8 research outputs found
Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks
Objective In the past few years there has been a growing interest
in studying brain functioning in natural, real-life situations. Mobile EEG allows
to study the brain in real unconstrained environments but it faces the intrinsic
challenge that it is impossible to disentangle observed changes in brain activity due
to increase in cognitive demands by the complex natural environment or due to the
physical involvement. In this work we aim to disentangle the influence of cognitive
demands and distractions that arise from such outdoor unconstrained recordings.
Approach We evaluate the ERP and single trial characteristics of a three-class
auditory oddball paradigm recorded in outdoor scenarioās while peddling on a
fixed bike or biking freely around. In addition we also carefully evaluate the trial
specific motion artifacts through independent gyro measurements and control for
muscle artifacts. Main results A decrease in P300 amplitude was observed in the
free biking condition as compared to the fixed bike conditions. Above chance P300
single-trial classification in highly dynamic real life environments while biking
outdoors was achieved. Certain significant artifact patterns were identified in
the free biking condition, but neither these nor the increase in movement (as
derived from continuous gyrometer measurements) can explain the differences
in classification accuracy and P300 waveform differences with full clarity. The
increased cognitive load in real-life scenarios is shown to play a major role in the
observed differences. Significance Our findings suggest that auditory oddball
results measured in natural real-life scenarios are influenced mainly by increased
cognitive load due to being in an unconstrained environment
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG
The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance.
In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet.
The feature-based classifier obtained an F1 score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.</p
The Suppression Curve as a quantitative approach for measuring brain maturation in preterm infants
Objectives: We apply the suppression curve (SC) as an automated approach to describe the maturational change in EEG discontinuity in preterm infants. This method allows to define normative values of interburst intervals (IBIs) at different postmenstrual ages (PMA). Methods: Ninety-two multichannel EEG recordings from 25 preterm infants (born ā¤32 weeks) with normal developmental outcome at 9 months, were first analysed using the Line Length method, an established method for burst detection. Subsequently, the SC was defined as the ālevel of EEG discontinuityā. The mean and the standard deviation of the SC, as well as the IBIs from each recording were calculated and correlated with PMA. Results: Over the course of development, there is a decrease in EEG discontinuity with a strong linear correlation between the mean SC and PMA till 34 weeks. From 30 weeks PMA, differences between discontinuous and continuous EEG become smaller, which is reflected by the decrease of the standard deviation of the SC. IBIs are found to have a significant correlation with PMA. Conclusions: Automated detection of individual maturational changes in EEG discontinuity is possible with the SC. These changes include more continuous tracing, less amplitude differences and shorter suppression periods, reflecting development of the vigilance states. Significance: The suppression curve facilitates automated assessment of EEG maturation. Clinical applicability is straight forward since values for IBIs according to PMA are generated automatically
Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor
Objective: After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance.
Methods: The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments.
Results: Four datasets including 71 neonates (1023 hours, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%.
Conclusion: This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors.
Significance: The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate
Machineālearningāderived sleepāwake staging from aroundātheāear electroencephalogram outperforms manual scoring and actigraphy
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a lowācost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flexāprinted cEEGrid electrodes placed around the ear, which can be implemented as a fully selfāapplicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (ārandom forestsā) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 Ā± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large interāindividual variation in sleep parameters. The results demonstrate that machineālearningābased scoring of aroundātheāear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machineālearningābased automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machineālearningābased scoring holds promise for largeāscale sleep studies
Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor
Objective: After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. Methods: The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. Results: Four datasets including 71 neonates (1023 hours, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. Conclusion: This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. Significance: The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate