Data acquisition systems with intelligent trigger capability

Two data acquisition systems, based on two solutions for improving the performance, are here presented. The first one, fully analog, is able to generate a voltage impulse at the occurrence of a transient phenomenon on the stationary waveform being monitored. In the second system the acquisition process is regulated by absolute value of the derivative of the signal under analysis. This system is realized with Field Programmable Gate Array technology. All theoretical relations underlying the proposed solutions are first discussed. Their most relevant hardware and software features are then described. A suitable measurement apparatus is set up for assessing the performance of both solutions, and the obtained results are finally given. (c) 2005 Elsevier Ltd. All rights reserved

Prevalence Of Functional Gastrointestinal Disorders According To Rome III Criteria In Italian Morbidly Obese Patients

The relationship between GI symptoms and obesity has yet to be completely clarified. Aim. To determine in a morbidly obese southern Italy adult population the prevalence of Functional Gastrointestinal Disorders (FGID) and its association with the presence of a Binge Eating (BE) behavior pattern. Methods. Consecutive obese patients eligible for bariatric surgery and 100 Healthy Controls (HC) were recruited. All participants were questioned and scored for the presence of FGID according to Rome III criteria and for the presence or the frequency-intensity of a number of upper and lower GI symptoms. BE behavior pattern was assessed. Results. One-hundred obese patients met the inclusion criteria. The prevalence of FGID was similar between obese patients and HC. There was a significant association between obese patients with BE behavior and postprandial distress syndrome (P = 0.04). Moreover, a significantly higher frequency-intensity score for epigastric fullness (1.23 ± 0.45 versus 0.35 ± 0.13, P = 0.01) was found in obese patients with BE behavior compared to obese patients without. Conclusions. Obese patients with a BE behavior pattern showed a significantly higher prevalence of postprandial distress syndrome. A greater knowledge of the GI symptoms associated with obesity along with the pathophysiological mechanisms underlying will be important in the clinical management of these patients

A Wearable Brain-Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain-computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values

Sarcopenia: What a Surgeon Should Know

Sarcopenia is an increasingly frequent syndrome characterized by generalized and progressive loss of muscle mass, reduction in muscle strength, and resultant functional impairment. This condition is associated with increased risk of falls and fractures, disability, and increased risk of death. When a sarcopenic patient undergoes major surgery, it has a higher risk of complications and postoperative mortality because of less resistance to surgical stress. It is not easy to recognize a sarcopenic patient preoperatively, but this is essential to evaluate the correct risk to benefit ratio. The role of sarcopenia in surgical patients has been studied for both oncological and non-oncological surgery. For correct surgical planning, data about sarcopenia are essential to design a correct tailored treatment

Metrological performance of a single-channel brain-computer interface based on motor imagery

In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset '2a' of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG

a new digital signal processing method for spectrum interference monitoring

Frequency spectrum is a limited shared resource, nowadays interested by an ever growing number of different applications. Generally, the companies providing such services pay to the governments the right of using a limited portion of the spectrum, consequently they would be assured that the licensed radio spectrum resource is not interested by significant external interferences. At the same time, they have to guarantee that their devices make an efficient use of the spectrum and meet the electromagnetic compatibility regulations. Therefore the competent authorities are called to control the access to the spectrum adopting suitable management and monitoring policies, as well as the manufacturers have to periodically verify the correct working of their apparatuses. Several measurement solutions are present on the market. They generally refer to real-time spectrum analyzers and measurement receivers. Both of them are characterized by good metrological accuracies but show costs, dimensions and weights that make no possible a use "on the field". The paper presents a first step in realizing a digital signal processing based measurement instrument able to suitably accomplish for the above mentioned needs. In particular the attention has been given to the DSP based measurement section of the instrument. To these aims an innovative measurement method for spectrum monitoring and management is proposed in this paper. It performs an efficient sequential analysis based on a sample by sample digital processing. Three main issues are in particular pursued: (i) measurement performance comparable to that exhibited by other methods proposed in literature; (ii) fast measurement time, (iii) easy implementation on cost-effective measurement hardware

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems