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1.
J Sleep Res ; : e12868, 2019 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-31131530

RESUMO

Several automated methods for scoring periodic limb movements during sleep (PLMS) and rapid eye movement (REM) sleep without atonia (RSWA) have been proposed, but most of them were developed and validated on data recorded in the same clinic, thus they may be biased. This work aims to validate our data-driven algorithm for muscular activity detection during sleep, originally developed based on data recorded and manually scored at the Danish Center for Sleep Medicine. The validation was carried out on a cohort of 240 participants, including de novo Parkinson's disease (PD) patients and neurologically healthy controls, whose sleep data were recorded and manually evaluated at Paracelsus-Elena Klinik, Kassel, Germany. In the German cohort, the algorithm showed generally good agreement between manual and automated PLMS indices, and identified with 88.75% accuracy participants with PLMS index above 15 PLMS per hour of sleep, and with 84.17% accuracy patients suffering from REM sleep behaviour disorder (RBD) showing RSWA. By comparing the algorithm performances in the Danish and German cohorts, we hypothesized that inter-clinical differences may exist in the way limb movements are manually scored and how healthy controls are defined. Finally, the algorithm performed worse in PD patients, probably as a result of increased artefacts caused by abnormal motor events related to neurodegeneration. Our algorithm can identify, with reasonable performance, participants with RBD and increased PLMS index from data recorded in different centres, and its application may reveal inter clinical differences, which can be overcome in the future by applying automated methods.

2.
Physiol Meas ; 40(2): 025008, 2019 Feb 26.
Artigo em Inglês | MEDLINE | ID: mdl-30736016

RESUMO

OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P es) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative P es. APPROACH: One thousand one hundred and nineteen patients from the Stanford Sleep Clinic with PSGs containing P es served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory neural network was implemented to achieve a context-based mapping between the non-invasive features and the P es values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive P es. MAIN RESULTS: The median difference between the measured and predicted P es was 0.61 cmH2O with an interquartile range (IQR) of 2.99 cmH2O and 5th and 95th percentiles of -5.85 cmH2O and 5.47 cmH2O, respectively. The model performed well when compared to actual esophageal pressure signal (ρ median = 0.581, p  = 0.01; IQR = 0.298; ρ 5% = 0.106; ρ 95% = 0.843). SIGNIFICANCE: A significant difference in predicted P es was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for P es, improving characterization of SDB events as obstructive or not.

3.
Eur J Neurosci ; 50(2): 1948-1971, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30762918

RESUMO

Quantitative electroencephalography from freely moving rats is commonly used as a translational tool for predicting drug-effects in humans. We hypothesized that drug-effects may be expressed differently depending on whether the rat is in active locomotion or sitting still during recording sessions, and proposed automatic state-detection as a viable tool for estimating drug-effects free of hypo-/hyperlocomotion-induced effects. We aimed at developing a fully automatic and validated method for detecting two behavioural states: active and inactive, in one-second intervals and to use the method for evaluating ketamine, DOI, d-cycloserine, d-amphetamine, and diazepam effects specifically within each state. The developed state-detector attained high precision with more than 90% of the detected time correctly classified, and multiple differences between the two detected states were discovered. Ketamine-induced delta activity was found specifically related to locomotion. Ketamine and DOI suppressed theta and beta oscillations exclusively during inactivity. Characteristic gamma and high-frequency oscillations (HFO) enhancements of the NMDAR and 5HT2A modulators, speculated associated with locomotion, were profound and often largest during the inactive state. State-specific analyses, theoretically eliminating biases from altered occurrence of locomotion, revealed only few effects of d-amphetamine and diazepam. Overall, drug-effects were most abundant in the inactive state. In conclusion, this new validated and automatic locomotion state-detection method enables fast and reliable state-specific analysis facilitating discovery of state-dependent drug-effects and control for altered occurrence of locomotion. This may ultimately lead to better cross-species translation of electrophysiological effects of pharmacological modulations.

4.
Brain Behav ; 9(1): e01197, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30592179

RESUMO

INTRODUCTION: Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations. MATERIALS AND METHODS: This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score. RESULTS: We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities. CONCLUSIONS: These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Cognição/fisiologia , Disfunção Cognitiva/diagnóstico por imagem , Neuroimagem/métodos , Idoso , Idoso de 80 Anos ou mais , Doença de Alzheimer/patologia , Doença de Alzheimer/fisiopatologia , Biomarcadores , Disfunção Cognitiva/patologia , Disfunção Cognitiva/fisiopatologia , Estudos Transversais , Doença , Eletroencefalografia , Feminino , Humanos , Aprendizado de Máquina , Imagem por Ressonância Magnética , Masculino
5.
Nat Commun ; 9(1): 5229, 2018 Dec 06.
Artigo em Inglês | MEDLINE | ID: mdl-30523329

RESUMO

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.

6.
J Sleep Res ; : e12793, 2018 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-30417544

RESUMO

Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. To test this hypothesis, we assessed sleep architecture and EEG of 167 men born in the Copenhagen Metropolitan Area in 1953, who, based on individual cognitive testing from early (~18 years) to late adulthood (~58 years), were divided into 85 subjects with negative and 82 with positive cognitive change over their adult life. Participants underwent standard polysomnography, including manual sleep scoring at age ~58 years. Features of sleep macrostructure were combined with a number of EEG features to distinguish between the two groups. EEG rhythmicity was assessed by spectral power analysis in frontal, central and occipital sites. Functional connectivity was measured by inter-hemispheric EEG coherence. Group differences were assessed by analysis of covariance (p < 0.05), including education and severity of depression as potential covariates. Subjects with cognitive decline exhibited lower sleep efficiency, reduced inter-hemispheric connectivity during rapid eye movement (REM) sleep, and slower EEG rhythms during stage 2 non-REM sleep. Individually, none of these tendencies remained significant after multiple test correction; however, by combining them in a machine learning approach, the groups were separated with 72% accuracy (75% sensitivity, 67% specificity). Ongoing medical screenings are required to confirm the potential of sleep efficiency and sleep EEG patterns as signs of individual cognitive progress.

7.
J Neurosci Methods ; 312: 53-64, 2018 Nov 20.
Artigo em Inglês | MEDLINE | ID: mdl-30468824

RESUMO

BACKGROUND: Documentation of REM sleep without atonia is fundamental for REM sleep behavior disorder (RBD) diagnosis. The automated REM atonia index (RAI), Frandsen index (FRI) and Kempfner index (KEI) were proposed for this, but achieved moderate performances. NEW METHOD: Using sleep data from 27 healthy controls (C), 29 RBD patients and 36 patients with periodic limb movement disorder (PLMD), we developed and validated a new automated data-driven method for identifying movements in chin and tibialis electromyographic (EMG) signals. A probabilistic model of atonia from REM sleep of controls was defined and movements identified as EMG areas having low likelihood of being atonia. The percentages of movements and the median inter-movement distance during REM and non-REM (NREM) sleep were used for distinguishing C, RBD and PLMD by combining three optimized classifiers in a 5-fold cross-validation scheme. RESULTS: The proposed method achieved average overall validation accuracies of 70.8% and 61.9% when REM and NREM, and only REM features were used, respectively. After removing apnea and arousal-related movements, they were 64.2% and 59.8%, respectively. COMPARISON WITH EXISTING METHOD(S): The proposed method outperformed RAI, FRI and KEI in identifying RBD patients and in particular achieved higher accuracy and specificity for classifying RBD. CONCLUSIONS: The results show that i) the proposed method has higher performances than the previous ones in distinguishing C, RBD and PLMD patients, ii) removal of apnea and arousal-related movements is not required, and iii) RBD patients can be better identified when both REM and NREM muscular activities are considered.

8.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 1-4, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440296

RESUMO

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.

9.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 163-166, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440364

RESUMO

Periodic limb movement disorder (PLMD) is a sleep disorder characterized by repetitive limb movements (LM) during night. The gold standard for LM detection consists of visual analysis of tibialis left (TIBL) and right (TIBR) electromyographic (EMG) signals. Such analysis is subjective and time-consuming. We here propose a semi-supervised and data-driven approach for LM detection during sleep that was trained and tested on 27 healthy controls (C) and 36 PLMD patients. After preprocessing of the EMG signals, discrete wavelet transform (Daubechies 4 mother wavelet and down to 4th decomposition level) was applied. EMG was reconstructed for each set of detail coefficients, thus obtaining four signals (DI-D4). The pre-processed EMG and DI-D4 signals were divided in 3-s mini-epochs of which traditional EMG features were calculated. Based on the assumption of lack of movements in healthy controls during rapid eye movement (REM) sleep, we used the features during REM of a subgroup of C to build a non-parametric probabilistic model defining the resting EMG distribution. This model was then used to classify the remaining mini-epochs as either resting EMG or LM. The percentages of 3-s mini-epochs with LMs were calculated for each subject and used to distinguish the remaining C and PLMD with a support vector machine and 5-fold cross validation scheme. Results showed that C can be distinguished by PLMD with accuracy higher than 82% in the preprocessed EMG and DI-D3 signals.

10.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 457-460, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440433

RESUMO

Obstructive Sleep Apnea (OSA) is a common sleep disorder affecting $>10\%$ of the middle-aged population. The gold standard diagnostic procedure is the Polysomnography (PSG), which is both costly and time consuming. A simple and non-expensive screening therefore would be of great value. This study presents a novel at-home screening method for OSA using a smartphone, a microphone and a modified armband, to measure continuous biological signals during a whole night sleep. A signal-processing algorithm was used to classify the subjects, into classes according to severity of the disorder. The system was validated by conducting a routine sleep study parallel to the data acquisition on a total of 23 subjects. Both binary and 4-class classification problems were tested. The binary classifications showed the best results with sensitiv- ities between 92.3 % and 100 %, and accuracies between 78.3 % and 91.3 %. The 4-class classification was not as successful with a sensitivity of 75 %, and accuracies of 56.5 % and 60 %. We conclude that mobile smartphone technology has a potential for OSA ambulatory screening.

11.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 4920-4923, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441446

RESUMO

Rapid eye movement (REM) sleep behavior disorder is considered the prodromal stage of alpha-synucleinopathies. Its diagnosis requires careful detection of REM sleep and the gold standard manual sleep staging is inconsistent and expensive. This work proposes a new automatic sleep staging model to add robust automation to such applications, using only electroencephalography (EEG) and electrooculography (EOG) recordings. The publicly available ISRUC-Sleep database was used to optimize the design of the proposed model. The model was trained and tested on subgroup-I consisting of 100 subjects with evidence of having different sleep disorders and the polysomnographic data were manually scored by two individual experts. We divided the EOG and EEG recordings in overlapping moving 33-s epochs with step of 3s and for each of them we computed several time and frequency-domain features. The features were used to train a random forest classifier that was able to label each 33-s epoch with the probabilities of being wakefulness, REM and non-REM. The mean of the probability values of ten 33-s epochs were calculated, and the sleep stage with the highest probability was chosen to classify a 30-s epoch and matched with the manual staged hypnogram. The performance of the model was tested using 20-fold cross validation scheme. When the epochs where the scorers agreed were used, the classification achieved an overall accuracy of 92.6% and a Cohen's kappa of 0.856. Future validation on RBD patients is needed, but these performances are promising as first step of development of an automated diagnosis of RBD.

12.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 6010-6013, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441706

RESUMO

Electroencephalogram (EEG) is a common tool in sleep medicine, but it is often compromised by non-neural artifacts. Excluding visually identified artifacts is time-consuming and removes relevant EEG information. Blind source separation (BSS) techniques, on the other hand, are capable of separating "brain" from "artifact source components". Existing algorithms for automated component labeling require either a priori morphological information or adaptation to individual recordings. We present a method for the automated identification of artifact components based on their autocorrelation and spectral properties. It requires no tuning for individual recordings. The method was tested on 100 one-minute EEG segments during rapid eye movement sleep. EEG source components were estimated by second order blind source identification and, as reference, manually labeled as "brain" or "artifact component". The algorithm identified electro-cardiogram components by autocorrelation peaks between 0.5-1.5 seconds and -oculogram components by linear discriminant analysis of spectral band-power. Using 5-fold cross-validation, we observed 97% accuracy (95% sensitivity, 98% specificity), as well as minimized correlation of artifacts and the EEG. The approach has demonstrated its potential as promising tool for a broad range of sleep medical applications.

13.
Sleep ; 41(10)2018 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-30011023

RESUMO

Rapid eye movement (REM) sleep without atonia detection is a prerequisite for diagnosis of REM sleep behavior disorder (RBD). As the visual gold standard method is time-consuming and subjective, several automated methods have been proposed. This study aims to compare their performances: The REM atonia index (RAI), the supra-threshold-REM-activity metric, the Frandsen index, the short/long muscle activity indices, and the Kempfner index algorithms were applied to 27 healthy control participants (C), 25 patients with Parkinson's disease (PD) without RBD (PD-RBD), 29 patients with PD and RBD (PD + RBD), 29 idiopathic patients with RBD, and 36 patients with periodic limb movement disorder (PLMD). The indices were calculated in various configurations: (1) considering all muscle activities; (2) excluding the ones related to arousals; (3) excluding the ones during apnea events; (4) excluding the ones before and after apnea events; (5) combining configurations 2 and 3; and (6) combining configurations 2 and 4. For each of these configurations, the discrimination capability of the indices was tested for the following comparisons: (1) (C, PD-RBD, PLMD) vs (PD + RBD, RBD); (2) C vs RBD; (3) PLMD vs RBD; (4) C vs PD-RBD; (5) C vs PLMD; (6) PD-RBD vs PD + RBD; and (7) C vs PLMD vs RBD. Results showed varying methods' performances across the different configurations and comparisons, making it impossible to identify the optimal method and suggesting the need of further improvements. Nevertheless, RAI seems the most sensible one for RBD detection. Moreover, apnea and arousal-related movements seem not to influence the algorithms' performances in patients' classification.

14.
IEEE J Transl Eng Health Med ; 5: 2000108, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29018634

RESUMO

Absence seizures are associated with generalized 2.5-5 Hz spike-wave discharges in the electroencephalogram (EEG). Rarely are patients, parents, or physicians aware of the duration or incidence of seizures. Six patients were monitored with a portable EEG-device over four times 24 h to evaluate how easily outpatients are monitored and how well an automatic seizure detection algorithm can identify the absences. Based on patient-specific modeling, we achieved a sensitivity of 98.4% with only 0.23 false detections per hour. This yields a clinically satisfying performance with a positive predictive value of 87.1%. Portable EEG-recorders identifying paroxystic events in epilepsy outpatients are a promising tool for patients and physicians dealing with absence epilepsy. Albeit the small size of the EEG-device, some children still complained about the obtrusive nature of the device. We aim at developing less obtrusive though still very efficient devices, e.g., hidden in the ear canal or below the skin.

15.
Conf Proc IEEE Eng Med Biol Soc ; 2017: 2578-2581, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-29060426

RESUMO

We designed a queue-based model, and investigated which parameters are of importance when predicting stroke outcome. Medical record forms have been collected for 57 ischemic stroke patients, including medical history and vital sign measurement along with neurological scores for the first twenty-four hours of admission. The importance of each parameter is identified using multiple regression combined with a circular queue to iteratively fit outcome. Out of 39 parameters, the model isolated 14 which combined could estimate outcome with a root mean square error of 1.69 on the Scandinavian Stroke Scale, where outcome for patients were 36.75 ± 10.99. The queue-based model integrating multiple linear regression shows promising results for automatic selection of significant medically relevant parameters.


Assuntos
Acidente Vascular Cerebral , Isquemia Encefálica , Humanos , Modelos Lineares , Análise Multivariada , Resultado do Tratamento
16.
Conf Proc IEEE Eng Med Biol Soc ; 2017: 3793-3796, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-29060724

RESUMO

Electroencephalogram (EEG) signal quality is often compromised by artifacts that corrupt quantitative EEG measurements used in clinical applications and EEG-related studies. Techniques such as filtering, regression analysis and blind source separation are often used to remove these artifacts. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic artifact set. Five EEG features were used to quantify temporal and spatial signal variations. Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.


Assuntos
Eletroencefalografia , Algoritmos , Artefatos , Processamento de Sinais Assistido por Computador
17.
Brain Res ; 1664: 37-47, 2017 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-28366617

RESUMO

Studies of the antidepressant vortioxetine have demonstrated beneficial effects on cognitive dysfunction associated with depression. To elucidate how vortioxetine modulates neuronal activity during cognitive processing we investigated the effects of vortioxetine (3 and 10mg/kg) in rats performing an auditory oddball (deviant target) task. We investigated neuronal activity in target vs non-target tone responses in vehicle-treated animals using electroencephalographic (EEG) recordings. Furthermore, we characterized task performance and EEG changes in target tone responses of vortioxetine vs controls. Quantification of event-related potentials (ERPs) was supplemented by analyses of spectral power and inter-trial phase-locking. The assessed brain regions included prelimbic cortex, the hippocampus, and thalamus. As compared to correct rejection of non-target tones, correct target tone responses elicited increased EEG power in all regions. Additionally, neuronal synchronization was increased in vehicle-treated rats during both early and late ERP responses to target tones. This indicates a significant consistency of local phases across trials during high attentional load. During early sensory processing, vortioxetine increased both thalamic and frontal synchronized gamma band activity and EEG power in all brain regions measured. Finally, vortioxetine increased the amplitude of late hippocampal P3-like ERPs, the rodent correlate of the human P300 ERP. These findings suggest differential effects of vortioxetine during early sensory registration and late endogenous processing of auditory discrimination. Strengthened P3-like ERP response may relate to the pro-cognitive profile of vortioxetine in rodents. Further investigations are warranted to explore the mechanism by which vortioxetine increases network synchronization during attentive and cognitive processing.


Assuntos
Antidepressivos/administração & dosagem , Atenção/efeitos dos fármacos , Encéfalo/efeitos dos fármacos , Encéfalo/fisiologia , Cognição/efeitos dos fármacos , Potenciais Evocados Auditivos/efeitos dos fármacos , Piperazinas/administração & dosagem , Sulfetos/administração & dosagem , Estimulação Acústica , Animais , Atenção/fisiologia , Percepção Auditiva/efeitos dos fármacos , Percepção Auditiva/fisiologia , Córtex Cerebral/efeitos dos fármacos , Córtex Cerebral/fisiologia , Cognição/fisiologia , Eletroencefalografia , Hipocampo/efeitos dos fármacos , Hipocampo/fisiologia , Masculino , Ratos Sprague-Dawley , Tálamo/efeitos dos fármacos , Tálamo/fisiologia , Vortioxetina
18.
J Neural Eng ; 14(2): 026012, 2017 04.
Artigo em Inglês | MEDLINE | ID: mdl-28177924

RESUMO

OBJECTIVE: Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats. APPROACH: An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task. MAIN RESULTS: While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones. SIGNIFICANCE: The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.


Assuntos
Algoritmos , Percepção Auditiva/fisiologia , Eletroencefalografia/métodos , Potencial Evocado P300/fisiologia , Potenciais Evocados Auditivos/fisiologia , Análise de Ondaletas , Estimulação Acústica/métodos , Animais , Masculino , Ratos , Ratos Sprague-Dawley , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
19.
Front Neurosci ; 10: 352, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27536212

RESUMO

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention.

20.
Conf Proc IEEE Eng Med Biol Soc ; 2016: 2529-2532, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-28268838

RESUMO

This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.


Assuntos
Monitorização Fisiológica/instrumentação , Monitorização Fisiológica/métodos , Processamento de Linguagem Natural , Sinais Vitais , Idoso , Pressão Sanguínea , Cuidados Críticos/métodos , Dinamarca , Parada Cardíaca/diagnóstico , Hospitalização , Hospitais , Humanos , Unidades de Terapia Intensiva , Informática Médica/métodos , Pessoa de Meia-Idade , Modelos Estatísticos , Oxigênio/química , Admissão do Paciente , Insuficiência Respiratória/diagnóstico , Sepse/diagnóstico
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