An expert system based on least square support vector machines for diagnosis of the valvular heart disease

Hanbay, Davut

doi:10.1016/j.eswa.2008.04.010

Cited by 50 publications

(28 citation statements)

References 20 publications

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“…리아프노프 지수 [4,5], 상관 차원 [6], 프랙탈 차원 [7], 근사엔트로피 [8] 등이 뇌파를 분 석하는데 사용되었다. 분류기로써 support vector machine (SVM) [9,10], least square support vector machine (LS-SVM) [11,12], artificial neural network (ANN) [13,14] …”

Section: 서론unclassified

Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform

Lee¹

2016

Journal of Digital Convergence

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주제어 : 간질, 퍼지신경망, 웨이블릿 변환, 힐버트 변환, 뇌파Abstract This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

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Section: 서론unclassified

Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform

Lee¹

2016

Journal of Digital Convergence

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“…Instead of using this large set as features, new features that are extracted from wavelet coefficients can be employed. The entropy of a signal or signal sub-bands are used for Doppler ultrasound data successfully in the literature [25,26]. Entropy is defined as the quantity of uncertainty, unpredictability, and randomness for an event.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis

et al. 2011

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Urinary incontinence is a common female disorder. Although generally not a serious condition, it negatively affects the lifestyle and daily activity of subjects. Stress urinary incontinence (SUI) is the most versatile of several incontinence types and is distinguished by physical degeneration of the continence-providing mechanism. Some surgical treatment methods exist, but the success of the surgery mainly depends upon a correct diagnosis. Diagnosis has two major steps: subjects who are suffering from true SUI must be identified, and the SUI sub-type must be determined, because each sub-type is treated with a different surgery. The first step is straightforward and uses standard identification methods. The second step, however, requires invasive, uncomfortable urodynamic studies that are difficult to apply. Many subjects try to cope with the disorder rather than seek treatment from health care providers, in part because of the invasive diagnostic methods. In this study, a diagnostic method with a success rate comparable to that of urodynamic studies is presented. This new method has some advantages over the current one. First, it is noninvasive; data are collected using Doppler ultrasound recording. Second, it requires no special tools and is easy to apply, relatively inexpensive, faster and more hygienic.

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“…LSSVM has been used successfully in various areas of pattern recognition and regression problems (Hanbay, 2009;Kang et al, 2008). LSSVM encompasses similar advantages to SVM, but its additional advantage is that it only requires the solving of a set of linear equations, which is much easier and computationally simpler.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

Ismail

Shabri

Samsudin³

2012

Hydrol. Earth Syst. Sci.

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Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

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An expert system based on least square support vector machines for diagnosis of the valvular heart disease

Cited by 50 publications

References 20 publications

Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform

Classification of Epileptic Seizure Signals Using Wavelet Transform and Hilbert Transform

Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

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