Name
Abstract | ||
|---|---|---|
In this paper a system for detecting the possibility of eye dystonia, a neural disorder that causes a person to blink excessively, by eye movement analysis is proposed. The designed system counts the number of blinks for a particular time interval and thus detecting the risk of eye dystonia. Electrooculogram (EOG) signal is recorded to collect eye movement data using a laboratory developed acquisition system. Radial Basis Function(RBF) kernel Support Vector Machine (SVM) classifier and Feed forward neural network classifier is used to classify blinks from other types of eye movements using combinations of Wavelet coefficients, Autoregressive (AR) parameters and Hjorth parameters with Power Spectral Density (PSD) as signal features. A maximum average accuracy of 95.33% over all classes and participants is obtained using RBF-SVM classifier with a feature space of AR parameters of order 5 and PSD taken together. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICAPR.2015.7050712 | Advances in Pattern Recognition |
Keywords | Field | DocType |
biomechanics,diseases,electro-oculography,medical disorders,medical signal processing,neurophysiology,patient diagnosis,radial basis function networks,support vector machines,ar parameters,eog signal,hjorth parameters,psd signal features,rbf kernel svm,rbf kernel support vector machine,rbf-svm classifier,autoregressive parameters,blink classification,blink count system,electrooculogram based blink detection,electrooculogram signal,excessive blinking,eye dystonia possibility detection,eye dystonia risk detection,eye movement analysis,eye movement data collection,eye movement types,feed forward neural network classifier,laboratory developed data acquisition system,maximum average accuracy,neural disorder possibility detection,neural disorder risk detection,power spectral density,radial basis function,support vector machine classifier,wavelet coefficient combinations,autoregressive parameters(ar),blink detection,electrooculogram (eog),eye dystonia,power spectral density (psd),support vector machine (svm),wavelet transform,electric potential,feature extraction,kernel,electrodes | Autoregressive model,Feedforward neural network,Feature vector,Pattern recognition,Computer science,Support vector machine,Electrooculography,Eye movement,Hjorth parameters,Artificial intelligence,Wavelet | Conference |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anwesha Banerjee | 1 | 3 | 1.78 |
| Monalisa Pal | 2 | 12 | 3.28 |
| D. N. Tibarewala | 3 | 68 | 11.88 |
| Amit Konar | 4 | 1859 | 140.38 |