1,720,964 research outputs found
Predicting and monitoring blood glucose through nutritional factors in type 1 diabetes by artificial neural networks
Full text linkThe monitoring and management of Postprandial Glucose Response (PGR), by administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) patients. Artificial Pancreas (AP), which combines autonomous insulin delivery and blood glucose sensor, is a promising solution; nevertheless, it still requires input from patients about meal carbohydrate intake for bolus administration. This is due to the limited knowledge of the factors that influence PGR. Even though meal carbohydrates are regarded as the major factor influencing PGR, medical experience suggests that other nutritional should be considered. To address this issue, in this work, we propose a Machine Learning (ML)-based approach for a more comprehensive analysis of the impact of nutritional factors (i.e., carbohydrates, protein, lipids, fiber, and energy intake) on the blood glucose levels (BGLs). In particular, the proposed ML-model takes into account BGLs, insulin doses, and nutritional factors in T1D patients to predict BGLs in 60-minute time windows after a meal. A Feed-Forward Neural Network was fed with different combinations of BGLs, insulin, and nutritional factors, providing a predicted glycaemia curve as output. The validity of the proposed system was demonstrated through tests on public data and on self-produced data, adopting intra- and inter-subject approach. Results anticipate that patient-specific data about nutritional factors of a meal have a major role in the prediction of postprandial BGLs
Low-Density EEG Correction With Multivariate Decomposition and Subspace Reconstruction
Full text linkA hybrid method is proposed for removing artifacts from electroencephalographic (EEG) signals. This relies on the integration of artifact subspace reconstruction (ASR) with multivariate empirical mode decomposition (EMD). The method can be applied when few EEG sensors are available, a condition in which existing techniques are not effective, and it was tested with two public datasets: 1) semisynthetic data and 2) experimental data with artifacts. One to four EEG sensors were taken into account, and the proposal was compared to both ASR and multivariate EMD (MEMD) alone. The proposed method efficiently removed muscular, ocular, or eye-blink artifacts on both semisynthetic and experimental data. Unexpectedly, the ASR alone also showed compatible performance on semisynthetic data. However, ASR did not work properly when experimental data were considered. Finally, MEMD was found less effective than both ASR and MEMD-ASR
Entropy-Based EEG Measures for Revealing Altered Neural Dynamics in Alzheimer's Disease: A Preliminary Study
No full textAlzheimer's disease (AD) is a prevalent age-related neurodegenerative condition, whose timely diagnosis is nowadays still complicated. In this context, investigating brain complexity can provide insight into the underlying mechanisms of neural dynamics in AD patients. In this regard, this study explores the differences in neural complexity between AD patients and healthy controls by using entropy measures. Specifically, multi-scale sample entropy (MSE) and multiscale approximate entropy (MAE) was computed on multi-channel electroencephalografic (EEG) signals at different time scales, highlighting the advantages and disadvantages of these two techniques, and outlining future improvements. Results show the potential of entropy measures as a promising strategy for early diagnosis of AD and for a better understanding of EEG dynamics alteration in AD, allowing for the development of more targeted therapies
Explainable AI Assessment of Meal-Related Features Impact in Predicting Basal Insulin for Type I Diabetes
No full textType 1 diabetes management using artificial pan-creas (AP) technology faces challenges in accurately regulating postprandial glucose levels. Current AP systems, based on heuris-tic control algorithms for basal insulin delivery, still require user input for mealtime insulin delivery. In the context of personalized patient-centered medicine, this study explores the impact of meal-related features on predicting postprandial basal insulin requirements using explainable artificial intelligence (XAI). Machine learning (ML) models were trained using preprandial blood glucose, mealtime insulin, preprandial basal insulin, and features from 15 T1D patients. The performance in terms of the root-mean-square-error (RMSE) and the mean-absolute-error (MSE) were (0.42 ± 0.1) insulin unit and (0.32 ± 0.07) insulin unit, respectively. Employing SHapley Additive exPlanations (SHAP) reveals significant influences of mealtime insulin bolus, carbohy-drates, and glycemic load, aligning with physiological knowledge on postprandial glycemia response (PGR)
Comparison of EEG Preprocessing Techniques for Complexity Measures in Alzheimer's Disease Detection
No full textAlzheimer's disease (AD) is a neurodegenerative condition characterized by memory loss, cognitive difficulties, and behavioral changes. Currently, there are no known permanent cure for this condition. Early and precise diagnosis is crucial for effective therapeutic intervention. This study introduces an unsupervised machine learning (ML)-based method for identifying patterns in electroencephalographic (EEG) signals from both healthy subjects and AD patients. Emphasis was placed on the preprocessing phase, examining the effects of various EEG data normalization techniques on the results. This generalized approach addresses the significant inter- and intra-subjective variability inherent in biological data, thereby enhancing method robustness and data consistency. Following the preprocessing phase, Multiscale Fuzzy Entropy (MFE) was derived from EEG signals. A k-means clustering algorithm was applied to identify distinct patterns. The efficacy of the clusters was assessed by using Silhouette Score (SS), Adjusted Rand Index Score (ARI), Adjusted Mutual Information Score (AMI) and V-Measure Score. The method was validated using a public EEG dataset. The results indicated enhanced clustering efficacy when normalization was applied, underscoring the critical importance of data preprocessing in the detection of AD
Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System
Full text linkThe present study introduces a brain–computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a “neurofeedback” group, which performed motor imagery while receiving feedback, and a “control” group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual’s ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain–computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation
Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI
Full text link: Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients
A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy
Full text linkAlzheimer’s disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
