Deep learning based torsional nystagmus detection for dizziness and vertigo diagnosis

Dizziness and vertigo are common clinical symptoms and typical complaints of many vestibular diseases. In the bedside examination of dizziness and vertigo, nystagmus is the most sensitive and specific sign of vestibular lesions. The measurement of nystagmus pattern by infrared video goggle collected in clinic can provide a valuable diagnostic information for dizziness and vertigo. This paper mainly studies the automatic detection of torsional BPPV nystagmus based on deep learning, thus assisting clinicians diagnose dizziness and vertigo conveniently. In order to eliminate the invalid frames from the blinking of patients under observation, a convolutional neural network(ConvNet) based eye movement video condensation approach is proposed. When calibrating the moving pupil in the captured frame sequence, the Hough transform and trajectory tracking based on template matching are well combined to improve the robustness to eyelash occlusion and pupil deformation. In addition, the optical flow field of moving eyeball is exploited to characterize the torsion motion of torsional nystagmus, based on which a Torsion-aware Bi-Stream Identification Network (TBSIN) model is proposed. Furthermore, through label-error correction based on temporal consistency, we can merge multiple continuous torsional frames into torsional nystagmus segments for clinical diagnosis. Experiments are conducted on a clinically collected torsional nystagmus video dataset and promising experimental results show the effectiveness of the proposed approach. In particular, we achieve 85.73% and 81.00% in view of Accuracy and F1 measurements for frame-level identification, as well as IOU performance 67.45% for final torsional nystagmus segment localization.