Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG. METHODS: Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects. RESULTS: For two-class classification, an accuracy of 0.94 was achieved in 10-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power. CONCLUSIONS: Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification. SIGNIFICANCE: Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.

Synthesis of Subject-Specific Human Balance Responses Using a Task-Level Neuromuscular Control Platform.

Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closed-loop simulations to inform the design of the task-level controller. The predicted muscle activations and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction.

Differentiating Walking from other Activities of Daily Living in Older Adults Using Wrist-based Accelerometers.

There is a significant body of literature demonstrating that accelerometers placed at various locations on the body can provide the data necessary to recognize walking. Most of the literature, however, either does not consider accelerometers placed at the wrist, or suggests that the wrist is not the ideal location. The wrist, however, is probably the most socially-acceptable location for a monitoring device. This study evaluates the possibility of using wrist accelerometers to recognize walking in the elderly during everyday life to evaluate the amount of time spent walking and, moreover, potentially recognize changes in stability that might lead to falls. Thirty elderly individuals aged 65 years and older were asked to wear a wrist accelerometer for four hours each while simultaneously being video recorded as they went about their normal daily activities. Accelerometer data were then analyzed using both frequency- and time-domain analyses. Particular attention was given to methods capable of being calculated on the wrist device so that future work will not require streaming large amounts of data from the device to the central server. Frequency based analysis to characterize walking in the test set yielded results of 98% area under the receiver operating characteristic curve (AUC). Using a time-series algorithm limited to features calculable on the wrist device, moreover, achieved an AUC of 90%. A small, socially-acceptable, wrist-based device, therefore, can successfully be used to differentiate walking from other activities of daily living in older adults. These findings may enable improved gait monitoring and efforts in falls prevention.