Accurate and interpretable assessment of cognitive workload from EEG remains a central challenge in neuroergonomics and real-time human–machine interaction. To address the limitations of existing Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) approaches, particularly their instability, limited neuroscientific grounding, and sensitivity to amplitude fluctuations, this paper introduces Scale-Stabilized Relative Intrinsic Mode Energy (SS-RIME), a theoretically motivated and physiologically informed feature extraction framework. SS-RIME integrates instantaneous frequency stabilization to enforce a consistent oscillatory hierarchy across subjects, delta (1–4 Hz) and theta (4–7.5 Hz) spectral weighting based on established frontal-midline activity, and cross-IMF energy normalization to reduce amplitude-driven variability. Applied to 64-channel EEG recorded during N-back tasks, the proposed framework achieved high performance, outperforming both classical machine-learning baselines and deep learning models such as EEGNet, DeepConvNet, and ShallowConvNet. SS-RIME yielded accuracies of $99.12\pm 0.41\%$ (0 vs. 2-back), $97.84\pm 0.63\%$ (0 vs. 3-back), and $92.31\pm 1.12\%$ (2 vs. 3-back), demonstrating strong cross-subject generalization. Theta-dominant IMFs over frontal midline regions emerged as the most discriminative components, supporting the neuroscientific validity of the stabilized and spectrally weighted Hilbert–Huang representation. With an inference time below 20 ms per epoch, SS-RIME is computationally efficient and suitable for real-time neuroergonomics applications, providing a robust, explainable, and physiologically grounded solution for EEG-based cognitive workload decoding while addressing key methodological gaps in prior EMD/CEEMDAN and deep learning approaches.

This study introduces C-EMDNet, a nonlinear speech denoising approach that combines the adaptive decomposition capabilities of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep convolutional architecture operating directly in the time-intrinsic mode function (IMF) domain. Unlike conventional enhancement methods that rely on fixed time–frequency representations, such as the short-time Fourier transform (STFT), the proposed approach interprets CEEMDAN IMFs as a morphological latent space that captures the multi-scale structure of speech. A U-Net-like network was trained to estimate mode-wise masks, enabling selective noise suppression while preserving the harmonic and formant structures. Experiments on standard noisy speech datasets show that C-EMDNet outperforms classical denoising algorithms and competitive deep learning baselines. These results highlight the promise of nonlinear morphological representations for an alternative framework speech enhancement.

This work introduces a fully adaptive speech denoising framework grounded in Empirical Mode Decomposition (EMD), designed to operate directly on the intrinsic time-frequency structure of the signal. The noisy speech waveform is adaptively separated into a set of intrinsic oscillatory modes, known as Intrinsic Mode Functions (IMFs), through an iterative time-domain procedure referred to as the sifting process. The idea of the proposed approach repose to threshold the IMFs adaptively. The main innovation of this work lies in the introduction of a time-adaptive IMF thresholding strategy driven by frame-level a posteriori SNR estimation. Adaptive noise is calculated directly from the noisy signal without the need for speech quiet detection. The enhanced speech waveform is obtained by recombining all adaptively thresholded IMFs, ensuring that each mode contributes only its cleaned oscillatory content. Because it relies solely on the intrinsic decomposition provided by Empirical Mode Decomposition, the proposed denoising framework operates as a fully datadriven and modelfree method. The proposed method is evaluated across multiple input SNR conditions and benchmarked against four classical baselines: EMDShrinkage, which applies a universal threshold, EMD-Kaiser, Improved EMD and adaptive wavelet thresholding (WTthresholding). The effectiveness of the proposed enhancement framework is assessed using both the output SignaltoNoise Ratio (SNRout) and the Perceptual Evaluation of Speech Quality (PESQ) metricExtensive simulations show that the proposed method provides stronger noise suppression and consistently outperforms other denoising methods, yielding higher SNR improvements and noticeably better perceptual quality. The present study focuses exclusively on speech signals degraded by additive white Gaussian noise.

Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to develop a reliable and efficient diagnostic framework that automates typhoid fever detection from clinical data while preserving patient privacy. Methods: To achieve this objective, we propose a hybrid framework combining genetic algorithm (GA)–based feature selection, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) deep learning classifier, and federated learning. The GA identifies the most informative clinical features, reducing redundancy and computational complexity. The selected features are then used to train a CNN–LSTM model in a federated learning setup using the Federated Averaging (FedAvg) algorithm, enabling collaborative model training across multiple clients without sharing raw patient data. Results: Experimental results show that the proposed framework achieves 92% accuracy, with a strong F1-score and satisfactory sensitivity. Compared to models trained on the full feature set, the proposed approach requires less memory and shorter training time, while maintaining balanced performance under class imbalance. Conclusions: These results demonstrate that integrating evolutionary feature selection, deep sequential learning, and federated training provides an effective and privacy-aware solution for multi-class typhoid fever diagnosis. The proposed framework is particularly suitable for clinical environments with limited data access and constrained resources.

Epileptic seizure detection from electroencephalography (EEG) remains challenging due to the non-stationary, multi-channel, and patient-specific nature of ictal activity. Handcrafted spectral features and fixed-basis wavelet transforms fail to fully capture the irregular, multi-scale oscillatory patterns that characterize seizure onset, while wavelet scattering transforms, although stable to time-warping deformations, are typically applied directly to the raw multi-channel signal, where seizure-related activity remains spectrally entangled with background brain activity and inter-channel redundancy is not explicitly modeled. This paper introduces an attention-guided Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)–wavelet scattering framework that first decomposes each EEG channel into intrinsic mode functions (IMFs), applies a wavelet scattering network independently to each retained mode, and aggregates the resulting representations through a dual-level learned attention mechanism operating jointly over intrinsic mode functions and EEG channels. Our approach achieves an overall accuracy of 99.68% and an F1-score of 98.91% on the CHB-MIT dataset, with a cross-dataset generalization accuracy of 99.10% when training on CHB-MIT and testing on the Bonn University dataset. Robustness experiments further show that performance remains above 96% accuracy under a 50% channel reduction and above 95% accuracy under moderate additive noise.

Transformer-based analysis of physiological signals critically depends on how input tokens are constructed, yet conventional tokens derived from raw temporal windows or spectrogram patches may overlook the intrinsic oscillatory organization of electroencephalographic (EEG) activity. To address this limitation, we propose AMST-Former, an adaptive mode-wise synchrosqueezing Transformer for patient-independent epileptic seizure detection. Each EEG channel is first decomposed into oscillatory modes using Adaptive Local Iterative Filtering (ALIF), after which the Synchrosqueezing Transform (SST) is applied independently to each mode. This decomposition-before-synchrosqueezing strategy reduces cross-component interference in instantaneous-frequency estimation while preserving mode-specific time–frequency dynamics. The resulting time–frequency patches are embedded together with channel, mode-frequency, and positional information. A hierarchical factorized-attention mechanism then models token relevance across time–frequency regions, oscillatory modes, and EEG channels before a Transformer encoder captures their interactions. Evaluation using a repeated patient-independent five-fold cross-validation protocol on the CHB-MIT database (22 subjects) yields an F1-score of 70.76% and an area under the precision–recall curve (AUPRC) of 0.870. At the event level, AMST-Former achieves a sensitivity of 81.4% at an average false detection rate of 1.39 false detections per 24 h. Ablation experiments show that mode-wise SST provides more effective representations than direct SST, per-mode Hilbert spectra, and fixed filter-bank decomposition. Synthetic experiments confirm the cross-component bias predicted by the analysis and shows that the SST of a mode remains accurate when separation is imperfect, whereas its Hilbert instantaneous frequency does not. Furthermore, the factorized-attention mechanism provides mode, time–frequency, and channel-level explanations, whose faithfulness is assessed through a deletion analysis. These results support adaptive mode-wise time–frequency tokenization as a promising strategy for interpretable, patient-independent EEG seizure detection.