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        <title>Frontiers in Signal Processing | Biomedical Signal Processing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/signal-processing/sections/biomedical-signal-processing</link>
        <description>RSS Feed for Biomedical Signal Processing section in the Frontiers in Signal Processing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-15T03:37:08.146+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1893420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1893420</link>
        <title><![CDATA[An explainable AI framework integrating deep learning and large language model for student’s mental health]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rupali D. Kasar</author><author>Garima Shukla</author>
        <description><![CDATA[Mental health disorders such as anxiety, depression, and Mild Cognitive Impairment (MCI) are increasingly prevalent among young adults aged 18–30, significantly affecting academic performance, cognitive functioning, and overall wellbeing. Traditional diagnostic approaches depend on subjective assessments and limited clinical observations, making early and accurate detection challenging. To address these limitations, this research proposes an interpretable deep learning-based multimodal method for comprehensive mental health prediction and personalized intervention. The framework integrates heterogeneous data sources, including demographic, cognitive, behavioral, physiological, and neurocognitive indicators collected from clinical settings. Data preprocessing includes imputation, normalization, encoding, and text transformation. A Cross-Directional Feature Learning Network (CDFLN) is employed for robust multimodal feature extraction, followed by a Multi-model Progressive Dense Self-Attention for Cross Domain (MPDSA-CD) architecture for classification of anxiety, depression, and MCI, along with cognitive risk and severity assessment. Model performance is further enhanced by the Starfish Optimization Algorithm for hyperparameter tuning and parameter refinement. To ensure clinical transparency, SHapley Additive exPlanations (SHAP) are utilized to interpret model predictions and identify key risk factors influencing mental health outcomes. The proposed method achieves an accuracy of 99.8%, precision of 99.7%, recall of 99.9%, and F1-score of 99.8%, demonstrating strong robustness, generalization ability, and clinical applicability for early detection and effective psychological intervention in young adults.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1769553</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1769553</link>
        <title><![CDATA[Identification of eye diseases with small fundus image datasets using a new unsupervised deep learning approach]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Al Rafi Aurnob</author><author>Sharia Arfin Tanim</author><author>Farook Sattar</author>
        <description><![CDATA[Ocular diseases are a severe global problem, particularly in countries that lack the technology or economy to treat them. Automated analysis of retinal fundus images could enable early screening, but supervised deep learning methods require large, annotated datasets that are expensive and time-consuming to obtain. This study introduces an unsupervised deep learning framework for learning and clustering of retinal image representation that achieves strong performance on small-scale fundus image datasets using only image-level disease labels to guide contrastive pair construction and classification loss, without requiring pixel-level annotations for clustering. The proposed method, NF-HAE-UDL (normalizing flow-guided hybrid autoencoder-based unsupervised deep learning), combines a hybrid autoencoder architecture (ResNet-50 and Swin Transformer) with normalizing flow and contrastive learning to learn discriminative latent representations. The autoencoder generates meaningful feature representations, upon which the normalizing flow maps the encoded features to a more expressive and structured latent space. Meanwhile, contrastive learning encourages the separation of patterns targeted to specific diseases through data augmentation. Evaluation on the STARE dataset (81 images, multi-label) and the IDRiD subset (211 images, binary) demonstrates superior unsupervised clustering performance compared to traditional methods, achieving ARI scores of 0.58±0.10 on STARE and 0.58±0.06 on IDRiD. Auxiliary classification validation on frozen encoder representations confirms that learned embeddings preserve disease discriminative information (88% in-sample accuracy on STARE, 98% on IDRiD), though these results represent quality assessment rather than diagnostic performance. Furthermore, Grad-CAM visualizations validated against expert annotations (IoU =0.52±0.11) highlight clinically meaningful attention to pathological features. The framework demonstrates a promising direction for effective, interpretable, unsupervised retinal image analysis with limited data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1853106</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1853106</link>
        <title><![CDATA[An explainable AI-driven hybrid SE-transformer architecture for robust knee osteoporosis classification from X-ray data]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sudhir Kumar Sangula</author><author>Siddique Ibrahim S. P.</author>
        <description><![CDATA[BackgroundEarly and accurate classification of knee osteoporosis from radiographic images is crucial for timely diagnosis and treatment, yet existing deep learning models often lack both contextual understanding and interpretability.ObjectivesTo develop an explainable hybrid deep learning framework that combines Squeeze-and-Excitation (SE) networks, Transformer architecture, BiLSTM, and BiGRU for improved knee osteoporosis classification.MethodsThe proposed model integrates SE blocks for channel-wise feature enhancement, a Transformer for capturing long-range spatial dependencies, and BiLSTM/BiGRU layers for sequential feature learning. Grad-CAM is employed to provide visual explanations of model predictions. Performance is evaluated using a stratified 70:20:10 training, validation, and testing split.ResultsThe framework achieved an accuracy of 91.0%, precision of 91.0%, recall of 90.2%, and an F1-score of 90.4% on the test set. The reported results are based on a single-dataset evaluation and comparisons under identical experimental conditions.ConclusionThe proposed SE-Transformer–BiLSTM–BiGRU framework delivers accurate and interpretable knee osteoporosis classification, demonstrating its potential for computer-aided diagnosis while warranting further validation on external datasets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1831207</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1831207</link>
        <title><![CDATA[Detection of bacterial and viral pneumonia in pediatric chest radiographs using fusion of mediastinum and lung imaging biomarkers]]></title>
        <pubdate>2026-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sukanta Kumar Tulo</author><author>Shilpi Ruchi Kerketta</author><author>O. Rahul Manohar</author><author>Pramod Martha</author>
        <description><![CDATA[IntroductionDiagnosis of pneumonia in pediatric patients remains challenging due to the similarity of manifestations across different pneumonia types. Evaluation of variations in two clinically significant regions, the lung and the mediastinum, on chest radiographs could assist in accurate disease identification. In this work, the morphological characteristics of the mediastinum and lungs are analyzed, and multiple wrapper-based biomarker fusion techniques are employed to enhance the differentiation of bacterial and viral pneumonia.MethodsThe pediatric radiographic images are acquired from a publicly accessible dataset. A hybrid segmentation model combining edge and region-based level set techniques is employed to segment the lungs and mediastinum. Furthermore, morphological imaging biomarkers such as geometric and Hu moments are extracted from the segmented masks and statistically analyzed. Multiple wrapper-based biomarker fusion methods are implemented using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms. Further, the optimal fused imaging biomarkers are fed to LDA and SVM classifiers to differentiate the conditions.Results and DiscussionResults indicate that the employed hybrid model could segment both lungs and mediastinum regions from raw radiographic images. The extracted imaging biomarkers effectively characterize the morphological variations. In bacterial pneumonia, the mean lung area is reduced, whereas the mean mediastinum area is increased compared to viral pneumonia. The SVM classifier provided better F-measures of 75.5%, 81.3%, and 82.9% to differentiate bacterial and viral pneumonia using individual mediastinum, lung, and fused biomarkers, respectively, compared to the LDA classifier. Further, enhanced F-measures of 76.5%, 82.0%, and 87.3% are obtained using the LDA-based wrapper selected mediastinum, lung, and fused biomarkers, respectively. The findings indicate that fusion of imaging biomarkers from the lung and mediastinum regions achieves better performance than individual biomarkers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1812987</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1812987</link>
        <title><![CDATA[InvZW: invariant feature learning via noise-adversarial training for robust image zero-watermarking]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdullah All Tanvir</author><author>Frank Y. Shih</author><author>Xin Zhong</author>
        <description><![CDATA[This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning. As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference signature through optimization in the feature space. The proposed framework consists of two key modules. In the first module, a feature extractor is trained via noise-adversarial learning to generate representations that are both invariant to distortions and semantically expressive. This is achieved by combining adversarial supervision against a distortion discriminator and a reconstruction constraint to retain image content. In the second module, we design a learning-based multibit zero-watermarking scheme where the trained invariant features are projected onto a set of trainable reference codes optimized to match a target binary message. Extensive experiments on diverse image datasets and a wide range of distortions show that our method achieves state-of-the-art robustness in both feature stability and watermark recovery. Comparative evaluations against existing self-supervised and deep watermarking techniques further highlight the superiority of our framework in generalization and robustness.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1797749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1797749</link>
        <title><![CDATA[Explainable artificial intelligence approaches in cardiovascular imaging: methodological advances and clinical implications]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Wentao Yan</author><author>Rui Sun</author><author>Li Shen</author>
        <description><![CDATA[Cardiovascular diseases remain the leading cause of mortality worldwide, making accurate and efficient imaging-based diagnosis indispensable. Modern modalities such as Coronary Computed Tomography Angiography, Cardiac Magnetic Resonance, Echocardiography, and Chest X-Ray enable rich structural and functional assessment; however, the rapid growth of imaging data strains traditional analysis. Deep learning has markedly improved performance across cardiovascular imaging tasks, yet its “black box” nature limits interpretability, clinician trust, and clinical adoption. eXplainable Artificial Intelligence (XAI) addresses this gap by exposing the decision logic of models in human-understandable forms. This review provides a structured synthesis of recent progress in XAI for cardiovascular imaging. We outline the core principles of perturbation-based and backpropagation-based methods, and survey their applications across major modalities for disease characterization, lesion discrimination, and risk stratification. We further analyze current evaluation challenges and methodological limitations, and propose future directions toward robust, trustworthy, and clinically deployable XAI systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615</link>
        <title><![CDATA[Temporal convolutional network architectures: a novel simultaneous spatio-temporal model for comparative analysis]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Milad Jabbari</author><author>Eisa Aghchehli</author><author>Chenfei Ma</author><author>Kianoush Nazarpour</author>
        <description><![CDATA[IntroductionConventional temporal-based deep learning models often fail to extract inter- channel information from electromyographic (EMG) signals. Existing spatio-temporal approaches typically sequentially combine spatial and temporal networks, but this strategy increases model complexity and parameter count.MethodWe introduce a simultaneous spatio-temporal convolutional deep network, which integrates spatial and temporal feature extraction connections within a single, explainable deep network.ResultsTo evaluate the new architecture through a comprehensive comparative analysis, we compared its performance and model size with three other established decoding methods. We used two internal and two publicly available EMG databases. We report that the application of convolutional filters in both spatial and temporal directions simultaneously enhances myoelectric decoding accuracy. Finally, we explain the proposed model using the saliency maps method.DiscussionThe findings indicate that the proposed simultaneous spatio-temporal configuration offers reliable classification performance and is well-suited for real-time on-board deployment. The proposed model explains how simultaneous spatio-temporal convolution enhances the contribution of both temporal and spatial components of EMG activity, resulting in improved classification performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807</link>
        <title><![CDATA[Self-face viewing attenuates cardiac modulation of corticospinal excitability]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Milana Makarova</author><author>Nikita Fedosov</author><author>Irina Mikhailova</author><author>Maria Nikolaeva</author><author>Alexei Ossadtchi</author><author>Alexey Tumyalis</author><author>Maria Volodina</author>
        <description><![CDATA[IntroductionWhile self-referential attention is thought to enhance interoceptive sensitivity, its effect on cardiac modulation of corticospinal excitability remains unexplored. This pilot study investigated how viewing one’s own face (self-face processing) modulates the cardiac-phase coupling of motor output and whether this heart-brain coupling depends on interoceptive accuracy (heartbeat perception).MethodsIn 15 healthy adults, motor-evoked potentials (MEPs) were elicited via transcranial magnetic stimulation (TMS) at three fixed time points following the R-peak (0, 250, and 500 m) during presentation of either self-face or other-face pictures. A Modulation Index was derived from log-transformed MEPs to quantify cardiac-phase modulation strength. Interoceptive accuracy was assessed via a heartbeat-counting task.ResultsContrary to the hypothesis that self- face viewing would enhance cardiac–motor coupling through inward attentional focus, self-face processing significantly reduced the overall magnitude of cardiac-phase modulation. This attenuation was most pronounced at 0 m and 250 m post-R-peak, corresponding to systolic phase. Across conditions, higher interoceptive accuracy predicted stronger modulation, though this relationship showed a tendency toward attenuation during self-face viewing (interaction p = 0.059).DiscussionThe results of this pilot TMS study suggest that, in a task requiring explicit evaluation of facial stimuli, self-face viewing acts as a potent exteroceptive stimulus that diverts attention away from interoceptive signals, thereby weakening the cardiac-cycle influence on motor excitability. These findings highlight the context-dependency of self-processing effects and suggest a possible link between HCT-based interoceptive accuracy and heart-brain- body coupling.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1715921</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1715921</link>
        <title><![CDATA[The role of signal preprocessing on the discriminability of canonical time-series characteristics and classification among individuals with and without Parkinson’s disease during serious game interaction]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maria Fernanda Soares de Almeida</author><author>Ariana Moura Cabral</author><author>Leandro Rodrigues da Silva Souza</author><author>Mila Figueira Nozella</author><author>Camille Marques Alves</author><author>Pedro Henrique Bernardes Caetano Milken</author><author>Maria Olivia Domingos Rezio Ramos</author><author>Luanne Cardoso Mendes</author><author>Adriano de Oliveira Andrade</author>
        <description><![CDATA[IntroductionOver the past decade, there has been a significant increase in studies using biomedical signals for objective monitoring of Parkinson’s disease (PD) motor symptoms. Inertial sensors are widely employed to record motion, producing time-series data that capture the underlying motor condition of patients. A major challenge in the field is classifying these signals to discriminate healthy subjects from PD individuals and distinguish motor conditions among patients. While many studies focus on feature classification, there is a lack of research on the influence of signal preprocessing.MethodsTo fill this gap, we evaluate data from healthy subjects and PD patients during interaction with the RehaBEElitation serious game. We employed the catch22 feature set to extract robust time-series characteristics. To evaluate the influence of preprocessing on classification between healthy individuals and patients in on and off medication states, four strategies were adopted.ResultsInitially, features extracted from raw data showed limited accuracy due to noise and voluntary movements. Subsequent interpolation to address discontinuities produced inconsistent results. The third strategy involved wavelet decomposition, which effectively mitigated trends and motion artifacts, resulting in a significant increase in accuracy across all models and confirming the vital role of sophisticated signal filtering. The fourth strategy combined interpolation and wavelet decomposition, achieving the best results with optimal separation (Accuracy = 100.0%) in binary classification and significant improvement in the multi-class problem.DiscussionOur findings establish that signal conditioning is pivotal for maximizing discriminative power. To further validate our findings, we benchmarked our pipeline against the RandOm Convolutional KErnel Transform (ROCKET) using a RidgeClassifierCV. The catch22 with Random Forest (RF) classifier, using a wavelet-based approach, achieved a balanced accuracy of 76.0% in the multiclass task, demonstrating superior performance compared to the ROCKETRidgeClassifierCV framework (69.0%) while maintaining a more compact and computationally efficient feature representation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1691777</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1691777</link>
        <title><![CDATA[EEG-based cognitive load estimation during the use of a virtual wheelchair simulator]]></title>
        <pubdate>2026-03-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Débora Pereira Salgado</author><author>Felipe Roque Martins</author><author>Angela Abreu Rosa de Sá</author><author>Ronan Flynnn</author><author>Niall Murray</author><author>Eduardo Lázaro Martins Naves</author>
        <description><![CDATA[IntroductionDriving a powered wheelchair is a complex task that requires the integration of motor, visual, and cognitive skills. The development of assistive technologies without appropriate assessment methods that help bridge the gap between users and developers may lead to abandonment and reduced engagement. Most assessments rely on explicit measures, such as performance metrics, or subjective tools like interviews and questionnaires. In contrast, implicit measures allow continuous inference of mental states during task execution. This study proposes the use of blink indices derived from electroencephalographic (EEG) signals as implicit metrics to estimate cognitive load during the use of a virtual reality wheelchair training simulator.MethodsA total of 25 participants (14 females and 11 males; mean age 26.50 ± 5.7 years) completed a predefined route using a virtual wheelchair simulator. Blink parameters, including frequency, duration, and velocity, were extracted from EEG signals during task performance. After completing the simulation, participants responded to the NASA Task Load Index (NASA-TLX) to assess subjective cognitive load, as well as the System Usability Scale (SUS) and the Igroup Presence Questionnaire (IPQ).ResultsThe findings showed that higher mental-visual demand was associated with decreases in blink frequency, duration, and velocity. Correlation analyses between NASA-TLX scores and blink parameters revealed weak to moderate associations. These results suggest partial convergence between subjective and physiological measures of cognitive load.DiscussionBlink-based indices derived from EEG signals provide relevant information regarding cognitive demand during wheelchair simulator use. However, blink parameters alone are insufficient to reliably infer cognitive load. When combined with subjective questionnaires, implicit physiological metrics may offer a more comprehensive assessment than questionnaires alone, supporting the development and refinement of assistive training technologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1745291</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1745291</link>
        <title><![CDATA[Equation-level parameterized fusion reformulation for multimodal epileptic seizure detection using interaction control and data-quality screening]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdul-Mumin Khalid</author><author>Musah Sulemana</author><author>Iddrisu Wahab Abdul</author>
        <description><![CDATA[Epileptic seizure detection remains challenging due to noise, inter-subject variability, and the poor generalization ability of unimodal learning models. To address these limitations, this study proposes an equation-level multimodal fusion reformulation for epileptic seizure detection that integrates EEG, ECG, EMG, and ACC signals using adaptive parameterized fusion and interaction control. The framework introduces four interpretable parameters: a fusion exponent (ρ), an interaction weight (δ), a stabilization factor λ, and a synergy amplifier η, which jointly regulate modality contribution, nonlinear cross-modal interaction, numerical stability, and synergistic enhancement within a unified mathematical formulation applicable to both traditional and deep learning models. The study is conducted on a multimodal dataset comprising recordings from 120 clinically diagnosed epilepsy patients, including 60 patients from Tamale Teaching Hospital and 60 from publicly available datasets. Signals were sampled at 512 Hz and segmented into 2-second windows with 50% overlap, yielding approximately 1,024,000 labeled samples. A formal Data Quality Assurance (DQA) model and a Novel Cosine Similarity (NCS) index were employed to assess signal reliability and cross-source alignment prior to fusion. Twelve machine learning and deep learning classifiers were evaluated using a strict patient-wise data split to prevent data leakage. Experimental results demonstrate consistent performance improvements across all models following equation-level reformulation. Traditional machine learning models improved from baseline accuracies of approximately 55–67% to 82–92%, while deep learning models improved from 70–82% to 89–97.9%, with the Transformer-based model achieving the highest performance. These results confirm that equation-level multimodal fusion provides a generalizable, interpretable, and computationally efficient approach for robust epileptic seizure detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1680796</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1680796</link>
        <title><![CDATA[Alzheimer’s detection using discrete wavelet transform based image fusion and vision information transformer]]></title>
        <pubdate>2026-03-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amar A. Dum</author><author>Kshama V. Kulhalli</author><author>Priyanka Singh</author>
        <description><![CDATA[Alzheimer’s disease (AD) is the most prevalent form of dementia and a major cause of mortality among older adults. Magnetic resonance imaging (MRI) and positron emission tomography (PET) are commonly used for AD diagnosis. Despite extensive research, the accuracy of automated detection methods remains limited. This study proposes a highly accurate AD classification model by integrating complementary information from MRI and PET scans. The images are fused using a discrete wavelet transform (DWT), augmented, and subsequently classified using a Vision Transformer (ViT). Comprehensive evaluation across nine performance metrics shows that the proposed ViT-based framework achieves 97.68\% accuracy, surpassing benchmark transfer learning models and state-of-the-art methods. Ablation studies and comparative analysis further confirm the robustness and reliability of the proposed approach for AD detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1715540</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1715540</link>
        <title><![CDATA[Singular spectrum analysis of near-infrared spectroscopy signal classification for mental arithmetic and rest state]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kanwardeep Singh Gahlot</author><author>Anukul Pandey</author><author>Sachin Taran</author><author>Rahul Thakur</author>
        <description><![CDATA[The brain–computer interface (BCI) is the connection between the human brain and computers, creating a bridge that mimics the human brain. The premise behind near-infrared spectroscopy (NIRS) is that increased oxygen consumption in the brain leads to increased blood flow due to nerve connections. NIRS is a non-invasive procedure; changes in oxyhemoglobin (Oxy-Hb) and deoxyhemoglobin (Deoxy-Hb) parameters can be easily utilized to detect brain hemodynamics. This study is based on the Oxy-Hb parameter to classify mental arithmetic and rest states of the brain using singular spectrum analysis (SSA). SSA results in a better-denoised signal and decomposition into different principal components for analysis of these states. Oxy- and Deoxy-Hb patterns are temporary and unstable, so features such as power bandwidth, entropy, and complexity were extracted for classification. The reported accuracy in existing methods is 79.4% for the antagonistic single-trial classification and 86.9% for graph NIRS methods. The present study’s mean accuracy was 98.4% based on a set of selected features using filtering detrending (FD)-SSA, thus reducing the cost of poor sorting. Finally, classification models were evaluated based on scores such as Matthew’s correlation coefficient, precision, F1-score, and recall, resulting in 0.889, 0.968, 0.966, and 0.963, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1724468</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1724468</link>
        <title><![CDATA[Research on heart rate estimation algorithm based on dynamic PPG]]></title>
        <pubdate>2026-02-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiawei Guo</author><author>Shiyuan Chen</author><author>Ting Lan</author><author>Ruochen Li</author><author>Lichao Wang</author><author>Yunchong Wu</author><author>Jun Zhong</author><author>Wei Zhu</author>
        <description><![CDATA[Heart rate is one of the most vital physiological parameters and is clinically widely used to assess human health status. In recent years, wearable devices based on photoplethysmography (PPG) have been extensively applied in real-time monitoring. However, PPG signals are susceptible to interference from various types of noise during acquisition, particularly motion artifacts (MA), which pose a significant challenge to the accurate extraction of physiological parameters. This study focuses on heart rate extraction from dynamic PPG signals and explores denoising methods combining traditional signal processing and machine learning techniques. The main research contents of this paper are as follows: further improvements are made on the basis of existing algorithms by integrating support vector machines (SVM). A more comprehensive signal quality assessment is performed via SVM, which incorporates the time-domain and frequency?domain statistical characteristics of both PPG signals and triaxial acceleration (ACC) signals. In addition, the short-time Fourier transform (STFT) is integrated to capture time-varying characteristics, thereby mitigating the impact of local signal quality degradation on the analysis of full-window signals. For spectral peak tracking, a Gaussian window is adopted to optimize the spectral search range and a comprehensive analysis is conducted by fusing spectral amplitude information with historical heart rate data. Experimental results demonstrate that the heart rate error of the test set is 1.71 beats per minute (BPM).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1700044</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1700044</link>
        <title><![CDATA[Comparing compressive sensing and downsampling for COVID-19 diagnosis from cough and speech audio signals]]></title>
        <pubdate>2026-01-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Leticia Silva</author><author>Alan Floriano</author><author>Carlos Valadão</author><author>Eliete Caldeira</author><author>Sridhar Krishnan</author><author>Teodiano Bastos Filho</author>
        <description><![CDATA[IntroductionSince the onset of the COVID-19 pandemic, extensive research has focused on developing non-invasive diagnostic approaches of respiratory syndrome using biomedical signals, particularly cough and speech audio. Time-frequency representations combined with Machine Learning models have shown potential in identifying acoustic biomarkers associated with respiratory conditions. Although many existing approaches demonstrate high performance, their use may be limited in resource-constrained environments due to processing or implementation demands.MethodsIn this study, we propose an end-to-end approach for COVID-19 inference based on compressed time-domain audio signals. The method combines temporal signal compression strategies - Downsampling (DS) and Compressive Sensing (CS) - with a Convolutional Neural Network (CNN) trained directly on the waveforms. This design eliminates the need for handcrafted features or spectrograms, aiming to reduce computational complexity while preserving classification performance.ResultsTo evaluate the proposed structure, we used data from two open-access datasets, one for coughing and one for speech. Experimental results, assessed using accuracy and F1-score metrics, indicate that CS outperformed DS in most scenarios, particularly under high compression rates (e.g., 200 Hz and 100 Hz).DiscussionThese findings support the use of compressed audio-based classification in real-world embedded and mobile health systems, where computational efficiency is essential.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1707422</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1707422</link>
        <title><![CDATA[Unraveling cardiac arrhythmia frequency: comparative analysis using time and frequency domain algorithms]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Laura Diaz-Maue</author><author>Annette Witt</author><author>Lina Elshareif </author><author>Holger Nobach</author>
        <description><![CDATA[During cardiac arrhythmia, the heart frequency is an important physiological parameter that can be identified by analyzing electrocardiogram (ECG) signals. However, the accuracy of the frequency estimation becomes increasingly challenging as the ECG morphology becomes more complex, for example, during transitions from tachycardia to fibrillation. In this paper, the authors compare seven conventional and novel time- and frequency-domain methods for cardiac arrhythmia frequency analysis, including an algorithm used in implantable cardioverter defibrillators. The objective of this study is to identify the approaches that reveal the potential presence of a dominant frequency and its role in characterizing different arrhythmia types. By evaluating the strengths and weaknesses of each method, the authors aim to establish an informative framework for extracting meaningful insights from electrocardiogram data in the context of cardiac arrhythmia frequency. In order to ascertain the statistical relevance of the methods, a dataset comprising 112 ECGs from arrhythmic murine hearts was analyzed. Additionally, a dataset comprising human arrhythmia data was examined to validate the techniques presented. The R-library, which contains the frequency determination algorithms, as well as the murine data set, is made available to the reader for the purposes of further testing and supplementation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1679555</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1679555</link>
        <title><![CDATA[A computational approach for prediction of exons using static encoding methods, digital filter and windowing technique]]></title>
        <pubdate>2025-11-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shaik Benarjee</author><author>Vaegae Naveen Kumar</author>
        <description><![CDATA[IntroductionIdentifying protein-coding regions in eukaryotic Deoxyribonucleic acid (DNA) remains difficult due to the sparse and uneven distribution of exons.MethodsThis work focusses into four static encoding schemes—integer, Voss, paired numeric, and Electron-Ion Interaction Potential (EIIP) to improve exon prediction using genomic signal processing. Two benchmark sequences, Caenorhabditis elegans Cosmid F56F11.4 and Mouse apolipoprotein A-IV (M13966.1), were analyzed in MATLAB. A Cauer (elliptic) band-pass filter was used to isolate the period-3 component, and a Blackman-Harris window was utilised to reduce spectral leakage. The elliptic filter in conjunction with EIIP-based encoding achieved the most distinct separation between coding and non-coding areas among the assessed techniques, identifying every exon segment with a minimal amount of noise.Results and discussionThe technique obtained 84% sensitivity, 96% specificity, and 94% accuracy on the C. elegans Cosmid sequence and 86.5% sensitivity, 93% specificity, and 91% accuracy on the M13966.1 gene sequence.ConclusionThese results show that the EIIP, Cauer filter and Blackman-Harris windowing framework offers a reliable and effective method for identifying exons.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2025.1555876</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2025.1555876</link>
        <title><![CDATA[Editorial: Smart biomedical signal analysis with machine intelligence]]></title>
        <pubdate>2025-01-31T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Tilendra Choudhary</author><author>Pulakesh Upadhyaya</author><author>Mohammad Zavid Parvez</author><author>Shaik Rafi Ahamed</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1479244</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1479244</link>
        <title><![CDATA[Markerless vision-based knee osteoarthritis classification using machine learning and gait videos]]></title>
        <pubdate>2024-11-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Slim Ben Hassine</author><author>Ala Balti</author><author>Sabeur Abid</author><author>Mohamed Moncef Ben Khelifa</author><author>Mounir Sayadi</author>
        <description><![CDATA[IntroductionKnee osteoarthritis (KOA) is a major health issue affecting millions worldwide. This study employs machine learning algorithms to analyze human gait using kinematic data, aiming to enhance the diagnosis and detection of KOA. By adopting this approach, we contribute to the development of an effective diagnostic methods for KOA, a prevalent joint condition.MethodsThe methodology is structured around several critical steps to optimize the model’s performance. These steps include extracting kinematic features from video data to capture essential gait dynamics, applying data filtering and reduction techniques to remove noise and enhance data quality, and calculating key gait parameters to boost the model’s predictive power. The machine learning model trains on these refined features, validates through cross-validation for robust performance assessment, and tests on unseen data to ensure generalizability.ResultsOur approach demonstrates significant improvements in classification accuracy, highlighting its potential for early and precise KOA detection. The model achieves a high classification accuracy, indicating its effectiveness in distinguishing KOA-related gait patterns.DiscussionFurthermore, a comparative analysis with another model trained on the same dataset demonstrates the superiority of our method, suggesting that the proposed approach serves as a reliable tool for early KOA detection and potentially improves clinical diagnostic workflows.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2024.1496320</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2024.1496320</link>
        <title><![CDATA[Corrigendum: Editorial: Physiological signal processing for wellness]]></title>
        <pubdate>2024-10-15T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Rakesh Chandra Joshi</author><author>Navchetan Awasthi</author><author>Priyadarsan Parida</author><author>Manob Jyoti Saikia</author>
        <description></description>
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