgitation (PR), and pulmonary stenosis (PS). MFCC feature used to extract feature matrix for each type of heart sounds after separation according to amplitude threshold. The frequency of normal heart sound (range= 1Hz to 139Hz) was specific without overlap with any of the studied defects (ranged= 156-556Hz). The frequency ranges for each of these defects was typical without overlap according to examined heart area (aortic, pulmonary, tricuspid and mitral area). The overall correct classification rate (CCR) using this model was 96% and sensitivity 98%. This model has great potential for prompt screening and specific defect detection. Effect of cardiac contractility, cardiomegaly or cardiac electrical activity on this novel detection system needs to be verified in future works.

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Section: B Heart Model Validationmentioning

confidence: 99%

Section: B Heart Model Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Recognition of Heart Murmur

Kotb¹,

Elmahdy²,

Mostafa³

et al. 2016

ijacsa

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“…The classification at this stage is very important because pathological heart sounds in newborns are more difficult to diagnose [6]. We achieve a high accuracy result to discriminate CHD by using optimized features and CART [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The result of this practice is a misallocation of healthcare funds, since echocardiograms are expensive. While it is clearly important to avoid that healthy newborn are sent for echocardiogram, it is also important to avoid that a newborn that has a pathological heart murmur is sent home without proper treatment [4].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnostic System for Congenital Heart Defects

Amiri¹,

Armano²

2013

IJACSA

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Abstract-congenital heart disease is the most common birth defect. The article describes detection and classification of congenital heart defect using classification and regressing trees. The ultimate goal of this research can decrease risk of cardiac arrest and mortality in compared with healthy children. The intelligent system proposed in three stages technique for automate diagnosis:(i) pre-processing(ii), feature extraction, and (iii) classification of congenital heart defects (CHD) using data mining tools. The intelligent diagnostic system has been validated with a representative dataset of 110 heart sound signals, taken from healthy and unhealthy medical cases. This system was evaluated in the test dataset with the following performance measurements global accuracy: 98.18%, sensitivity, 96.36% and specificity 100%. This results show the feasibility of classification based on optimized feature extraction and classifier. This paper follows the Association for the recommendations of the Advancement of Medical Instrumentation.

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