Heart Murmur Recognition Based on Hidden Markov Model

Zhong, Lisha; Wan, Jiangzhong; Huang, Zhiwei; Cao, Gaofei; Xiao, Bo

doi:10.4236/jsip.2013.42020

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Hidden Markov Models (HMM) are the most widely utilized categorization systems [63]. For developing PRSs, HMM is employed to record the sequential information available in feature vectors.…”

Section: Hidden Markov Modelmentioning

confidence: 99%

A Concise Survey on Biometric Recognition Methods

Kanakam,

Jubilson,

Emani

et al. 2023

IJCDS

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In this modern era, user authentication has become vital regarding security and privacy in keeping their information and valuables safe. Password or pin-based authentication systems are insufficient to protect the security of individuals. People now anticipate more dependable and secure biometric authentication systems as a result of virtualization in both academics and industry. In this study, we discussed biometrics, various biometric authentication methods, and the work in each biometric system. We summarised and emphasised the significant contribution made by several researchers in this field. Our goal is to present a thorough analysis that may throw light on the important contributions and aid researchers looking to contribute their work to biometrics.

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Section: Hidden Markov Modelmentioning

confidence: 99%

A Concise Survey on Biometric Recognition Methods

Kanakam,

Jubilson,

Emani

et al. 2023

IJCDS

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“…ECG exhibits heartbeat, rhythm and erythematic but it does not classify murmurs. Conversely, PCG and echocardiography explain whether the abnormality is due to murmurs, muscles, veins or valves and so forth, but echocardiography is costly technique and it also does not yield better quality images for bulky (overweight) persons or patients suffering from lung diseases (Ahmed, Dey, & Ashour, ; Garde, Sörnmo, & Laguna, ; Zhong, Wan, Huang, Cao, & Xiao, ). To overcome these limitations, a computer‐aided cognitive system could be developed to assist the cardiologists for heart murmur detection at early stages and differentiate the normal heart sounds from the sounds with certain pathological indications (Norouzi et al, ; Saba, Al‐Zahrani, & Rehman, ).…”

Section: Introductionmentioning

confidence: 99%

Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM

Fahad

Khan

Saba

et al. 2018

Microscopy Res & Technique

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Auscultation of heart dispenses identification of the cardiac valves. An electronic stethoscope is used for the acquisition of heart murmurs that is further classified into normal or abnormal murmurs. The process of heart sound segmentation involves discrete wavelet transform to obtain individual components of the heart signal and its separation into systole and diastole intervals. This research presents a novel scheme to develop a semi-automatic cardiac valve disorder diagnosis system. Accordingly, features are extracted using wavelet transform and spectral analysis of input signals. The proposed classification scheme is the fusion of adaptive-neuro fuzzy inference system (ANFIS) and HMM. Both classifiers are trained using the extracted features to correctly identify normal and abnormal heart murmurs. Experimental results thus achieved exhibit that proposed system furnishes promising classification accuracy with excellent specificity and sensitivity. However, the proposed system has fewer classification errors, fewer computations, and lower dimensional feature set to build an intelligent system for detection and classification of heart murmurs.

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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%

“…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. The BPNN overall CCR was reported to be 82.8% and HMM model murmur sounds overall CCR was 94.2% [6]. The recognition using HMM with empirical mode decomposition (EMD) and MFCC yielded overall accuracy equal 98.9% [7].…”

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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Heart Murmur Recognition Based on Hidden Markov Model

Cited by 6 publications

References 4 publications

A Concise Survey on Biometric Recognition Methods

A Concise Survey on Biometric Recognition Methods

Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM

Improving the Recognition of Heart Murmur

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