1D Convolutional Neural Networks for Detecting Nystagmus

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 inner-ear, 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.

[1]  Benjamin Thompson,et al.  An Optokinetic Nystagmus Detection Method for Use With Young Children , 2015, IEEE Journal of Translational Engineering in Health and Medicine.

[2]  Michael Tschannen,et al.  Convolutional recurrent neural networks for electrocardiogram classification , 2017, 2017 Computing in Cardiology (CinC).

[3]  Kenneth Holmqvist,et al.  gazeNet: End-to-end eye-movement event detection with deep neural networks , 2018, Behavior Research Methods.

[4]  Theekapun Charoenpong,et al.  A new method to detect nystagmus for vertigo diagnosis system by eye movement velocity , 2015, 2015 14th IAPR International Conference on Machine Vision Applications (MVA).

[5]  U. Rajendra Acharya,et al.  A deep convolutional neural network model for automated identification of abnormal EEG signals , 2018, Neural Computing and Applications.

[6]  Adrian James,et al.  Menière's disease. , 2007, BMJ clinical evidence.

[7]  L. Walther.  Current diagnostic procedures for diagnosing vertigo and dizziness , 2017, GMS current topics in otorhinolaryngology, head and neck surgery.

[8]  Amine Ben Slama,et al.  A deep convolutional neural network for automated vestibular disorder classification using VNG analysis , 2020, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[9]  Su-lin Zhang,et al.  Repeated courses of intratympanic dexamethasone injection are effective for intractable Meniere’s disease , 2017, Acta oto-laryngologica.

[10]  D. Chicco,et al.  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.

[11]  Benjamin Thompson,et al.  A method for detecting optokinetic nystagmus based on the optic flow of the limbus , 2014, Vision Research.

[12]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[13]  Lovekesh Vig,et al.  Anomaly detection in ECG time signals via deep long short-term memory networks , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[14]  J. Andrew Bangham,et al.  Morphological scale-space preserving transforms in many dimensions , 1996, J. Electronic Imaging.

[15]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[16]  A. Mallinson,et al.  Migraine and Vertigo: A Marriage of Convenience? , 2010, Headache.

[17]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[18]  Serkan Gurkan,et al.  Design of a Novel Efficient Human–Computer Interface: An Electrooculagram Based Virtual Keyboard , 2010, IEEE Transactions on Instrumentation and Measurement.

[19]  Jacob L. Newman,et al.  An investigation into the diagnostic accuracy, reliability, acceptability and safety of a novel device for Continuous Ambulatory Vestibular Assessment (CAVA) , 2019, Scientific Reports.

[20]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[21]  Qiang Zhang,et al.  Classification of ECG signals based on 1D convolution neural network , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[22]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[23]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.

[24]  Sangdeok Kim,et al.  Epileptic seizure detection for multi-channel EEG with deep convolutional neural network , 2018, 2018 International Conference on Electronics, Information, and Communication (ICEIC).

[25]  Andrew Bath,et al.  Automatic nystagmus detection and quantification in long-term continuous eye-movement data , 2019, Comput. Biol. Medicine.

[26]  Nicholas Ayache,et al.  Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition , 2017, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[27]  Tomasz Przybyła,et al.  An automatic saccadic eye movement detection in an optokinetic nystagmus signal , 2014, Biomedizinische Technik. Biomedical engineering.

[28]  Amine Ben Slama,et al.  Features extraction for medical characterization of nystagmus , 2016, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[29]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[30]  Jorge Otero-Millan,et al.  Detection of Saccades and Quick-Phases in Eye Movement Recordings with Nystagmus , 2020, ETRA Short Papers.

[31]  S. Sandhaus.  Stop the spinning: diagnosing and managing vertigo. , 2002, The Nurse practitioner.

[32]  Eric G. Johnson,et al.  Differential diagnosis and management of a patient with peripheral vestibular and central nervous system disorders: a case study , 2010, The Journal of manual & manipulative therapy.