Temporal Variability Analysis in sEMG Hand Grasp Recognition using Temporal Convolutional Networks

Hand movement recognition via surface electromyographic (sEMG) signal is a promising approach for the advance in Human-Computer Interaction. However, this field has to deal with two main issues: (1) the long-term reliability of sEMG-based control is limited by the variability affecting the sEMG signal (especially, variability over time); (2) the classification algorithms need to be suitable for implementation on embedded devices, which have strict constraints in terms of power budget and computational resources. Current solutions present a performance over-time drop that makes them unsuitable for reliable gesture controller design. In this paper, we address temporal variability of sEMG-based grasp recognition, proposing a new approach based on Temporal Convolutional Networks, a class of deep learning algorithms particularly suited for time series analysis and temporal pattern recognition. Our approach improves by 7.6% the best results achieved in the literature on the NinaPro DB6, a reference dataset for temporal variability analysis of sEMG. Moreover, when targeting the much more challenging inter-session accuracy objective, our method achieves an accuracy drop of just 4.8% between intra- and inter-session validation. This proves the suitability of our setup for a robust, reliable long-term implementation. Furthermore, we distill the network using deep network quantization and pruning techniques, demonstrating that our approach can use down to 120x lower memory footprint than the initial network and 4x lower memory footprint than a baseline Support Vector Machine, with an inter-session accuracy degradation of only 2.5%, proving that the solution is suitable for embedded resource-constrained implementations

Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful

Objective: Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not always be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion.Design: Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models.Methods: Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a simple 19-point score system to stratify patients according to their subtype diagnosis.Results: Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy: 82.9-95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS procedures.Conclusions: Our score could be integrated in clinical practice and guide surgical decision-making in patients with unilateral successful AVS and contralateral suppression

Machine learning applied to ambulatory blood pressure monitoring: a new tool to diagnose autonomic failure?

BACKGROUND: Autonomic failure (AF) complicates Parkinson’s disease (PD) in one-third of cases, resulting in complex blood pressure (BP) abnormalities. While autonomic testing represents the diagnostic gold standard for AF, accessibility to this examination remains limited to a few tertiary referral centers. OBJECTIVE: The present study sought to investigate the accuracy of a machine learning algorithm applied to 24-h ambulatory BP monitoring (ABPM) as a tool to facilitate the diagnosis of AF in patients with PD. METHODS: Consecutive PD patients naïve to vasoactive medications underwent 24 h-ABPM and autonomic testing. The diagnostic accuracy of a Linear Discriminant Analysis (LDA) model exploiting ABPM parameters was compared to autonomic testing (as per a modified version of the Composite Autonomic Symptom Score not including the sudomotor score) in the diagnosis of AF. RESULTS: The study population consisted of n = 80 PD patients (33% female) with a mean age of 64 ± 10 years old and disease duration of 6.2 ± 4 years. The prevalence of AF at the autonomic testing was 36%. The LDA model showed 91.3% accuracy (98.0% specificity, 79.3% sensitivity) in predicting AF, significantly higher than any of the ABPM variables considered individually (hypotensive episodes = 82%; reverse dipping = 79%; awakening hypotension = 74%). CONCLUSION: LDA model based on 24-h ABPM parameters can effectively predict AF, allowing greater accessibility to an accurate and easy to administer test for AF. Potential applications range from systematic AF screening to monitoring and treating blood pressure dysregulation caused by PD and other neurodegenerative disorders

Probing beta decay matrix elements through heavy ion charge exchange reactions

To access information on neutrinoless double beta decay (0νββ) nuclear matrix elements, it has been proposed by the NUMEN collaboration to exploit the analogies between double beta decay processes and double charge exchange (DCE) nuclear reactions, looking in particular at the conditions where the corresponding cross section can be factorized into nuclear reaction and nuclear structure terms. DCE reactions can be treated as a convolution of two correlated or uncorrelated single charge exchange (SCE) processes, resembling 0νββ and 2νββ, respectively. Thus it is important to model first SCE processes, to get a deeper insight into the possibility to factorize the corresponding cross section, so one can gain a better understanding of DCE cross section factorization. In this contribution, DCE reactions are discussed in terms of the convolution of two uncorrelated SCE processes, which should allow one to extract information on 2νββ nuclear matrix elements. These theoretical investigations are performed in close synergy with the experimental activity running at INFN-LNS within the NUMEN project

Impact of pairing on clustering and neutrino transport properties in low-density stellar matter

We analyze the effects of pairing correlations on the behavior of stellar matter, focusing on thermodynamical conditions close to the onset of the liquid–gas phase transition, which are characterized by quite large density fluctuations and where clustering phenomena occur. We concentrate on the neutrino transport properties and we show, within a thermodynamical treatment, that at moderate temperatures, where pairing effects are still active, the scattering of neutrinos in the nuclear medium is significantly affected by the matter superfluidity. The pairing correlations can indeed enhance neutrino trapping and reduce the energy flux carried out by neutrino emission. New hints about an important impact of pairing on the cooling mechanism, by neutrino emission, are so envisaged and therefore this study could be of relevant interest for the evolution of proto-neutron stars and in modelization of supernova explosions