Early and correct classification of neurodegenerative diseases like Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is one of the most important challenges in clinical neurology. In this paper, we present a novel electroencephalogram (EEG)-based approach that integrates a rich set of multiresolution features to improve the performance of automatic classification. Our approach fuses the Graph Fourier Transform (GFT), Graph Wavelet Transform (GWT), Discrete Wavelet Transform (DWT), and a newly developed Graph Empirical Mode Decomposition (GEMD) technique to primarily boost the performance of the proposed model. This also retained the complementary spatial, spectral, and temporal information carried by the EEG signals, which are significant for the differentiation of AD, FTD, and HC subjects. The EEG recordings were segmented into fixed lengths with non-overlapping windows of four durations: 1000, 5000, 10,000, and 20,000 samples. Energy and entropy features were obtained for each segment, both individually within domains and combined into a single 388-dimensional feature vector. The features were then normalized and fed into various machine learning (ML) models, including support vector machines (SVMs), k-nearest neighbors (kNNs), decision trees (DTs), random forests (RFs), and an ensemble learning model with the AdaBoost capability. The proposed model was tested using accuracy, precision, recall, specificity, and F1-scores, with results showing that the ensemble model was better than the other benchmark models in every classification task. That is, in this binary classification problem, an accuracy of 98.84% for AD vs. HC, 98.67% for AD vs. FTD, and 98.94% for FTD vs. HC was obtained. In the multiclass task (AD, FTD, HC), the method reached 96.68% accuracy, demonstrating the efficacy of the proposed method for the identification of Alzheimer's disease and frontotemporal dementia. Compared to previous research using the same dataset, our approach has demonstrated improved performance, validating the effectiveness of graph-based multiresolution feature fusion for dementia classification using EEG signals.

Artificial intelligence and machine learning applications in ambulatory surgery - A systematic review.

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• Proposed MVIC-Net which unifies four complementary ECG representations for robust sleep apnea detection. • Incorporated CINet architecture as a component of MVIC-Net that advances image-based ECG analysis via hierarchical interactive learning. • XAI ensures clinical transparency of CINet, aligning model decisions with physiologically meaningful signal regions. • Proposed MVIC-Net achieves 99.25% accuracy, setting a new benchmark for single-lead ECG-based apnea screening. • Multi-view fusion of MVIC-Net surpasses single-view and ensemble models; advantages confirmed statistically. • McNemar’s test highlights the significance of MVIC-Net’s performance improvements. • Results emphasize enhanced interpretability and generalizability for real-world clinical deployment. Deep learning models, particularly Convolutional Neural Networks (CNNs), have advanced automated Sleep Apnea (SA) detection from single-lead Electrocardiogram (ECG) signals. However, current approaches often face two limitations: (1) reliance on single-view representations (e.g., time-series or one image type) that can fail to capture the diverse feature set inherent in complex ECG dynamics; and (2) image-based methods that frequently employ standard architectures which may not optimally extract intricate local patterns crucial for SA identification. To address these challenges, we propose MVIC-Net, a Multi-View Interactive Convolutional Network framework that uniquely processes four complementary ECG representations simultaneously: 1D numeric data, 2D reshaped numeric data, Continuous Wavelet Transform (CWT) images, and circular plot images. We introduce the Convolutional and Interactive Learning Neural Network (CINet), an architecture inspired by SCINet and specifically engineered for enhanced ECG image feature extraction. CINet utilizes a hierarchical downsampling and interactive learning mechanism: it recursively splits feature maps, processes subsequences through distinct convolutional filters, and interactively combines them to mitigate information loss while capturing multi-resolution features effectively. Our MVIC-Net model integrates pre-trained CINet and standard CNN encoders, fusing their outputs via concatenation before final classification. Empirical validation on the Apnea-ECG database demonstrates superior performance, achieving 99.25% accuracy. This significantly surpasses individual-view models (with CINet reaching 96.32% accuracy on image-based views), pre-trained ResNet152V2 baselines, and a multi-model ensemble approach. To enhance clinical trust, we incorporate Explainable AI (XAI) via Grad-CAM, providing transparency into CINet’s decision-making process. Our results establish the effectiveness of combining multi-view learning with interactive convolutional architectures for robust physiological signal classification.

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From empirical formulas to machine learning: A comprehensive review of detonation velocity prediction for advanced energetic materials

Predicting blue carbon sequestration in Sundarban coastal mangroves: A spatially explicit approach with INVEST and machine learning to advance climate resilience and UN SDG-aligned nature-based climate solutions.

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