Temperature Sensing Properties of Biocompatible Yb/Er-Doped GdF3 and YF3 Mesocrystals.

Y0.8-xGdxF3:Yb/Er mesocrystals with a biocompatible surface and diverse morphological characteristics were successfully synthesized using chitosan-assisted solvothermal processing. Their structural properties, studied using X-ray powder diffraction, Fourier transform infrared spectroscopy, scanning and transmission electron microscopy and energy dispersive X-ray analysis, were further correlated with the up-conversion emission (λexc = 976 nm) recorded in function of temperature. Based on the change in the visible green emissions originating from the thermally coupled 2H11/2 and 4S3/2 levels of Er3+, the corresponding LIR was acquired in the physiologically relevant range of temperatures (25-50 °C). The detected absolute sensitivity of about 0.0042 °C-1, along with the low cytotoxicity toward both normal human lung fibroblasts (MRC-5) and cancerous lung epithelial (A549) cells, indicate a potential for use in temperature sensing in biomedicine. Additionally, their enhanced internalization in cells, without suppression of cell viability, enabled in vitro labeling of cancer and healthy cells upon 976 nm laser irradiation.

Feature selection methods for accelerometry-based seizure detection in children.

We investigate the application of feature selection methods and their influence on distinguishing nocturnal motor seizures in epileptic children from normal nocturnal movements using accelerometry signals. We studied two feature selection methods applied one after the other to reduce the complexity and computation costs of least-squares support vector machine (LS-SVM) models. Simultaneous feature selection analyses were performed for each seizure type individually and jointly. Starting from 140 features, a filter method based on mutual information was applied to remove irrelevant and redundant features. The obtained subset was further reduced through a wrapper feature selection strategy using an LS-SVM classifier with both forward search and backward elimination. The discriminative power of each feature subset was evaluated on the test data in terms of the area under the receiver operating characteristic curve, sensitivity, and false detection rate per hour. We showed that, by using only a filter method for feature selection, it was possible to obtain classification results of comparable or slightly reduced performance with respect to the complete feature set. The attained results could facilitate further development of accelerometry-based seizure detection and alarm systems.

Biodegradation of cotton and cotton/polyester fabrics impregnated with Ag/TiO2 nanoparticles in soil.

This study discusses the biodegradation behavior of cotton and cotton/PET fabrics impregnated with Ag/TiO2 nanoparticles in soil. Biodegradation behavior was evaluated by standard test method ASTM 5988-03 based on determination of percentage conversions of carbon content to CO2 as well as by soil burial test and enzymatic hydrolysis with cellulase where the extent of biodegradation was estimated by the calculation of fabric weight loss. The morphological and chemical changes of fibers during biodegradation process were analyzed by SEM and FTIR spectroscopy, respectively. The results obtained by all applied methods suggested that Ag/TiO2 nanoparticles hindered the biodegradation of investigated cotton and cotton/PET fabrics. Soil burial test indicated faster biodegradation of the impregnated blend compared to impregnated cotton fabric which is attributed to smaller amount of fabricated Ag nanoparticles on the blend proved by AAS measurement. Similar trend was established by enzymatic hydrolysis of cotton fibers. Severe damage of cotton fibers in both fabrics due to biodegradation process was confirmed by SEM. However, the cotton fiber damage occurred to a lesser extent in the samples that were impregnated with Ag/TiO2 nanoparticles. PET fibers remained intact which was also indicated by FTIR analysis.

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update.

PURPOSE: Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. METHOD: We performed a thorough literature review and had contact with manufacturers of commercially available devices. RESULTS: This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. CONCLUSION: Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences.

Instrumented BASFI (iBASFI) Shows Promising Reliability and Validity in the Assessment of Activity Limitations in Axial Spondyloarthritis.

OBJECTIVE: The Bath Ankylosing Spondylitis Functional Index (BASFI) is the most popular method to assess activity capacity in axial spondyloarthritis (axSpA), to our knowledge. It is endorsed by the Assessment of Spondyloarthritis international Society. But it may have recall bias or aberrant self-judgments in individual patients. Therefore, we aimed to (1) develop the instrumented BASFI (iBASFI) by adding a body-worn accelerometer with automated algorithms to performance-based measurements (PBM), (2) study the iBASFI's core psychometric properties, and (3) reduce the number of iBASFI items. METHODS: Twenty-eight patients with axSpA wore a 2-axial accelerometer while completing 12 PBM derived from the BASFI. A chronometer and both manual and "automated algorithm-based" acceleration segmentation identified movement time. Test-retest trials and methods (algorithm vs manual segmentation/chronometer/BASFI) were compared with ICC, standard error of measurement [percentage of movement time (SEM%)], and Spearman ρ correlation coefficients. Linear regression identified the optimal set of reliable iBASFI PBM. RESULTS: Good to excellent test-retest reliability was found for 8/12 iBASFI items (ICC range 0.812-0.997, SEM range 0.4-30.4%), typically with repeated and fast movements. Automated algorithms excellently mimicked manual segmentation (ICC range 0.900-0.998) and the chronometer (ICC range 0.878-0.998) for 10/12 iBASFI items. Construct validity compared with the BASFI was confirmed for 7/12 iBASFI items (δ range 0.504-0.755). Together, sit-to-stand speed test (stBeta 0.483), cervical rotation (stBeta -0.392), and height (stBeta -0.375) explained 59% of the variance in the BASFI (p < 0.01). CONCLUSION: The proof-of-concept iBASFI showed promising reliability and validity in measuring activity capacity. The number of the iBASFI's PBM may be minimized, but further validation in larger axSpA cohorts is needed before its clinical use.

Long-term accelerometry-triggered video monitoring and detection of tonic-clonic and clonic seizures in a home environment: Pilot study.

PURPOSE: The aim of our study was to test the efficacy of the VARIA system (video, accelerometry, and radar-induced activity recording) and validation of accelerometry-based detection algorithms for nocturnal tonic-clonic and clonic seizures developed by our team. METHODS: We present the results of two patients with tonic-clonic and clonic seizures, measured for about one month in a home environment with four wireless accelerometers (ACM) attached to wrists and ankles. The algorithms were developed using wired ACM data synchronized with the gold standard video-/electroencephalography (EEG) and then run offline on the wireless ACM signals. Detection of seizures was compared with semicontinuous monitoring by professional caregivers (keeping an eye on multiple patients). RESULTS: The best result for the two patients was obtained with the semipatient-specific algorithm which was developed using all patients with tonic-clonic and clonic seizures in our database with wired ACM. It gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night. This included 13 extra seizures detected (31%) compared with professional caregivers' observations. CONCLUSION: While the algorithms were previously validated in a controlled video/EEG monitoring unit with wired sensors, we now show the first results of long-term, wireless testing in a home environment.

Automated Detection of Tonic-Clonic Seizures Using 3-D Accelerometry and Surface Electromyography in Pediatric Patients.

Epileptic seizure detection is traditionally done using video/electroencephalography monitoring, which is not applicable for long-term home monitoring. In recent years, attempts have been made to detect the seizures using other modalities. In this study, we investigated the application of four accelerometers (ACM) attached to the limbs and surface electromyography (sEMG) electrodes attached to upper arms for the detection of tonic-clonic seizures. sEMG can identify the tension during the tonic phase of tonic-clonic seizure, while ACM is able to detect rhythmic patterns of the clonic phase of tonic-clonic seizures. Machine learning techniques, including feature selection and least-squares support vector machine classification, were employed for detection of tonic-clonic seizures from ACM and sEMG signals. In addition, the outputs of ACM and sEMG-based classifiers were combined using a late integration approach. The algorithms were evaluated on 1998.3 h of data recorded nocturnally in 56 patients of which seven had 22 tonic-clonic seizures. A multimodal approach resulted in a more robust detection of short and nonstereotypical seizures (91%), while the number of false alarms increased significantly compared with the use of single sEMG modality (0.28-0.5/12h). This study also showed that the choice of the recording system should be made depending on the prevailing pediatric patient-specific seizure characteristics and nonepileptic behavior.

Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection.

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a nonparametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a positive predictive value of 60.04%. However, there is a noticeable interpatient difference.

Non-EEG seizure-detection systems and potential SUDEP prevention: state of the art.

PURPOSE: There is a need for a seizure-detection system that can be used long-term and in home situations for early intervention and prevention of seizure related side effects including SUDEP (sudden unexpected death in epileptic patients). The gold standard for monitoring epileptic seizures involves video/EEG (electro-encephalography), which is uncomfortable for the patient, as EEG electrodes are attached to the scalp. EEG analysis is also labour-intensive and has yet to be automated and adapted for real-time monitoring. It is therefore usually performed in a hospital setting, for a few days at the most. The goal of this article is to provide an overview of body signals that can be measured, along with corresponding methods, state-of-art research, and commercially available systems, as well as to stress the importance of a good detection system. METHOD: Narrative literature review. RESULTS: A range of body signals can be monitored for the purpose of seizure detection. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important patho-physiological mechanism of SUDEP, and of movement, as many seizures have a motor component. CONCLUSION: The most effective seizure detection systems are multimodal. Such systems should also be comfortable and low-power. The body signals and modalities on which a system is based should take account of the user's seizure types and personal preferences.

Long-term home monitoring of hypermotor seizures by patient-worn accelerometers.

Long-term home monitoring of epileptic seizures is not feasible with the gold standard of video/electro-encephalography (EEG) monitoring. The authors developed a system and algorithm for nocturnal hypermotor seizure detection in pediatric patients based on an accelerometer (ACM) attached to extremities. Seizure detection is done using normal movement data, meaning that the system can be installed in a new patient's room immediately as prior knowledge on the patient's seizures is not needed for the patient-specific model. In this study, the authors compared video/EEG-based seizure detection with ACM data in seven patients and found a sensitivity of 95.71% and a positive predictive value of 57.84%. The authors focused on hypermotor seizures given the availability of this seizure type in the data, the typical occurrence of these seizures during sleep, i.e., when the measurements were done, and the importance of detection of hypermotor seizures given their often refractory nature and the possible serious consequences. To our knowledge, it is the first detection system focusing on this type of seizure in pediatric patients.

Autonomic effects of refractory epilepsy on heart rate variability in children: influence of intermittent vagus nerve stimulation.

AIM: Vagus nerve stimulation (VNS) is a therapeutic option for individuals with refractory epilepsy. Individuals with refractory epilepsy are prone to dysfunction of the autonomic nervous system. Reduced heart rate variability is a marker of dysfunction of the autonomic nervous system. Our goal was to study heart rate variability in children with refractory epilepsy and the influence of VNS on this parameter. METHODS: In 17 children (13 male; four female; mean age 7 y 6 mo; age range 3-16 y) with refractory epilepsy, electroencephalographic and electrocardiographic data were obtained before and after implantation of VNS during stage 2 and slow-wave sleep. Time and frequency domain parameters were calculated and the results were compared with an age- and sex-matched group of individuals without refractory epilepsy. RESULTS: Our results show that autonomic cardiac control is affected in individuals with refractory epilepsy. There is a striking reduction in vagal tone during slow-wave sleep and modulation capacity is smaller than in individuals without refractory epilepsy. Implantation of VNS induces a shift in sympathovagal balance towards sympathetic predominance and an improvement in autonomic modulation. INTERPRETATION: Heart rate variability is affected in children with refractory epilepsy, and changes after implantation of VNS. The observed changes could be of importance in the cardiac complications of individuals with epilepsy and should be explored in more detail.