DSpace Collection:https://hdl.handle.net/10171/192782024-03-28T14:23:02Z2024-03-28T14:23:02ZEffect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomyhttps://hdl.handle.net/10171/678102023-12-15T11:27:51Z2023-01-01T00:00:00ZTitle: Effect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomy
Abstract: Background: Sugammadex has been associated with increases in the bispectral index (BIS). We evaluated the effects of sugammadex administration on quantitative electroencephalographic (EEG) and electromyographic (EMG) measures.Methods: We performed a prospective observational study of adult male patients undergoing robot-assisted radical prostatectomy. All patients received a sevoflurane-based general anaesthetic and a continuous infusion of rocuronium, which was reversed with 2 mg kg(-1) of sugammadex i.v. BIS, EEG, and EMG measures were captured with the BIS VistaTM monitor.Results: Twenty-five patients were included in this study. Compared with baseline, BIS increased at 4-6 min (13 coefficient: 3.63; 95% confidence interval [CI]: 2.22-5.04; P<0.001), spectral edge frequency 95 (SEF95) increased at 2-4 min (13 coefficient: 0.29; 95% CI: 0.05-0.52; P=0.016) and 4-6 min (13 coefficient: 0.71; 95% CI: 0.47-0.94; P<0.001), and EMG increased at 4-6 min (13 coefficient: 1.91; 95% CI: 1.00-2.81; P<0.001) after sugammadex administration. Compared with baseline, increased beta power was observed at 2-4 min (13 coefficient: 93; 95% CI: 1-185; P=0.046) and 4-6 min (13 co-efficient: 208; 95% CI: 116-300; P<0.001), and decreased delta power was observed at 4-6 min (13 coefficient: -526.72; 95% CI: -778 to -276; P<0.001) after sugammadex administration. Neither SEF95 nor frequency band data analysis adjusted for EMG showed substantial differences. None of the patients showed clinical signs of awakening.Conclusions: After neuromuscular block reversal with 2 mg kg(-1) sugammadex, BIS, SEF95, EMG, and beta power showed small but statistically significant increases over time, while delta power decreased.2023-01-01T00:00:00ZLa neurofisiología clínica: pasado, presente y futurohttps://hdl.handle.net/10171/652112023-02-06T06:04:50Z2009-01-01T00:00:00ZTitle: La neurofisiología clínica: pasado, presente y futuro
Abstract: La Neurofisiología Clínica es una especialidad médica cuyo objeto es el estudio del sistema nervioso y
muscular con fines diagnósticos, pronósticos y terapéuticos.
En este artículo se analiza el objetivo básico que
pretende esta disciplina, las técnicas que utiliza y su
reconocimiento como especialidad médica. Se hace
un pequeño recorrido por su definición y alcance de la
misma, cómo se estructura hoy día y las posibilidades
de futuro que ofrece.; Clinical Neurophysiology is a medical speciality
whose aim is the study of the nervous and muscular
system for diagnostic, prognostic and therapeutic purposes.
This article analyses the basic objective pursued
by this discipline, the techniques it employs and its
recognition as a medical speciality. The article briefly
reviews its definition and scope, how it is structured at
present and the future possibilities it offers.2009-01-01T00:00:00ZKinematic and kinetic patterns related to free-walking in Parkinson's diseasehttps://hdl.handle.net/10171/643942022-10-07T01:07:03Z2018-01-01T00:00:00ZTitle: Kinematic and kinetic patterns related to free-walking in Parkinson's disease
Abstract: The aim of this study is to compare the properties of free-walking at a natural pace
between mild Parkinson’s disease (PD) patients during the ON-clinical status and two control groups.
In-shoe pressure-sensitive insoles were used to quantify the temporal and force characteristics of a
5-min free-walking in 11 PD patients, in 16 young healthy controls, and in 12 age-matched healthy
controls. Inferential statistics analyses were performed on the kinematic and kinetic parameters
to compare groups’ performances, whereas feature selection analyses and automatic classification
were used to identify the signature of parkinsonian gait and to assess the performance of group
classification, respectively. Compared to healthy subjects, the PD patients’ gait pattern presented
significant differences in kinematic parameters associated with bilateral coordination but not in
kinetics. Specifically, patients showed an increased variability in double support time, greater gait
asymmetry and phase deviation, and also poorer phase coordination. Feature selection analyses
based on the ReliefF algorithm on the differential parameters in PD patients revealed an effect of
the clinical status, especially true in double support time variability and gait asymmetry. Automatic
classification of PD patients, young and senior subjects confirmed that kinematic predictors produced
a slightly better classification performance than kinetic predictors. Overall, classification accuracy of
groups with a linear discriminant model which included the whole set of features (i.e., demographics
and parameters extracted from the sensors) was 64.1%2018-01-01T00:00:00ZAn interactive framework for the detection of ictal and interictal activities: cross-species and stand-alone implementationhttps://hdl.handle.net/10171/637392023-12-15T11:27:51Z2022-01-01T00:00:00ZTitle: An interactive framework for the detection of ictal and interictal activities: cross-species and stand-alone implementation
Abstract: Background and objective: Despite advances on signal analysis and artificial intelligence, visual inspec-
tion is the gold standard in event detection on electroencephalographic recordings. This process requires
much time of clinical experts on both annotating and training new experts for this same task. In sce-
narios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures
and interictal events can occur on hours- or days-long recordings. Although other solutions have al-
ready been proposed, most of them are not integrated on clinical and basic science environments due
to their complexity and required specialization. Here we present a pipeline that arises from coordinated
efforts between life-science researchers, clinicians and data scientists to develop an interactive and it-
erative workflow to train machine-learning tools for the automatic detection of electroencephalographic
events in a variety of scenarios.
Methods: The approach consists on a series of subsequent steps covering data loading and configuration,
event annotation, model training/re-training and event detection. With slight modifications, the combi-
nation of these blocks can cope with a variety of scenarios. To illustrate the flexibility and robustness of
the approach, three datasets from clinical (patients of Dravet Syndrome) and basic research environments
(mice model of the same disease) were evaluated. From them, and in response to researchers’ daily needs,
four real world examples of interictal event detection and seizure classification tasks were selected and
processed.
Results: Results show that the current approach was of great aid for event annotation and model de-
velopment. It was capable of creating custom machine-learning solutions for each scenario with slight
adjustments on the analysis protocol, easily accessible to users without programming skills. Final anno-
tator similarity metrics reached values above 80% on all cases of use, reaching 92.3% on interictal event
detection on human recordings.
Conclusions: The presented framework is easily adaptable to multiple real world scenarios and the inter-
active and ease-to-use approach makes it manageable to clinical and basic researches without program-
ming skills. Nevertheless, it is conceived so data scientists can optimize it for specific scenarios, improving
the knowledge transfer between these fields.2022-01-01T00:00:00Z