Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke using Brainstem Auditory Evoked Potential

To evaluate the feasibility of brainstem auditory evoked potential (BAEP) for rehabilitation prognosis prediction in patients with ischemic stroke, 181 patients were tested using the Korean version of the modified Barthel index (K-MBI) at admission (basal K-MBI) and discharge (follow-up K-MBI). The BAEP measurements were performed within two weeks of admission on average. The criterion between favorable and unfavorable outcomes was defined as a K-MBI score of 75 at discharge, which was the boundary between moderate and mild dependence in daily living activities. The changes in the K-MBI scores (discharge–admission) were analyzed by nonlinear regression models, including the artificial neural network (ANN) and support vector machine (SVM), with the basal K-MBI score, age, and interpeak latencies (IPLs) of the BAEP (waves I, I–III, and III–V). When including the BAEP features, the correlations of the ANN and SVM regression models increased to 0.70 and 0.64, respectively. In the outcome prediction, the ANN model with the basal K-MBI score, age, and BAEP IPLs exhibited a sensitivity of 92% and specificity of 90%. Our results suggest that the BAEP IPLs used with the basal K-MBI score and age can play an adjunctive role in the prediction of patient rehabilitation prognoses.

The Difference of Brainstem Auditory Evoked Potential Latency in Diabetic Patient with Good and Poor Glycemic Control

BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder which may complicate other organs, including the nervous system. Literatures which discuss about DM complications in the peripheral nervous system are easy to find but not so many of the central nervous system. Central diabetic neuropathy is a new concept which could be detected by a simple and non-invasive method, called brainstem auditory evoked potential (BAEP). AIM: The aim of the study was to find differences in BAEP latencies of a diabetic patient with good and poor glycemic control. METHODS: This was a cross-sectional study of 80 patients who came for follow-up in diabetic center and neurology polyclinic at Sanglah Hospital, from April to July 2016. The subjects were divided into two groups, depending on their glycemic control, then having BAEP examination. RESULTS: The unpaired t-test found prolonged BAEP latencies (either peak latency of wave III, V, IPL I-III, III-V, and I-V) in both ears at the poor glycemic control group, but the results were not differed significantly (p > 0.05). CONCLUSION: BAEP wave latencies were found prolonged in DM patient with poor glycemic control but not statistically significant. Further evaluation of BAEP latencies in DM patients is needed with prolonged duration and their relation with other comorbid factors, especially smoking habit.