Publications by authors named "Aarts R"

Article Synopsis
  • * The case of a patient with clozapine-resistant schizophrenia highlights the differing views of caregivers and physician-researchers regarding the patient's decision-making capacity and the complexities surrounding it.
  • * To improve research quality and inclusivity, it is crucial to enhance assessment tools, encourage inclusive participation, and support patient representation in decision-making.
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Article Synopsis
  • - The study focused on classifying sleep states (quiet sleep, active sleep, wake) in preterm infants by analyzing cardiorespiratory signals from patient monitors in a neonatal intensive care unit.
  • - Researchers recorded data from eight preterm infants and used advanced algorithms to extract features from electrocardiography and respiratory signals, finding that incorporating motion data helped improve classification accuracy.
  • - Results showed a majority of the time was spent in active sleep, and the inclusion of cardiorespiratory interactions significantly enhanced the automated detection of sleep states, making the classification more reliable.
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Regenerative life support systems (RLSS) will play a vital role in achieving self-sufficiency during long-distance space travel. Urine conversion into a liquid nitrate-based fertilizer is a key process in most RLSS. This study describes the effects of simulated microgravity (SMG) on Comamonas testosteroni, Nitrosomonas europaea, Nitrobacter winogradskyi and a tripartite culture of the three, in the context of nitrogen recovery for the Micro-Ecological Life Support System Alternative (MELiSSA).

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The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants.

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Introduction: Acute mechanical thrombectomy (MT) is the preferred treatment for large vessel occlusion-related stroke. Histopathological research on the obtained occlusive embolic thrombus may provide information regarding the aetiology and pathology of the lesion to predict prognosis and propose possible future acute ischaemic stroke therapy.

Methods: A total of 75 consecutive patients who presented to the Amphia Hospital with acute large vessel occlusion-related stroke and underwent MT were included in the study.

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Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential.

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The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming.

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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.

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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.

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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.

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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.

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Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost.

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Objective: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges.

Approach: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges.

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Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders.

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Frequent pain and discomfort in premature infants can lead to long-term adverse neurodevelopmental outcomes. Video-based monitoring is considered to be a promising contactless method for identification of discomfort moments. In this study, we propose a video-based method for automated detection of infant discomfort.

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Objective: Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL).

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Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.

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Introduction: If asylum applicants need to prove that they have been persecuted in their home country, expert judgment of the psychological and physical consequences of torture may support the judicial process. Expert medico-legal reports can be used to assess whether the medical complaints of the asylum seeker are consistent with their asylum account. It is unclear which factors influence medical expert judgement about the consistency between an asylum seeker's symptoms and story, and to what extent expert medico-legal reports are associated with judicial outcomes.

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: To date, most studies on the mental health of refugees in Europe have focused on the prevalence and treatment of psychopathology. Little is known about those who illegally reside in the host country, referred to, in the Netherlands, as undocumented asylum seekers. There are indications that mental health and psychosocial problems are more prevalent in this group than among refugees in general, with unsatisfactory treatment outcomes.

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Objective: To date, mainly obtrusive methods (e.g. adhesive electrodes in electroencephalography or electrocardiography) have been necessary to determine the preterm infant sleep states.

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Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24 h blood pressure trends using a wrist-worn photoplethysmography (PPG) sensor and a deep neural network in free-living individuals, comparing the deep neural network to traditional machine learning and non-machine learning baselines.

Approach: A wrist-worn PPG sensor was worn by 106 healthy individuals for 226 d during which 5111 reference values for blood pressure (BP) were obtained with a 24 h ambulatory BP monitor and matched with the PPG sensor data. Features based on heart rate variability and pulse morphology were extracted from the PPG waveforms.

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Autonomic cardiac activity during sleep has been widely studied. Research has mostly focused on cardiac activity between different sleep stages and wakefulness as well as between normal and pathological sleep. This work investigates autonomic activity changes during sleep onset in healthy subjects with long and short sleep onset latency (SOL).

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Background Long-term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation ( AF ). This study examined the accuracy of a novel approach for AF detection using photo-plethysmography signals measured from a wrist-based wearable device. Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion ( ECV ) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings ( HOL ).

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