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Sixty digits undergoing terminalization were randomized to undergo either simple transection of the digital nerves (control) or direct suture of the nerves to each other (centro-central union, CCU). Thirty-one digits were available for review with a minimum follow-up of one year following the surgery. Assessment was by visual analogue scales (VAS) and clinical examination. Measurements included static two-point discrimination (S2PD) for sensation, dolorimetry for stump tenderness and grip-strength.Resting pain, cold intolerance and perceived tenderness were comparable in the two groups. Perceived and observed touch sensitivity were better in the control group but the stumps in this group were more tender than those in the CCU group. We conclude that CCU results in more comfortable stumps but causes greater loss of sensitivity. It is a simple and worthwhile adjunct to digital amputation.

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Breath sounds have been shown very valuable for diagnosis of obstructive sleep apnea. In this study, we present a subject independent method for automatic classification of breath and related sounds during sleep. An experienced operator manually labelled segments of breath sounds from 11 sleeping subjects as: inspiration, expiration, inspiratory snoring, expiratory snoring, wheezing, other noise, and non-audible. Ten features were extracted and fed into 3 different classifiers: naïve Bayes, Support Vector Machine, and Random Forest. Leave-one-out method was used in which data from each subject, in turn, is evaluated using models trained with all other subject. Mean accuracy for concurrent classification of all 7 classes reached 85.4%. Mean accuracy for separating data into 2 classes, snoring and non-snoring, reached 97.8%. To our knowledge, these are the highest accuracies achieved in automatic classification of all breath sounds components concurrently and for snoring, in a subject independent model.