1D Convolutional Neural Networks for Detecting Nystagmus.

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Diagnostics

Background:

• Vertigo affects 25% of individuals, presenting diagnostic challenges for clinicians.
• Nystagmus, characterized by involuntary eye flickering, signifies inner-ear dysfunction and is a key indicator of vertigo.
• Accurate diagnosis of vertigo and its underlying causes, like nystagmus, is crucial for effective patient management.

Purpose of the Study:

• To explore the application of Deep Neural Network (DNN) architectures for automated nystagmus detection.
• To develop and evaluate methods for training DNNs with limited clinical data for nystagmus identification.
• To assess the accuracy of DNNs in detecting both induced and pathological nystagmus.

Main Methods:

• Utilized data from a novel medical device recording head and eye movements during a clinical investigation.
• Employed data augmentation techniques to generate new training samples from limited existing data (average 11 mins of nystagmus per subject).
• Conducted cross-fold validation experiments to evaluate the performance of the trained DNN models.

Main Results:

• Achieved an average F1 score of 0.59 (SD = 0.24) across four cross-validation folds, indicating modest accuracy in nystagmus detection.
• Demonstrated the model's capability to identify periods of pathological nystagmus in a Ménière's Disease patient.
• Successfully identified pathological nystagmus despite the network being trained on differently induced nystagmus samples.

Conclusions:

• Deep Neural Networks offer a promising approach for the automated detection of nystagmus, a critical sign of vertigo.
• The developed methods enable effective training of DNNs even with scarce clinical data, overcoming a common limitation in medical AI.
• This AI-driven approach holds potential for improving the diagnosis and management of inner-ear disorders causing vertigo.