Study the different level of eye movement based on electrooculography (EOG) technique

Noor, Nurul Muthmainnah Mohd; Kamarudin, Muhammad Qawiem bin

doi:10.1109/iecbes.2016.7843559

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…Definition (6) and it is the difference between the time at which volunteer fixes the gaze on the stimulus (t iniFIX ) and the time in which the gaze starts the movement to the opposite direction (t endFIX ):…”

Section: Gain =mentioning

confidence: 99%

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Measurement and Analysis of Eye Movements Performance to Predict Healthy Brain Aging

2020

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Objective: This article presents the healthy pattern of eye movements (EM) in 145 healthy volunteers from 20 to 86 years old. Volunteers were classified into four groups according to their age. A saccadic paradigm, in horizontal and vertical axes, was performed. We described a pattern behavior in healthy volunteers to demonstrate that it can be used to measure the aging and functionality of the brain. Methods: A gaze-tracker based in video-oculography technology was used. Before EM tests, clinical data were collected, participants performed a cognitive test to discard subtle abnormalities and signed an informed consent form. To demonstrate the relationship between EM and brain aging, a linear or quadratic model was computed and statistical analysis among groups was presented. Conclusion: EM variables could be considered as biomarkers to measure the aging effect and functionality of the brain. Video-oculography is a suitable technique for measuring EM in clinical practice. Significance: The ocular healthy pattern as well as the methodology followed in this clinical study, is the base for ongoing studies aiming to incorporate EM analysis at routine practice as markers in early diagnosis for patients with neurodegenerative diseases like Alzheimer's dementia or Parkinson's disease.INDEX TERMS Aging, biomarker, measurement techniques, statistical analysis, healthy pattern.

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Section: Gain =mentioning

confidence: 99%

“…The EOG system measures the electrical signal generated when the eyes move. Here, some electrodes must be placed around the eyes in contact with the skin [2]- [6]. The VOG systems consist of, at least, one camera that records the eye/s movement.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Analysis of Eye Movements Performance to Predict Healthy Brain Aging

2020

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“…For recording EOG at 128 Hz, the electrodes are connected to Mobi from Twente Medical Systems International (TMSI). Noor et al 18 proposed an Electrooculography (EOG) technique that uses simple Ag/Ag Cl electrodes to pick up the deflected signal caused by the eyes. Bipolar leads are used to record both horizontal and vertical movements of the eyes.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Archana

Rajalakshmi

2022

Proc Inst Mech Eng H

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Single-channel Electrooculogram (EOG) is proposed for detecting diabetic retinopathy. The Corneal-retinal potential of the eyes plays a vital role in the acquisition of Electrooculography. Diabetes is the most prevalent disease and for one out of three people with diabetes above 40 years, diabetic retinopathy occurs. It is necessary for the early detection of diabetic retinopathy as it is one of the primary reasons for blindness in the population. The potential difference between cornea and retina leads to the acquisition of EOG signal. The proposed study aims to design a low-cost miniaturized hardware circuit to obtain EOG signal using second order filters without compromising in accuracy of the outcome signal and to classify the signal into normal and diabetic retinopathy subjects by extracting the statistical features like kurtosis, mean, median absolute deviation, standard deviation, and range from software filtered EOG signal. Among the classifiers used, Support vector machine (SVM) shows a higher accuracy of 93.33%. The sensitivity, specificity and Area Under Curve (AUC) values of SVM are 96.43%, 90.625%, 0.93% is considered as more favorable outcome for the proposed method and it supports the developed prototype and processing methodology. The novelty of the research is based on proposing and exploring a non-invasive methodology for Diabetic retinopathy diagnosis based on EOG signal. Thus, the designed hardware is simple in operation and cost effective, provides an affordable and non-invasive diagnostic tool for diabetic retinopathy patients

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“…The potential difference between cornea and retina voltages obtained using electrodes placed on lobes of the subjects [8] is estimated by Electrooculography (EOG) approach. The subsequent sign is known as the electrooculogram and its applications lies in ophthalmological verdict and in recording eye movements.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Brahmaiah¹,

Yarlagadda²,

Prasad³

2021

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

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Study the different level of eye movement based on electrooculography (EOG) technique

Cited by 8 publications

References 11 publications

Measurement and Analysis of Eye Movements Performance to Predict Healthy Brain Aging

Measurement and Analysis of Eye Movements Performance to Predict Healthy Brain Aging

Non-invasive technique to detect diabetic retinopathy based on Electrooculography signal using machine learning classifiers

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

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