Explore the vast range of titles available.

We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission).

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Abstract Background and Objectives The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation. Research Design and Methods The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically. Results Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute β: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated. Discussion and Implications The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification of agitation.

Among the most critical behavioral and psychological symptoms of dementia, agitation can lead to decreased quality of life of people with dementia and their caregivers. Monitoring triggers of agitation and its subtypes could enable early detection or prediction of agitated moments, which could be used to guide preventive or mitigating interventions. However, at this point in time, limited research exists on quantifying environmental triggers of agitation or its subtypes. In this paper, we aim to quantify the relationships between specific environmental factors and agitation as well as specific agitation subtypes, such as motor and verbal agitation. Using a cross-sectional repeated measures design, 37 people with dementia, admitted to a specialized neuropsychiatric ward for patients with dementia and severe behavioral and psychological problems, were each included for 1 week. During this period, the Pittsburgh Agitation Scale was filled in by the nurses on the ward following an experience sampling methodology to assess a patient's agitation level on a momentary basis. Continuous environmental data (light, sound, and temperature) were collected from fixed sensors mounted on the ward. Generalized linear mixed models were used to quantify relationships between environmental variables and outcome variables (agitation, motor agitation, and verbal agitation). These models accounted for the hierarchical nature of our dataset as well as confounding factors, such as time of day and the room-level location of the patient. The time window for analysis was selected through a comparison of β coefficient estimates across various window lengths. Models were built up sequentially, per outcome variable, using selected features per environmental modality. We found that different environmental factors captured in the window of 33 to 12 minutes before the agitation moment were most informative for different subtypes of agitation: mean light level (β=-0.61, 95% CI -1.12 to -0.10; P=.02) for motor agitation and SD of sound level (β=0.68, 95% CI 0.34-1.02; P<.001) for verbal agitation. Contextual factors such as time of day (β range=0.51-0.94; P<.05 to <.001) and room-level location (β range=0.85-1.08; P<.01 to <.001) were also significant predictors of agitation. Integrating the key differences between predictors of verbal and motor agitation, respectively, the higher SD in sound level and the lower mean light level, in a model predicting the occurrence of subtype-specific agitation, could substantially improve model performance. Overall, these findings can aid in the development of predictive models for agitation based on environmental data and enable subsequent just-in-time interventions, improving the quality of life for both patients and caregivers.

Abstract Background and Objectives Agitation, a critical behavioral and psychological symptom in dementia, has a profound impact on a patients’ quality of life as well as their caregivers’. Autonomous and objective characterization of agitation with multimodal systems has the potential to capture key patient responses or agitation triggers. Research Design and Methods In this article, we describe our multimodal system design that encompasses contextual parameters, physiological parameters, and psychological parameters. This design is the first to include all three of these facets in an n > 1 study. Using a combination of fixed and wearable sensors and a custom-made app for psychological annotation, we aim to identify physiological markers and contextual triggers of agitation. Results A discussion of both the clinical as well as the technical implementation of the to-date data collection protocol is presented, as well as initial insights into pilot study data collection. Discussion and Implications The ongoing data collection moves us toward improved agitation quantification and subsequent prediction, eventually enabling just-in-time intervention.

Background With the increase of the average life expectancy, the number of people with dementia (PwD) is expected to increase to 152 million by 2050. Agitation is a very frequent and clinically important neuropsychiatric symptom of dementia, that is associated with more rapid progression of the disease, increased morbidity and mortality, and higher economical costs. However, there is currently no way to robustly detect agitation and there is a clear need to understand what causes agitation in each person with dementia. Current methods with questionnaires are very subjective, prone to bias and very intermittent. This is an important factor in the current sub‐optimal prevention and treatment of agitation, and the overuse of potentially dangerous psychotropic medication in PwD. Method We designed a unique platform combining non‐obtrusive physiological sensors worn by the PwD, location and environmental sensors, and ecological momentary assessment by staff, in order to continuously monitor agitation, discover digital biomarkers of agitation and identify triggers for agitation both on an individual and population level. Result So far, we included 42 patients in our study, that were monitored for at least 7 consecutive days and nights, to form the largest database of its kind in the world. We will present our current findings on potential digital biomarkers of agitation in dementia. Data will also be presented on which environmental triggers are involved in different types of agitation. Finally, we will elaborate on lessons learned during this project that can guide future research in this field. Conclusion Multimodal sensing combined with machine learning techniques, could allow for more continuous, objective and quantifiable monitoring of agitation in dementia. This could lead to a better evaluation of interventions, lower need for potentially dangerous psychotropic drug use, less caregiver stress and lower economic costs. Our results support the initial hypothesis of population heterogeneity and they represent the base for personalized agitation detection and treatment evaluation using physiological markers and environmental triggers.

With the increase of the average life expectancy, the number of people with dementia (PwD) is expected to increase to 152 million by 2050. Agitation is a very frequent and clinically important neuropsychiatric symptom of dementia, that is associated with more rapid progression of the disease, increased morbidity and mortality, and higher economical costs. However, there is currently no way to robustly detect agitation and there is a clear need to understand what causes agitation in each person with dementia. Current methods with questionnaires are very subjective, prone to bias and very intermittent. This is an important factor in the current sub-optimal prevention and treatment of agitation, and the overuse of potentially dangerous psychotropic medication in PwD. We designed a unique platform combining non-obtrusive physiological sensors worn by the PwD, location and environmental sensors, and ecological momentary assessment by staff, in order to continuously monitor agitation, discover digital biomarkers of agitation and identify triggers for agitation both on an individual and population level. So far, we included 42 patients in our study, that were monitored for at least 7 consecutive days and nights, to form the largest database of its kind in the world. We will present our current findings on potential digital biomarkers of agitation in dementia. Data will also be presented on which environmental triggers are involved in different types of agitation. Finally, we will elaborate on lessons learned during this project that can guide future research in this field. Multimodal sensing combined with machine learning techniques, could allow for more continuous, objective and quantifiable monitoring of agitation in dementia. This could lead to a better evaluation of interventions, lower need for potentially dangerous psychotropic drug use, less caregiver stress and lower economic costs. Our results support the initial hypothesis of population heterogeneity and they represent the base for personalized agitation detection and treatment evaluation using physiological markers and environmental triggers.

This paper describes accurate monitoring of hydration using impedance variation in a human being, which accompanies extracellular fluid loss or gain. A prototype of a precision multifrequency bioimpedance meter built around advanced biomedical S°C (System on Chip) MUSEIC v.2 was used in this study. Calibration of the bioimpedance board was conducted on an RC-model of the volunteer selected for the experiments. The model was built using the averaged results of multiple impedance measurements on the subject within the 1 Hz - 100 kHz, which were repeated on different days on near-euhydrated volunteer. The sources of observed variability in bioimpedance have been studied. Several factors were spotted that affect hydration assessment but were not reported in the literature before. The specific protocols for reaching fluid shift were developed in this study. They enabled minimization of errors in altered hydration state. The bioimpedance method is shown in this research to correctly reflect hydration variation in a single person, so that there is no need for averaging over large population to observe the trend. For demonstration of sensitivity of the developed device to fluid shift, it was tested on the volunteer undergoing repeated mild dehydration and rehydration using light-effort exercise (outdoor cycling). The bioimpedance results were compared with the reference hydration. The latter was obtained using the subject weight measurement and precise counting of caloric intake, the weight of food, and also with the aid of established weight baseline prior to any planned experiment. Special attention has been paid to sodium balance, and several diets have been developed for its regulation. The predictable body fluid loss and gain was supported by measured sweating rate, and also by dehydration and rehydration diets designed for precise control of ion and water intake. The accuracy of fluid shift measurement down to a standard deviation of 200 ml is demonstrated, which essentially exceeds capabilities of known methods and devices, including 'gold standards' like isotope dilution for hydration assessment. Such accuracy satisfies requirements of healthcare and sport. The device has not yet been validated on population.

Abstract It is an uninformative truism to state that the brain operates at multiple spatial and temporal scales, each with each own set of emergent phenomena. More worthy of attention is the point that our current understanding of it cannot clearly indicate which of these phenomenological scales are the significant contributors to the brain’s function and primary output (i.e. behaviour). Apart from the sheer complexity of the problem, a major contributing factor to this state of affairs is the lack of instrumentation that can simultaneously address these multiple scales without causing function altering damages to the underlying tissue. One important facet of this problem is that standard neural recording devices normally require one output connection per electrode. This limits the number of electrodes that can fit along the thin shafts of implantable probes generating a limiting balance between density and spread. Sharing a single output connection between multiple electrodes relaxes this constraint and permits designs of ultra-high density probes. Here we report the design and in-vivo validation of such a device, a complementary metal-oxide-semiconductor (CMOS) scanning probe with 1344 electrodes; the outcome of the European research project NeuroSeeker. We show that this design targets both local and global spatial scales by allowing the simultaneous recording of more than 1000 neurons spanning 7 functional regions with a single shaft. The neurons show similar recording longevity and signal to noise ratio to passive probes of comparable size and no adverse effects in awake or anesthetized animals. Addressing the data management of this device we also present novel visualization and monitoring methods. Using the probe with freely moving animals we show how accessing a number of cortical and subcortical brain regions offers a novel perspective on how the brain operates around salient behavioural events. Finally, we compare this probe with lower density, non CMOS designs (which have to adhere to the one electrode per output line rule). We show that an increase in density results in capturing neural firing patterns, undetectable by lower density devices, which correlate to self-similar structures inherent in complex naturalistic behaviour. To help design electrode configurations for future, even higher density, CMOS probes, recordings from many different brain regions were obtained with an ultra-dense passive probe. Footnotes * Added more animals results for the analysis presented