Current perception threshold and reaction time in the assessment of sensory peripheral nerve fibers through sinusoidal electrical stimulation at different frequencies

INTRODUCTION: The Perception Sensory Threshold (ST) for sinusoidal current stimuli at 5, 250, and 2,000 Hz is commonly used in the assessment of peripheral nerve fibers (C, Aδ, and Aβ, respectively). However, the neuroselectivity of these frequencies is far from consensus. In addition, Reaction Time (RT) measurements suggest that 2,000 Hz stimuli excite Aβ-fibers, 250 Hz Aβ- or Aδ-fibers, as well as 5 Hz Aβ-, Aδ- or C-fibers. Therefore, we suppose that the sinusoidal current neuroselectivity may be better observed if ST and RT parameters are jointly evaluated. In addition, we have investigated whether there are other sets of frequencies that could be used. METHODS: Thus this work investigates ST and RT for stimuli with frequency ranging from 1 to 3,000 Hz, on 28 healthy subjects aged from 19 to 44 years old (27.1±5.49). ST and RT dissimilarity among different frequencies was evaluated applying bi-dimensional Fisher Quadratic Discriminant. RESULTS: The lowest classification error (3.6%) was obtained for 1, 250, and 3,000 Hz. Error for 5, 250, and 2,000Hz was 16.7%. Stimulation frequency at 1 Hz evoked more sensations related to C-fibers (53% of reports) than to Aβ-fibers (36%). However, this behavior did not repeat itself at 5 Hz (only 21% of perceptions were related to C-fibers against 64% to Aβ-fibers). Sensations related to Aβ-fibers prevailed for the highest frequencies presented to the subjects (2,000 Hz - 82% and 3,000 Hz - 93%). Mean RT values showed a decreasing trend with frequency. CONCLUSION: These results suggest that frequencies 1, 250, and 3,000 Hz are more neuroselective than 5, 250, and 2,000 Hz for the evaluation of peripheral sensitive fibers. Furthermore, they show RT usefulness

Learning the optimal kernel for Fisher discriminant analysis via second order cone programming

Kernel Fisher discriminant analysis (KFDA) is a popular classification technique which requires the user to predefine an appropriate kernel. Since the performance of KFDA depends on the choice of the kernel, the problem of kernel selection becomes very important. In this paper we treat the kernel selection problem as an optimization problem over the convex set of finitely many basic kernels, and formulate it as a second order cone programming (SOCP) problem. This formulation seems to be promising because the resulting SOCP can be efficiently solved by employing interior point methods. The efficacy of the optimal kernel, selected from a given convex set of basic kernels, is demonstrated on UCI machine learning benchmark datasets.Fisher discriminant analysis Kernel methods Machine learning Kernel optimization Support vector machines Convex optimization Second order cone programming Semidefinite programming