Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

Authors:
Harun Uğuz;Gür Emre Güraksın;Uçman Ergün;Rıdvan Saraçoğlu
Affiliations:
Department of Computer Engineering, Selçuk University, Konya, Turkey;Department of Computer Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey;Department of Biomedical Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey;Department of Electronic and Computer Education, Selçuk University, Konya, Turkey
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

Citing 20
Cited 1

Digital signal processing (3rd ed.): principles, algorithms, and applications

Digital signal processing (3rd ed.): principles, algorithms, and applications
Application of Periodogram and AR Spectral Analysis to EEG Signals

Journal of Medical Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms

Genetic Algorithms
A genetic classification error method for speech recognition

Signal Processing
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Recognising handwritten Arabic manuscripts using a single hidden Markov model

Pattern Recognition Letters
A recurrent neural network classifier for Doppler ultrasound blood flow signals

Pattern Recognition Letters
Lexicon and hidden Markov model-based optimisation of the recognised Sinhala script

Pattern Recognition Letters
Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

Computers in Biology and Medicine
Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals

Expert Systems with Applications: An International Journal
Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

Computers in Biology and Medicine
Simultaneous gesture segmentation and recognition based on forward spotting accumulative HMMs

Pattern Recognition
A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Computer Methods and Programs in Biomedicine
Detection of heart valve diseases by using fuzzy discrete hidden Markov model

Expert Systems with Applications: An International Journal
Statistics over features for internal carotid arterial disorders detection

Computers in Biology and Medicine
A biomedical system based on fuzzy discrete hidden Markov model for the diagnosis of the brain diseases

Expert Systems with Applications: An International Journal
Detection of machine failure: Hidden Markov Model approach

Computers and Industrial Engineering
Selection of optimal AR spectral estimation method for internal carotid arterial Doppler signals using Cramer-Rao bound

Computers and Electrical Engineering
A Chinese sign language recognition system based on SOFM/SRN/HMM

Pattern Recognition

A low-cost screening method for the detection of the carotid artery diseases

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.