High-intensity transient signals (HITS) detected by transcranial Doppler (TCD) ultrasound may correspond to artifacts or to microembolic signals, the latter being either solid or gaseous emboli. The goal of this study was to assess what can be achieved with an automatic signal processing system for artifact/microembolic signals and solid/gas differentiation in different clinical situations. The authors studied 3,428 HITS in vivo in a multicenter study, i.e., 1,608 artifacts in healthy subjects, 649 solid emboli in stroke patients with a carotid stenosis, and 1,171 gaseous emboli in stroke patients with patent foramen ovale. They worked with the dual-gate TCD combined to three types of statistical classifiers: binary decision trees (BDT), artificial neural networks (ANN), and support vector machines (SVM). The sensitivity and specificity to separate artifacts from microembolic signals by BDT reached was 94% and 97%, respectively. For the discrimination between solid and gaseous emboli, the classifier achieved a sensitivity and specificity of 81% and 81% for BDT, 84% and 84% for ANN, and 86% and 86% for SVM, respectively. The current results for artifact elimination and solid/gas differentiation are already useful to extract data for future prospective clinical studies.

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-The sensitivity of a simulated surface electromyogram (SEMG) to changes in conduction velocity (CV) of individual motor units was assessed. Changes in the conduction velocity measurement and the median frequency and spectral compression measures of both the amplitude and power spectra of the SEMG were calculated. Results show that motor units close to the electrodes dominate the SEMG signal. Results also show that for changes in CV of individual motor units the CV measurement and the spectral measures differ. In the case of uniform changes in the CV of all motor units, changes in these measures are similar and unity sensitivity is observed. Indications from this preliminary study highlight the need for care when deducing the physiological significance of changes observed in the SEMG. Keywords -Electromyogram, conduction velocity, spectral measures, sensitivity. I. INTRODUCTIONNeedle electromyography is a conventional electrodiagnostic evaluation tool with a proven and long established history in the diagnosis and treatment of disorders of nerve and muscle, [1]. According to the same report from the American Association of Electodiagnostic Medicine, surface electromyography (EMG) is not a valid clinical tool for the diagnosis and treatment of muscle and nerve disorders, as many questions are as yet unanswered. Some investigators believe electrophysiological diagnosis tests are obsolete, [2]. Conflicting opinions exist however, with Rainoldi and co-authors claiming that EMG is a "very promising" clinical technique for detecting information about the global activity of the muscle, [3]. A changing muscle fiber conduction velocity (CV) is the most direct electroneurophysiological sign of a changing excitability of the muscle fiber membrane, [4]. The question of how best to record changes in muscle fiber conduction velocity naturally arises. The limited extent to which changes in muscle physiology may be inferred from the recorded surface signal needs to be recognised. Noninvasive measurement of conduction velocity is often perceived to be the most valuable tool in EMG. . This is largely explained by concluding that the spectral measures are more influenced by other factors, including firing rates, recruitment, synchronisation and motor unit action potential (MUAP) shape. The EMG signal is sensitive to all these factors and is also strongly dominated by motor units (MU's) located in the vicinity of the recording electrodes. In a simple MUAP model, Fuglevand and colleagues, demonstrated that only MU's within 10-12mm of the electrodes would contribute any significant energy to the surface signal, [7]. Despite this awareness of the limited pick-up range of surface electrodes, results from EMG analysis continue to be interpreted as indicative of the overall physiological state of the muscle.A better understanding of the relationship between CV and measures of spectral compression require insight into the factors influencing the EMG signal. This relationship has been partially investigated using a mathematical mo...