Publications by authors named "R Aarts"

98 Publications

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study.

Clin EEG Neurosci 2021 Nov 1:15500594211050492. Epub 2021 Nov 1.

534522Eindhoven University of Technology, Eindhoven, the Netherlands.

Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.
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http://dx.doi.org/10.1177/15500594211050492DOI Listing
November 2021

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography.

NPJ Digit Med 2021 Sep 15;4(1):135. Epub 2021 Sep 15.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.

Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen's kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.
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http://dx.doi.org/10.1038/s41746-021-00510-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443610PMC
September 2021

Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis.

J Appl Physiol (1985) 2021 04 4;130(4):1015-1024. Epub 2021 Feb 4.

Department of Neonatology, Máxima Medical Centre, Veldhoven, The Netherlands.

Cardiorespiratory interaction (CRI) has been intensively studied in adult sleep, yet not in preterm infants, in particular across different sleep states including wake (W), active sleep (AS), and quiet sleep (QS). The aim of this study was to quantify the interaction between cardiac and respiratory activities in different sleep states of preterm infants. The postmenstrual age (PMA) of preterm infants was also taken into consideration. The CRI during sleep was analyzed using a visibility graph (VG) method, enabling the nonlinear analysis of CRI in a complex network. For each sleep state, parameters quantifying various aspects of the CRI characteristics from constructed VG network including mean degree () and its variability (), clustering coefficient (CC) and its variability (CC), assortativity coefficient (AC), and complexity () were extracted from the CRI networks. The interaction effect of sleep state and PMA was found to be statistically significant on all CRI parameters except for AC and . The main effect between sleep state and CRI parameters was statistically significant except for CC, and that between PMA and CRI parameters was statistically significant except for . In conclusion, the CRI of preterm infants is associated with sleep states and PMA in general. For preterm infants with a larger PMA, CRI has a more clustered pattern during different sleep states, where QS shows a more regular, stratified, and stronger CRI than other states. In the future, these parameters can be potentially used to separate sleep states in preterm infants. The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.
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http://dx.doi.org/10.1152/japplphysiol.00333.2020DOI Listing
April 2021

Video-based actigraphy is an effective contact-free method of assessing sleep in preterm infants.

Acta Paediatr 2021 06 12;110(6):1815-1816. Epub 2021 Jan 12.

Department of Neonatology, Máxima Medical Center, Veldhoven, The Netherlands.

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http://dx.doi.org/10.1111/apa.15740DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247861PMC
June 2021

Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:847-850

Parkinson's disease (PD) patients with freezing of gait (FOG) can suddenly lose their forward moving ability leading to unexpected falls. To overcome FOG and avoid the falls, a real-time accurate FOG detection or prediction system is desirable to trigger on-demand cues. In this study, we designed and implemented an in-place movement experiment for PD patients to provoke FOG and meanwhile acquired multimodal physiological signals, such as electroencephalography (EEG) and accelerometer signals. A multimodal model using brain activity from EEG and motion data from accelerometers was developed to improve FOG detection performance. In the detection of over 700 FOG episodes observed in the experiments, the multimodal model achieved 0.211 measured by Matthews Correlation Coefficient (MCC) compared with the single-modal models (0.127 or 0.139).Clinical Relevance- This is the first study to use multimodal: EEG and accelerometer signal analysis in FOG detection, and an improvement was achieved.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175288DOI Listing
July 2020
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