Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to ...capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.
A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.
The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.
This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.
In the above article <xref ref-type="bibr" rid="ref1">1 , the updated text for the Acknowledgment section is: "The RADAR-AD project has received funding from the Innovative Medicines Initiative 2 ...Joint Undertaking under grant agreement No 806999. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA ( http://www.imi.europa.eu )." Additionally, we would like to add the disclaimer: "This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein."
Preeclampsia (PE) is a hypertensive disorder of pregnancy that can cause detrimental obstetric outcomes if not managed properly. Current evidence demonstrates higher risk for long-term cardiovascular ...disease in preeclamptic women. Even in uncomplicated pregnancies, the heart work overload often reveals subtle cardiac defects or abnormalities, which otherwise remain undiagnosed in women without a history of pregnancy. Pathophysiologic patterns occurring in PE patients resemble biochemical responses observed in cases of cardiovascular disease. It has been estimated that women with an obstetric history of PE are more likely to develop coronary artery disease in the long run. Currently, additionally to whether any approach could actually contribute to minimizing mortality and morbidity among these affected populations, there is no consensus regarding management for these patients. In this review we summarized the current scientific evidence regarding the correlation between PE and long-term coronary artery disease. Based on this knowledge, we propose postpartum and lifetime management for these high-risk patients in order to minimize morbidity and mortality within this population.
Acute appendicitis is common in patients with right lower quadrant pain and affects all gender and age groups. Because clinical diagnosis of patients with right lower quadrant pain remains a ...challenge to emergency physicians and surgeons, imaging is of major importance. Ultrasound has well-established direct and indirect signs for diagnosing acute appendicitis and revealing the presence of an appendicolith. Appendectomy, which can be either open or laparoscopic, constitutes the basic treatment. However, the need for an appendectomy is debatable, particularly in high-risk patients. We report the case of a 42-year-old woman with no relevant medical history who was sent to the emergency department by her family physician with right lower quadrant pain of 18 hours' duration. Using ultrasound, the emergency physicians identified, inside the appendix, a 0.6cm appendiceal faecolith, migration of which was eventuated by manipulation of the ultrasound probe. The patient was then successfully treated non-operatively without any antibiotic prescription. Despite its rarity, migration of an appendiceal faecolith is possible. When migration of an appendicolith is perhaps actualised spontaneously or by ultrasound probe manipulation, the likelihood of an appendectomy decreases dramatically. This hypothesis provides patients who present an appendiceal faecolith with an alternative treatment approach that will lead to the avoidance of surgery, minimise morbidity and reduce hospitalisation costs.
Functional magnetic resonance imaging (fMRI) has become one of the most important techniques for studying the human brain in action. A common problem in fMRI analysis is the detection of activated ...brain regions in response to an experimental task. In this work we propose a novel clustering approach for addressing this issue using an adaptive regression mixture model. The main contribution of our method is the employment of both spatial and sparse properties over the body of the mixture model. Thus, the clustering approach is converted into a maximum a posteriori estimation approach, where the expectation-maximization algorithm is applied for model training. Special care is also given to estimate the kernel scalar parameter per cluster of the design matrix by presenting a multi-kernel scheme. In addition an incremental training procedure is presented so as to make the approach independent on the initialization of the model parameters. The latter also allows us to introduce an efficient stopping criterion of the process for determining the optimum brain activation area. To assess the effectiveness of our method, we have conducted experiments with simulated and real fMRI data, where we have demonstrated its ability to produce improved performance and functional activation detection capabilities.
In this study, we present an advanced Bayesian framework for the analysis of functional magnetic resonance imaging (fMRI) data that simultaneously employs both spatial and sparse properties. The ...basic building block of our method is the general linear regression model that constitutes a well-known probabilistic approach. By treating regression coefficients as random variables, we can apply an enhanced Gibbs distribution function that captures spatial constrains and at the same time allows sparse representation of fMRI time series. The proposed scheme is described as a maximum a posteriori approach, where the known expectation maximization algorithm is applied offering closed-form update equations for the model parameters. We have demonstrated that our method produces improved performance and functional activation detection capabilities in both simulated data and real applications.
This paper deals with the classification of steady-state visual evoked potentials (SSVEP), which is a multiclass classification problem addressed in SSVEP-based brain-computer interfaces. In ...particular, our method named MultiLRM_MKL uses multiple linear regression models under a Sparse Bayesian Learning (SBL) framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces. We verify the effectiveness of our method in handling different kernel spaces by evaluating its performance with a new kernel based on canonical correlation analysis. In particular, we prove the benefit of combining multiple kernels by outperforming several state-of-the-art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area (<inline-formula><tex-math notation="LaTeX">O_z</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">O_1</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">O_2</tex-math></inline-formula>).
InterDigitated Electrodes (IDEs) is a generic platform for a wide range of diverse applications with their implementation in sensing modules being a major one. We propose the use of IDCs with deep ...sub-micron critical dimension; equally spaced electrodes of 200 nm width for enhanced sensing performance and also the method of fabrication thereof. The transducer configuration was studied theoretically with a finite element method simulation by using COMSOL Multiphysics. The miniaturization of the IDEs up to 200 nm critical dimension with an adequate sensing area for the deposition of the polymeric materials is considered beneficial in terms of sensitivity gain. The IDCs were designed to deliver capacitance values of few pF in order to be compatible with already developed miniaturized low-power readout electronics. The transducers fabrication is performed with conventional microelectronic/micromachining processing and then coated with several semi-selective polymeric films. Besides the fabrication of multiple sensor arrays (chips) on the same silicon wafer, the miniaturization offers the integration with the readout electronics on the same chip. The evaluation of the sensing performance of the semi-selective polymer coated sensors is performed upon exposure to vapours of pure and binary mixtures of VOCs and humidity in various concentrations. The sensors demonstrate high sensitivity to the examined analytes as a result of the miniaturization, while their semi-selectivity is a key for applications in complex vapour environment discrimination.
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•Simulation with a finite element method and microfabrication of IDE arrays in the deep sub-micron regime•Chemocapacitors arrays for the detection of VOCs and humidity•High sensitivity as a result of the miniaturization of the IDE critical dimension
Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most ...commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field.
The realization of a wireless sensing system and its sensing performance evaluation, under laboratory conditions, for the monitoring of specific volatile organic compounds (VOCs) present in printed ...flexing packaging industries is demonstrated. Prior to the utilization of the wireless mote, we present the microfabrication of appropriate sensor array based on chemocapacitors and its integration with appropriate low power consumption read-out electronics meeting the requirements of the application. The sensing unit is an array of interdigitated chemocapacitors (IDCs). The wireless sensing system is tested upon exposure to VOCs, humidity and gaseous mixtures simulating the real industrial environment and the raw data are transmitted via a wireless network and monitored to a front-end software. Results showed that the sensing system is characterized by very good sensing performance with high repeatability and long-term stability. Further data processing with principal component analysis (PCA) highlights the sensing system's ability to discriminate between gaseous environments with different composition/concentration. Thus the particular wireless sensing system is suitable for remote real-time unattended industrial environment monitoring.
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