A new method to detect nystagmus for vertigo diagnosis system by eye movement velocity

Charoenpong, Theekapun; Pattrapisetwong, Preeyanan; Mahasitthiwat, Visan

doi:10.1109/mva.2015.7153161

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Charoenpong [ 21 ] proposed a method to detect involuntary eye movements with eye movement velocity. This method includes three main steps: pupil extraction, eye movement velocity calculation, and nystagmus detection.…”

Section: Introductionmentioning

confidence: 99%

Vertical Nystagmus Recognition Based on Deep Learning

Yang

2023

Sensors

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Vertical nystagmus is a common neuro-ophthalmic sign in vestibular medicine. Vertical nystagmus not only reflects the functional state of vertical semicircular canal but also reflects the effect of otoliths. Medical experts can take nystagmus symptoms as the key factor to determine the cause of dizziness. Traditional observation (visual observation conducted by medical experts) may be biased subjectively. Visual examination also requires medical experts to have enough experience to make an accurate diagnosis. With the development of science and technology, the detection system for nystagmus can be realized by using artificial intelligence technology. In this paper, a vertical nystagmus recognition method is proposed based on deep learning. This method is mainly composed of a dilated convolution layer module, a depthwise separable convolution module, a convolution attention module, a Bilstm−GRU module, etc. The average recognition accuracy of the proposed method is 91%. Using the same training dataset and test set, the recognition accuracy of this method for vertical nystagmus was 2% higher than other methods.

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Section: Introductionmentioning

confidence: 99%

Vertical Nystagmus Recognition Based on Deep Learning

Yang

2023

Sensors

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Section: Introductionmentioning

confidence: 99%

“…The literature [31][32][33][34] showed how eye movement and nystagmus videos can be utilized in dizziness diagnosis. Theekapun Charoenpong et al [32] proposed a method to diagnose vertigo by measuring the eye movement velocity of nystagmus. Videos of eye performance were recorded by infrared cameras, and diagnostic information was obtained in three steps: pupil extraction, eye movement velocity calculation and nystagmus detection.…”

Section: Introductionmentioning

confidence: 99%

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Mao

Liu

et al. 2020

IEEE Access

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Medical research confirms that eye movement abnormalities are related to a variety of psychological activities, mental disorders and physical diseases. However, as the specific manifestations of various diseases in terms of eye movement disorders remain unclear, the accurate diagnosis of diseases according to eye movement is difficult. In this paper, a deep neural network (DNN) method is employed to establish a disease discrimination model according to eye movement. First, multiple eye-tracking experiments are designed to obtain eye images. Second, pupil characteristics, including position and size, are extracted, and the feature vectors of eye movement are obtained from the normalized pupil information. Based on a long short-term memory (LSTM) network, a classifier that corresponds to each feature, which is referred to as a weak classifier, is built. The experimental samples are preclassified, and the classification ability of each weak classifier for different diseases is also calculated. Last, a strong classifier is achieved for disease discrimination by synthesizing all the weak classifiers and their classification abilities. By classification testing for three categories of healthy controls, brain injury patients and vertigo patients, the experimental results demonstrated the efficiency of this method. With the deep learning method, more medical information can be excavated from eye movement to improve the values in disease diagnosis.

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“…There is much previous work on the automated detection of nystagmus events from eye movement data [28,23,32,9,5]. Existing techniques usually involve the detection of saccades, by way of information contained in the velocity signal.…”

Section: Introductionmentioning

confidence: 99%

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Newman

Phillips

Cox

et al. 2019

Computers in Biology and Medicine

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A new method to detect nystagmus for vertigo diagnosis system by eye movement velocity

Cited by 7 publications

References 6 publications

Vertical Nystagmus Recognition Based on Deep Learning

Vertical Nystagmus Recognition Based on Deep Learning

Disease Classification Based on Synthesis of Multiple Long Short-Term Memory Classifiers Corresponding to Eye Movement Features

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

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