Heart diseases diagnosis using HMM

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

doi:10.1109/melecon.2002.1014641

Cited by 15 publications

(3 citation statements)

References 6 publications

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“…Because it has been successfully used for automatic diagnosis in many applications (e.g., diagnosis of diseases in medicine [15][16][17] and diagnosis of mechanical system faults [18][19]), we propose to use Hidden Markov Model (HMM) [12] in diagnosing faults of multi radio access technologies networks. The alarmed data, in each historical file, are segmented into T segments; each segment has a duration of K seconds.…”

Section: Hmm-based Modelling and Recognitionmentioning

confidence: 99%

Autonomas Fault Diagnosis System for Cellular Networks Based on Hidden Markov Model

AbdelMoez

Ali

Abdelhamid

2015

JES. Journal of Engineering Sciences

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Automated diagnosis and Troubleshooting (TS) in Radio Access Networks (RAN) of cellular systems are basic management tasks, which are required to guarantee efficient use of network resources. In this paper, we investigate the usage of machine learning techniques: stochastic methods and discriminant analysis for automating these TS tasks. Our proposed framework is based on Hidden Markov Model (HMM), Principle Component Analysis (PCA) and Fisher Linear Discriminant (FLD) techniques. In a learning phase, symptoms relating to faults in the network are extracted from a network management system (NMS). Then they are used to create a fault model. This model is used to identify the unknown faults using a nearest neighbor classifier. Reported results for the automated diagnosis using live RAN measurements illustrate the efficiency of the proposed TS framework and its importance to mobile network operators.

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Section: Hmm-based Modelling and Recognitionmentioning

confidence: 99%

Autonomas Fault Diagnosis System for Cellular Networks Based on Hidden Markov Model

AbdelMoez

Ali

Abdelhamid

2015

JES. Journal of Engineering Sciences

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“…Multi-Layer Perceptron (MLP) is most commonly used ANN model used for classification of heart sound. Besides ANN, Hidden Markov Models (HMM) has been used to analyze and classify heart sound [21][22]. In HMM, the heart sound is modeled by a set of observation states along the time, and a heart sound category is detected by detecting specific observation sequence.…”

Section: Introductionmentioning

confidence: 99%

Detection of heart condition by time and frequency based template

Rahman

Ahmed

2014

2014 17th International Conference on Computer and Information Technology (ICCIT)

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The phonocardiogram (PCG) is an easy and costless yet powerful tool to detect the heart condition. While the cardiovascular disease is an increasing global threat to humanity, there is a decrease in doctors' capability for diagnosing these diseases by auscultation. In this work, a neural network based automated system is proposed to aid early detection of these diseases. Two template-based feature representations are developed to effectively represent the characteristics of heart sound. These features, extracted from sound files of known cases, are then used to train a neural network. Our experimental results corroborate that the proposed method can efficiently detect the heart condition with good overall classification accuracy. In addition, a graphical user interface and a low cost device is developed which is user-friendly and can be used without much relevant knowledge.

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“…The reasons partly have been the lack of further pursuit, unproven or low accuracy rates and primarily unrealizability in the hardware level because of the size of the algorithm. Some design methods use segmentation, wavelets, neural networks, S-transform, Hilbert Transform, decision tree, Hidden Markov models, etc [5][6][7][8][9][10][11][12][13][14][15][16][17]. Despite having designed a variety of algorithms, researchers faced a challenging situation in terms of classification of the heart sounds because of the large number of possible cases as well as the characteristics of the sound from a signal processing perspective.…”

Section: Descriptionmentioning

confidence: 99%

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

Rios-Gutiérrez¹,

Alba-Flores²,

Ejaz³

et al.

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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In this work we present the development of a system that can be used for the study, detection and classification of human heart sounds using digital signal processing and artificial intelligence techniques. The design and implementation of such system is broken down into two processes: digital signal processing part and artificial intelligence part. The ultimate goal of the project is to develop an intelligent system that can be used for the detection and classification of various types of human heart murmurs. *

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Heart diseases diagnosis using HMM

Cited by 15 publications

References 6 publications

Autonomas Fault Diagnosis System for Cellular Networks Based on Hidden Markov Model

Autonomas Fault Diagnosis System for Cellular Networks Based on Hidden Markov Model

Detection of heart condition by time and frequency based template

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

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