Importance of methodological choices in data manipulation for validating epileptic seizure detection models

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet widely available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, difficulty in comparing results, and low reproducibility. Therefore, this article identifies a wide range of methodological decisions that must be made and reported when training and evaluating the performance of epilepsy detection systems. We characterize the influence of individual choices using a typical ensemble random-forest model and the publicly available CHB-MIT database, providing a broader picture of each decision and giving good-practice recommendations, based on our experience, where possible.RYC2021-032853-

Neural network architecture optimization using automated machine learning for borehole resistivity measurements

Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. Using extremely large DNNs to approximate the operators is possible, but it demands considerable training time. Moreover, evaluating the network after training also requires a significant amount of memory and processing power. In addition, we may overfit the model. In this work, we propose a scoring function that accounts for the accuracy and size of the DNNs compared to a reference DNNs that provides good approximations for the operators. Using this scoring function, we use DNN architecture search algorithms to obtain a quasi-optimal DNN smaller than the reference network; hence, it requires less computational effort during training and evaluation. The quasi-optimal DNN delivers comparable accuracy to the original large DNN.PDC2021-121093-I00 IA4TES RYC2021-032853-

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while running on a lightweight, portable device that protects the patient’s privacy. Several devices and algorithms have been developed for cough counting, but many use only error-prone audio signals, rely on offline processing that compromises data privacy, or utilize processing and memory-intensive neural networks that require more hardware resources than can fit on a wearable device. Therefore, there is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge Artificial Intelligence (edge-AI) fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events from 15 subjects. Furthermore, a variety of non-cough sounds and motion scenarios mimicking daily life activities are also present, which the research community can use to accelerate machine learning (ML) algorithm development. A technical validation of the dataset reveals that it represents a wide variety of signal-to- noise ratios, which can be expected in a real-life use case, as well as consistency across experimental trials. Finally, to demonstrate the usability of the dataset, we train a simple cough vs non-cough signal classifier that obtains a 91% sensitivity, 92% specificity, and 80% precision on unseen test subject data. Such edge-friendly AI algorithms have the potential to provide continuous ambulatory monitoring of the numerous chronic cough patients.RYC2021-032853-

Machine learning discovery of optimal quadrature rules for isogeometric analysis

We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and non-uniform knot sequences, and then the generated optimal rules are used for integration over higher-dimensional spaces using tensor products. The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on adaptive gradient-descent. However, since the optimization space is highly non-convex, the success of the search strongly depends on the number of quadrature points and the parameter initialization. Thus, we use a dynamic programming strategy that initializes the parameters from the optimal solution over the spline space with a lower number of knots. With this method, we found optimal quadrature rules for spline spaces when using IGA discretizations with up to 50 uniform elements and polynomial degrees up to 8, showing the generality of the approach in this scenario. For non-uniform partitions, the method also finds an optimal rule in a reasonable number of test cases. We also assess the generated optimal rules in two practical case studies, namely, the eigenvalue problem of the Laplace operator and the eigenfrequency analysis of freeform curved beams, where the latter problem shows the applicability of the method to curved geometries. In particular, the proposed method results in savings with respect to traditional Gaussian integration of up to 44% in 1D, 68% in 2D, and 82% in 3D spaces.Euskampus Foundation through the ORLEG-IA project in the Misiones Euskampus 2.0 program. RYC2021-032853-I/MCIN/AEI/10.13039/501100011033 funded by the Spanish Ministry of Science and Innovation and by the European Union NextGenerationEU/PRTR

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

Determining population trends and conservation status of the common quail (Coturnix coturnix) in Western Europe

In this paper we review the conservation status and population trends of the common quail (Coturnix coturnix) from 1900 to the present. Data are sometimes contradictory with regard to the status of this species as it has some features that make it difficult to produce reliable population estimates. Recent data clearly suggest, either at a local scale or at a trans–national scale, that the Atlantic common quail populations have remained stable in the last two decades, and that restocking practices with farm–reared quails (hybrids with the Japanese quail, Coturnix japonica) do not affect our estimates. The complex movement patterns showed by this species require special attention. Analysis of ring recoveries can give important information, especially about the nomadic movement of quails in search of suitable habitats after the destruction of winter cereal crops due to harvesting. Thus, when developing a breeding distribution model for this species, continuously updated information on seasonal habitat and weather must be included for optimal prediction. Including fortnightly data of vegetation indices in distribution models, for example, has shown good results. Obtaining reliable predictions about changes in species distribution and movements during the breeding period could provide useful knowledge about the conservation status and population trends and would help in the design of future management measures

Post–breeding movements and migration patterns of western populations of common quail (Coturnix coturnix): from knowledge to hunting management

Patrones de movimientos y de migración postcría en la población occidental de codorniz común (Coturnix coturnix): algunas recomendaciones de gestión cinegética Hemos investigado los patrones de los movimientos postcría de la codorniz común (Coturnix coturnix) en la península ibérica con el fin de describir su fenología de paso migratorio y algunas características fisiológicas de los individuos. Esta información es necesaria para un ajuste óptimo de los períodos de caza. Hemos trabajado a partir de dos conjuntos de datos: a) capturas efectuadas en una zona que no es de cría (Garraf) de agosto a octubre en 2009 y 2010; b) recuperaciones, posteriores a la presunta época de cría, de individuos anillados en Europa y recapturados en España durante el período 1933–2005. Los resultados obtenidos muestran que los movimientos postcría en Garraf están formados por dos oleadas: una primera, que se produce sobre el 10 VIII, formada principalmente por jóvenes del año inactivos sexualmente que no son fisiológicamente migrantes; y una segunda, mucho más intensa, que se produce sobre el 17 IX, formada principalmente por migrantes jóvenes del año inactivos sexualmente. La época de caza en España tiene lugar principalmente durante la primera oleada, preservando el paso de los migrantes provenientes de España y de otros países europeos. La información de los movimientos postcría en otras regiones españolas y en otros países europeos en los que la codorniz común es una especie cinegética popular, permitiría mejorar el ajuste entre el período de caza y la migración, proporcionando recomendaciones de gestión cinegética más precisas para esta especie.Patrones de movimientos y de migración postcría en la población occidental de codorniz común (Coturnix coturnix): algunas recomendaciones de gestión cinegética Hemos investigado los patrones de los movimientos postcría de la codorniz común (Coturnix coturnix) en la península ibérica con el fin de describir su fenología de paso migratorio y algunas características fisiológicas de los individuos. Esta información es necesaria para un ajuste óptimo de los períodos de caza. Hemos trabajado a partir de dos conjuntos de datos: a) capturas efectuadas en una zona que no es de cría (Garraf) de agosto a octubre en 2009 y 2010; b) recuperaciones, posteriores a la presunta época de cría, de individuos anillados en Europa y recapturados en España durante el período 1933–2005. Los resultados obtenidos muestran que los movimientos postcría en Garraf están formados por dos oleadas: una primera, que se produce sobre el 10 VIII, formada principalmente por jóvenes del año inactivos sexualmente que no son fisiológicamente migrantes; y una segunda, mucho más intensa, que se produce sobre el 17 IX, formada principalmente por migrantes jóvenes del año inactivos sexualmente. La época de caza en España tiene lugar principalmente durante la primera oleada, preservando el paso de los migrantes provenientes de España y de otros países europeos. La información de los movimientos postcría en otras regiones españolas y en otros países europeos en los que la codorniz común es una especie cinegética popular, permitiría mejorar el ajuste entre el período de caza y la migración, proporcionando recomendaciones de gestión cinegética más precisas para esta especie.We investigated the patterns of post–breeding movements of the common quail (Coturnix coturnix) in the Iberian peninsula with the aim of describing its migratory phenology and some physiological features of individuals. This information is needed to adjust hunting seasons in an optimal way. We worked with two data–sets: a) captures made in a non–breeding site (Garraf) from August to October in 2009 and 2010; b) post–breeding recoveries of individuals ringed in Europe and recaptured in Spain between 1933 and 2005. The results showed that post–breeding movements in Garraf occur in two waves: a first wave that occurs around 10 VIII and is mainly composed of non–sexually active yearlings that do not correspond physiologically to migrants, and a second much more intense wave, which occurs around 17 IX and is mainly composed of non–sexually active migrant yearlings. The hunting season in Spain takes place mainly during the first wave, preserving the passage of migrant individuals from Spain and other European countries. Information on the post–breeding movements in other Spanish regions and other European countries where the common quail is a popular game species would improve timing between the hunting season and migration by providing more precise recommendations for hunting management

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase remains a challenge, particularly for biomedical wearable systems. Traditional training algorithms might not be suitable for these applications due to the substantial memory requirements and high computational costs associated with processing the large number of bits involved in neural network operations. In this paper, we introduce a novel learning procedure specifically designed for low-power wearable systems, dubbed Bio-BPfree (deep neural network training without backpropagation for low-power wearable systems). Using a two-class classification task, Bio-BPfree replaces conventional forward and backward backpropagation passes with four forward passes, two for data of the positive class and two for data of the negative class. Each layer is equipped with a unique objective function aimed at minimizing the distance between data points within the same class while maximizing the distance between data points from different classes. Our experimental results, which were obtained by conducting rigorous evaluations on the MIT-BIH dataset that features electrocardiogram (ECG) signals, effectively demonstrate the superior performance and suitability of Bio-BPfree for two-class classification tasks, particularly within the challenging environment of low-power wearable systems designed for continuous health monitoring and assessment.RYC2021-032853-