Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway

• Published in: Journal of neurology Vol. 266; no. Suppl 1; pp. 108 - 117
• Main Authors: Ahmadi, Seyed-Ahmad, Vivar, Gerome, Frei, Johann, Nowoshilow, Sergej, Bardins, Stanislav, Brandt, Thomas, Krafczyk, Siegbert
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2019; Springer Nature B.V
• Subjects: Adult; Artificial intelligence; Atrophy; Cerebellum; Classification; Cohort Studies; Data Mining - methods; Data processing; Diagnosis, Computer-Assisted - methods; Embedding; Female; Humans; Learning algorithms; Machine Learning; Male; Mapping; Medicine; Medicine & Public Health; Movement disorders; Nervous System Diseases - diagnosis; Nervous System Diseases - physiopathology; Neurodegenerative diseases; Neurological diseases; Neurology; Neuroradiology; Neurosciences; Nystagmus; Original Communication; Parkinson's disease; Polyneuropathy; Postural Balance - physiology; Rubber; Stochasticity; Tremor; Vertigo; Visualization; Static posturography; Body sway; Neurological stance and gait disorders; Machine learning
• ISSN: 0340-5354; 1432-1459
• DOI: 10.1007/s00415-019-09458-y

We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS,
n
 = 49), distal sensory polyneuropathy (PNP,
n
 = 12), anterior lobe cerebellar atrophy (CA,
n
 = 48), downbeat nystagmus syndrome (DN,
n
 = 16), primary orthostatic tremor (OT,
n
 = 25), Parkinson’s disease (PD,
n
 = 27), phobic postural vertigo (PPV
n
 = 59) and healthy controls (HC,
n
 = 57). We classify disorders and rank sway features using supervised machine learning. We compute a continuous, human-interpretable 2D map of stance disorders using t-stochastic neighborhood embedding (t-SNE). Classification of eight diagnoses yielded 82.7% accuracy [95% CI (80.9%, 84.5%)]. Five (CA, PPV, AVS, HC, OT) were classified with a mean sensitivity and specificity of 88.4% and 97.1%, while three (PD, PNP, and DN) achieved a mean sensitivity of 53.7%. The most discriminative stance condition was ranked as “standing on foam-rubber, eyes closed”. Mapping of sway path features into 2D space revealed clear clusters among CA, PPV, AVS, HC and OT subjects. We confirm previous claims that machine learning can aid in classification of clinical sway patterns measured with static posturography. Given a standardized, long-term acquisition of quantitative patient databases, modern machine learning and data analysis techniques help in visualizing, understanding and utilizing high-dimensional sensor data from clinical routine.