This work introduces a novel methodology for Electroencephalography (EEG) data analysis in the context of neurological diseases, emphasizing feature extraction through covariance matrices and their integration with Case-Based Reasoning (CBR). Departing from traditional techniques such as Fast Fourier Transform (FFT) and statistical analysis, we investigate the synergy between covariance matrices and CBR, highlighting their potential to improve the efficacy of EEG data analysis over conventional methods like Random Forest (RF) and Support Vector Machine (SVM). Covariance matrices analyze the relationships between channels, indirectly capturing interactions between brain regions, while CBR uses similarities in these relationship patterns across cases to make decisions, both techniques focusing on understanding the data through its interrelationships. Additionally, we incorporate Tangent Space Mapping (TSM) to make the covariance matrices more suitable for traditional classifiers by projecting them into a space that preserves their geometric properties. Empirical results on public EEG datasets show that CBR, using covariance matrices with TSM, achieves the best accuracy of 0.72 for Alzheimer's Disease (AD) and up to 0.83 for Parkinson's Disease (PD).

Stress is a prevalent global concern impacting individuals across various life aspects. This paper investigates stress detection using electroencephalographic (EEG) signals, which have proven valuable for studying neural correlates of stress. Stress was induced in students, and physiological data was recorded as part of the experimental setup. Different feature sets were extracted and four machine learning models, including LightGBM, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were utilized for classification tasks. The findings indicate that the mean and standard deviation of 19 channels consistently outperform other feature sets. LightGBM demonstrates superior performance across all scenarios compared to CNN, KNN, and SVM. Overall, this study presents an effective stress detection approach using EEG signals and demonstrates the potential of integrating simple statistical features for enhanced classification accuracy. The findings contribute to the advancement of stress monitoring technologies, with potential applications in wearables and BCIs for real-time stress management.

Abstract HTE 3.0 aims to support clinicians in the detection of patients with dyslipidemia, especially patients with familial hypercholesterolemia (FH), and in the recommendation of personalized lipid‐lowering treatments. The core of HTE 3.0 is a clinical decision support system in which several knowledge‐based systems are serialized: patient detection, therapeutic target setting, personalized treatment assessment, and treatment combination and prioritization, according to different criteria. The experimental evaluation of HTE 3.0 shows that the use of HTE 3.0 would mean increasing the capacity to detect FH by 5.7 times compared with usual clinical practice. Regarding the lipid‐lowering treatment, a comparison of 18 cases among seven lipidologists shows that the differences between treatments provided by HTE 3.0 and human lipidologists are smaller than the differences between human experts.

In this work, we introduce TA4L, a new efficient algorithm to transform multivariate time series into Lexicographical Symbolic Time Interval Sequences (LSTISs), that is, sequences ready to feed time-interval related pattern (TIRP) mining algorithms. The ultimate goal is to make explicit the embedded, ad-hoc pre-processes related to TIRP mining algorithms while offering an efficient solution for the required pre-processing. On the one hand, TA4L divides the signals into segments based on time duration (instead of the often-used practice based on the number of samples), which allows the construction of consistent time intervals. Concatenation of intervals is controlled by a maximum time gap constraint that reinforces the generated time intervals’ consistency. Moreover, different ways to parallelise the algorithm are explored that are accompanied by efficient data structures to speed up the pre-processing cost. TA4L has been experimentally evaluated with synthetic and real datasets, and the results show that TA4L requires significantly less computation time than other state-of-the-art approaches, revealing that it is an effective algorithm.

Monitoring emotions are gaining attention in the care of mental disease and behavioural health changes. To that end, there is an increasing interest in measuring emotions with sensors. In particular, deep learning approaches are being used for feature learning that enables emotion recognition. In this work, the focus is on using unsupervised techniques, as pattern mining, to characterise physiological signals from the classification of emotions with complementary predictive methods. An analysis is conducted to compare the performance results of the feature learning approaches regarding the pattern mining approach and the different approaches' properties.

Type 1 diabetes mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.

Lower-limb fracture surgery is one of the major causes for autonomy loss among aged people. For care institutions, tackling with an optimized rehabilitation process is a key factor as it improves both the patients quality of life and the associated costs of the after surgery process. This paper presents bag-of-steps, a new methodology to predict the rehabilitation length and discharge date of a patient using insole force sensors and a predictive model based on the bag-of-words technique. The sensors information is used to characterize the patients gait creating a set of step descriptors. This descriptors are later used to define a vocabulary of steps using a clustering method. The vocabulary is used to describe rehabilitation sessions which are finally entered to a classifier that performs the final rehabilitation estimation. The methodology has been tested using real data from patients that underwent surgery after a lower-limb fracture

Complex event processing is key technology for current business in which sequences of events are controlled. However, defining complex events is not easy, and sequence learning algorithms can help. To that end, sequence learning methods should consider temporal relationship among events. In this paper, we tackle the problem of mining complex events using frequent sequence pattern mining with time gaps. A constraint model of the learning problem is proposed. Consistently, the learning problem is addressed using solvers off the shelf. The experiments are carried out in a bike hiring domain so as a CEP system can account how many users will reach a depot, independently of which was their origin. Results are analysed in terms of individual and multiple users, as well as regarding the scalability of the method.

Auctions have been used to deal with resource allocation in multiagent environments, especially in service‐oriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.

Auctions have been used to deal with resource allocation in multi-agent systems. In some environments like service-oriented electronic markets, it is advisable to use recurrent auctions since resources are perishable and auctions are repeated over time with the same or a very similar set of agents. Recurrent auctions are a sequence of one-shot auctions of any kind. As a drawback some problems do appear that could cause the market to collapse at mid-long term. Previous works have dealt with these problems by adding fairness to the auction outcomes but they dealt with multi-unit auctions, in which several units of an item are sold. In this paper, we present a new fair mechanism for multi-unit combinatorial auctions, in which different items, and several units per item are sold in each auction.

This paper presents an approach for service monitoring through workflow modeling and complex event processing. Workflows allow the representation of services process interactions while complex event processing (CEP) is a concept for event driven architectures which offers an alternative solution for monitoring and supervision. In this paper we propose a methodology to combine both technologies where CEP is used to monitor workflows and to predict possible delays. A case study on medical equipment maintenance business is shown.

The paper discusses maintenance challenges of organisations with a huge number of devices and proposes the use of probabilistic models to assist monitoring and maintenance planning. The proposal assumes connectivity of instruments to report relevant features for monitoring. Also, the existence of enough historical registers with diagnosed breakdowns is required to make probabilistic models reliable and useful for predictive maintenance strategies based on them. Regular Markov models based on estimated failure and repair rates are proposed to calculate the availability of the instruments and Dynamic Bayesian Networks are proposed to model cause-effect relationships to trigger predictive maintenance services based on the influence between observed features and previously documented diagnostics.

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage.

Rating the intelligence of artificially made systems is important for measuring progress in scientific and engineering methods. Unfortunately, there is currently no universal agreement about what is considered an intelligent system, and how to measure its intelligence. This research focus on measuring the progress in the robotic technologies deployed for the RoboCup competitions, since one of the original premises of those competitions was to advance the development of intelligent robotic systems. A method used for rating the competence of human chess players is adapted for measuring the advancement in the competence of robotic teams. The results indicate significant yearly improvements in the capabilities of the robotic teams. The same method can be used to indirectly quantify the benefits in specific technology choices.

This paper presents an efficient technique for determining a low-cost disassembly sequence suitable to extract a subset of s components from an assembly containing n components, e of which are exterior (e/spl Lt/n). The most efficient solution to this so-called geometric selective disassembly problem is the wave propagation algorithm, which is reported to have a computational complexity of O(sn/sup 2/). Instead, the complexity of the proposed algorithm is O(enlogn) when s/spl Lt/n, and O(sn) when s/spl sime/n. Experimental results with synthetic 3D assemblies are presented.