A deep convolutional neural network for automated vestibular disorder classification using VNG analysis

Slama, Amine Ben; Mouelhi, Aymen; Sahli, Hanene; Zeraii, Abderrazek; Marrakchi, J.; Trabelsi, H.

doi:10.1080/21681163.2019.1699165

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…Although neural networks have previously been applied to several tasks involving eye-movement signals, such as classifying normal versus abnormal nystagmus during caloric tests [16] and detecting saccades [34], this study is the first example of 1D CNNs applied to the task of detecting entire nystagmus waveforms from within hours of normal eye-movement data. While heuristic approaches to detecting optokinetic nystagmus have been shown to yield high levels of classification accuracy (89.13% sensitivity and 98.54% specificity in [10], and 93% accuracy in [12]), these results are not comparable with our study as the data was captured during optokinetic tests and are extremely short in duration (8 seconds each in [10], compared to up to 24 hours in our longest example and almost an hour in the shortest).…”

Section: Discussionmentioning

confidence: 99%

“…Such approaches, while effective when applied to short-term data that are known to contain nystagmus, can be slow to process large quantities of data and may produce many false positive detections when applied to highly variable long-term data. 1D Convolutional Neural Networks (CNNs) have also been used to classify diseased versus healthy induced nystagmus signals captured using video goggles in clinical settings [16]. Despite this technique not being used to identify or confirm the presence of nystagmus, it is reassuring that it achieved a classification accuracy of 96.36% for discriminating signals from healthy people with those from patients suffering from Vestibular Neuritis and Ménière's disease.…”

Section: Introductionmentioning

confidence: 99%

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1D Convolutional Neural Networks for Detecting Nystagmus

Newman

Phillips

Cox

2021

IEEE J. Biomed. Health Inform.

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Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the innerear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network Architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

1D Convolutional Neural Networks for Detecting Nystagmus

Newman

Phillips

Cox

2021

IEEE J. Biomed. Health Inform.

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“…Several classification methods can be employed for pattern recognition. Nevertheless, for multiple categorization works [32,33], it has been confirmed that DNN presents a highquality tool. To acquire authentic classification results, we have selected the PG region of different images for assembling the training dataset of the network.…”

Section: Classification Of Pg Lesion Using Dbn-dnn Classifiermentioning

confidence: 98%

Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework

Slama¹,

Mbarki²,

Seddik³

et al. 2021

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Manual assessment of parotid gland status from magnetic resonance imaging is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to get consistent, objective and rapid diagnoses of parotid gland tumor. In this kind of cancer, a large variety in their characteristics, that brings various difficulties for traditional image analysis methods. In this paper, we propose an automatic method to perform both segmentation and classification of Parotid gland region in order to give quantitative assessment and uniform indicators of PGs status that will help pathologists in their diagnostic. The experimental result illustrates the high accuracy of the results of the proposed method compared to the ground truth. Furthermore, a comparative study with existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.

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“…Slama et al [27], [28] used a multilayer neural network (MNN) and the recorded parameters from Video Nysta Geographic (VNG) data to analyze the nystagmus to diagnose whether one person has a vestibular disorder or normal. Nystagmus signal from Videonystagmography (VNG) device has been analyzed by CNN-based method to classify two classes of vestibular disorder [29]. Lim et al [30] introduced a more complete approach that does not just only classify the nystagmus, but also diagnoses the final BPPV class using various ad-hoc techniques.…”

Section: A Bppv Analysismentioning

confidence: 99%

LAD: A Hybrid Deep Learning System for Benign Paroxysmal Positional Vertigo Disorders Diagnostic

Pham

Mina

et al. 2022

IEEE Access

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Herein, we introduce "Look and Diagnose" (LAD), a hybrid deep learning-based system that aims to support doctors in the medical field for diagnosing effectively the Benign Paroxysmal Positional Vertigo (BPPV) disorder. Given the body postures of the patient in the Dix-Hallpike and lateral head turns test, the visual information of both eyes is captured and fed into LAD for analyzing and classifying into one of six possible disorders which the patient might be suffering from. The proposed system consists of two streams: (1) an RNN-based stream that takes raw RGB images of both eyes to extract visual features and optical flow of each eye followed by ternary classification to determine left/right posterior canal (PC) or other; and (2) pupil detector stream that detects the pupil when it is classified as Non-PC and classifies the direction and strength of the beating to categorize the Non-PC types into the remaining four classes: Geotropic BPPV (left and right) and Apogeotropic BPPV (left and right). Experimental results show that with the given body postures of the patient, the system is capable of accurately classifying given BPPV disorder into the six types of disorder with an accuracy of 91% on the validation set. The proposed method can successfully classify disorders with an accuracy of 93% for the Posterior Canal disorder and 95% for the Geotropic and Apogeotropic disorder, paving a potential direction for research with the medical data.

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A deep convolutional neural network for automated vestibular disorder classification using VNG analysis

Cited by 13 publications

References 17 publications

1D Convolutional Neural Networks for Detecting Nystagmus

1D Convolutional Neural Networks for Detecting Nystagmus

Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework

LAD: A Hybrid Deep Learning System for Benign Paroxysmal Positional Vertigo Disorders Diagnostic

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