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Slow biphasic complexes (SBC) have been identified in the EEG of patients suffering for inflammatory brain diseases. Their amplitude, location and frequency of appearance were found to correlate with the severity of encephalitis. Other characteristics of SBCs and of EEG traces of patients could reflect the grade of pathology. Here, EEG rhythms are investigated together with SBCs for a better characterization of encephalitis. EEGs have been acquired from pediatric patients: ten controls and ten encephalitic patients. They were split by neurologists into five classes of different severity of the pathology. The relative power of EEG rhythms was found to change significantly in EEGs labeled with different severity scores. Moreover, a significant variation was found in the last seconds before the appearance of an SBC. This information and quantitative indexes characterizing the SBCs were used to build a binary classification decision tree able to identify the classes of severity. True classification rate of the best model was 76.1% (73.5% with leave-one-out test). Moreover, the classification errors were among classes with similar severity scores (precision higher than 80% was achieved considering three instead of five classes). Our classification method may be a promising supporting tool for clinicians to diagnose, assess and make the follow-up of patients with encephalitis.

The echocardiographic estimation of right atrial pressure (RAP) is based on the size and inspiratory collapse of the inferior vena cava (IVC). However, this method has proven to have limits of reliability. The aim of this study is to assess feasibility and accuracy of a new semi-automated approach to estimate RAP. Standard acquired echocardiographic images were processed with a semi-automated technique. Indexes related to the collapsibility of the vessel during inspiration (Caval Index, CI) and new indexes of pulsatility, obtained considering only the stimulation due to either respiration (Respiratory Caval Index, RCI) or heartbeats (Cardiac Caval Index, CCI) were derived. Binary Tree Models (BTM) were then developed to estimate either 3 or 5 RAP classes (BTM3 and BTM5) using indexes estimated by the semi-automated technique. These BTMs were compared with two standard estimation (SE) echocardiographic methods, indicated as A and B, distinguishing among 3 and 5 RAP classes, respectively. Direct RAP measurements obtained during a right heart catheterization (RHC) were used as reference. 62 consecutive 'all-comers' patients that had a RHC were enrolled; 13 patients were excluded for technical reasons. Therefore 49 patients were included in this study (mean age 62.2 ± 15.2 years, 75.5% pulmonary hypertension, 34.7% severe left ventricular dysfunction and 51% right ventricular dysfunction). The SE methods showed poor accuracy for RAP estimation (method A: misclassification error, ME = 51%, R 2  = 0.22; method B: ME = 69%, R 2  = 0.26). Instead, the new semi-automated methods BTM3 and BTM5 have higher accuracy (ME = 14%, R 2  = 0.47 and ME = 22%, R 2  = 0.61, respectively). In conclusion, a multi-parametric approach using IVC indexes extracted by the semi-automated approach is a promising tool for a more accurate estimation of RAP.

Surface electromyogram (EMG) has a relatively large pick-up volume, reflecting the activity of muscle tissue placed quite far from the electrodes. This could be beneficial when the global muscle activity is of interest, but it is a limitation when selective information is needed. The EMG from muscles that are neighbors of the one of interest is called crosstalk. Its interpretation, identification, quantification and removal have been the objectives of many works in the literature. However, it is still considered as an open problem, with effects that are difficult to predict. In this paper, the problem of crosstalk is discussed and the main literature is reviewed. Finally, a few recent techniques are introduced that are potentially relevant to quantify or reduce it.

Interference surface electromyogram (EMG) reflects many bioelectric properties of active motor units (MU), which are however difficult to estimate due to the asynchronous summation of their discharges. This paper introduces a deconvolution technique to estimate the cumulative firings of MUs. Tests in simulations show that the power spectral density of the estimated MU firings has a low-frequency peak corresponding to the mean firing rate of MUs in the detection volume of the recording system, weighted by the amplitudes of MU action potentials. The peak increases in amplitude and its centroid shifts to a higher frequency when MU synchronization is simulated (mainly due to the shift of discharges of large MUs). The peak is found even at high force levels, when such a contribution does not emerge from the EMG. This result is also confirmed in preliminary applications to experimental data. Moreover, the simulated cumulative firings of MUs are estimated with a correlation above 90% (considering frequency contributions up to 150 Hz), for all force levels. The method requires a single EMG channel, thus being feasible even in applied studies using simple recording systems. It may open many potential applications, e.g., in the study of the modulation of MU firing rate induced by either fatigue or pathology and in coherency analysis. Graphical Abstract Examples of application of the deconvolution (Deconv) algorithm and comparison with the cumulative firings and the cumulated weighted firings (CWF, i.e., each firing pattern is weighted by the root mean squared amplitude of the corresponding MU action potential). Portions of data are shown on the left, the power spectral densities (PSD) on the right (Welch method applied to 3 s of data, sub-epochs of 0.5 s, mean value removed from each of them, 50% of overlap). A) Simulated signal (50% of maximal voluntary contraction, MVC) with random MU firings. B) Simulated signal (50% MVC) with a level of synchronization equal to 10%. C) Experimental data from vastus medialis at 40% MVC (data decomposed by the algorithm of Holobar and Zazula, IEEE Trans. Sig. Proc. 2007; PSD of the cumulated firings almost identical to that of CWF, as few MUs were identified).

The purpose of this work is the estimation of the Glasgow outcome scale (GOS) from a single continuous electroencephalogram (c-EEG) routinely recorded to monitor comatose patients in the neurosurgical intensive care unit. c-EEG was recorded from 13 patients in the acute phase: five with GOS = 5, four with GOS = 3 and four with GOS = 1. Different indexes were extracted from epochs of c-EEG (classical: amplitude and spectral estimators; non classical: from recurrence quantification analysis-RQA-and approximate entropy). Descriptors of different indexes (temporal variation and mean, standard deviation, skewness of the distribution across epochs) were used to train support vector machines to identify the correct GOS. We found classifiers allowing correct classification of the patients. Spectral indexes allowed to get optimal performances in classifying GOS 1 and 3. Nonlinear indexes (especially determinism from RQA) were optimal for identifying GOS = 5. Thus, the integration of information from classical/linear and nonlinear c-EEG descriptors in a multi-index classifier is important for GOS estimation.

Surface electromyographic (EMG) signal modelling is important for signal interpretation, testing of processing algorithms, detection system design and didactic purposes. Various surface EMG signal models have been proposed in the literature. This study focuses on the proposal of a method for modelling surface EMG signals, using either analytical or numerical descriptions of the volume conductor for space-invariant systems, and on the development of advanced models of the volume conductor by numerical approaches, accurately describing the volume conductor geometry and the conductivity, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function has been derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fibre orientation. In some cases (e.g. multi-pinnate muscles), accurate description of the conductivity tensor can be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibres is presented and applied to investigate representatively a bi-pinnate muscle with rectilinear and curvilinear fibres. The study thus proposes an approach for surface EMG signal simulation in space invariant systems, as well as new models of the volume conductor using numerical methods.

Spatial filters are used for increasing selectivity in surface EMG signal detection. The study investigated the importance of the description of the volume conductor to the inference of conclusions on comparing filter selectivity from simulation analyses. A cylindrical multi-layer description of the volume conductor was used for the simulation analysis. Different anatomies were analysed with this model, and results on filter selectivity were compared. The longitudinal single (LSD), double (LDD) and normal double differential (Laplacian, NDD) filters were investigated. Largely different conclusions could be drawn when comparing filter selectivity resulting from simulations with different volume conductor models. A filter that performed best with a particular anatomy could be the poorest with another anatomy. With a bone-muscle model and superficial fibres, the ratio between peak-to-peak values of the propagating and non-propagating signal components was approximately 220% for LDD and LSD and lower than for NDD (approximately 290%). With a bone-muscle-fat-skin model, LSD performed significantly worse (150%) than both LDD and NDD, which showed similar performances (approximately 300%). Similarly, if the lateral distance of the recording was increased by 10 degrees, the signal amplitude was reduced to 2% with LSD and LDD and to 4% with NDD. With another anatomy, LSD and LDD reduced signal amplitude to 20-25%, and NDD reduced it to 4%. Similar considerations could be drawn for other selectivity indexes. Thus, modelling should be used carefully to infer conclusions on spatial selectivity and to indicate particular choices of spatial filters.