Methods for linear least squares problems in which the observation matrix is banded or takes the form of a band plus several columns are considered. Algorithms using Givcns plane rotations to exploit these structures ...
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Methods for linear least squares problems in which the observation matrix is banded or takes the form of a band plus several columns are considered. Algorithms using Givens plane rotations to exploit these structures ...
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Methods for linear least squares problems in which the observation matrix is banded or takes the form of a band plus several columns are considered. Algorithms using Givens plane rotations to exploit these structures are presented and some applications discussed. The level of presentation is predominantly expository.
Practical formulae are developed for using implicit Runge-Kutta formulae to solve initial-value problems in ordinary differential equations. The region of absolute stability of the method is maximal and the resulting ...
EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from r...
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EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners
This paper offers an extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence (AI) in healthcare, namely uncertainty, focusing on the large panoply of recent studies address...
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This paper offers an extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence (AI) in healthcare, namely uncertainty, focusing on the large panoply of recent studies addressing the connection between uncertainty, AI, and healthcare. The concept of uncertainty is a recurring theme across multiple disciplines, with varying focuses and approaches. Here, we focus on the diverse nature of uncertainty in medical applications, emphasizing the importance of quantifying uncertainty in model predictions and its advantages in specific clinical settings. Questions that emerge in this context range from the guidelines for AI integration in the healthcare domain to the ethical deliberations and their compatibility with cutting-edge AI research. Together with a description of the main specific works in this context, we also discuss that, as medicine evolves and introduces novel sources of uncertainty, there is a need for more versatile uncertainty quantification methods to be developed collaboratively by researchers and healthcare professionals. Finally, we acknowledge the limitations of current uncertainty quantification methods in addressing the different facets of uncertainty within the medical domain. In particular, we identify from this survey a relative paucity of approaches that focus on the user’s perception of uncertainty and accordingly of trustworthiness.
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