Biomedical Signal Processing and Control最新文献

{"title":"Rapid auscultation techniques for acute heart failure in ambulance scenarios","authors":"Hui Yu ,&nbsp;Zhaoyu Qiu ,&nbsp;Zhigang Li ,&nbsp;Jinglai Sun ,&nbsp;Guangpu Wang ,&nbsp;Xin Chen ,&nbsp;Jing Zhao ,&nbsp;Shuo Wang","doi":"10.1016/j.bspc.2025.108730","DOIUrl":"10.1016/j.bspc.2025.108730","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Acute Heart Failure (AHF) leads to over 26 million hospital admissions worldwide annually, imposing a significant healthcare burden. Current diagnostic methods based on biochemical markers and echocardiography often require more than 20 min, limiting their applicability in time-critical emergency scenarios. Auscultation, a rapid and non-invasive practice, provides complementary information to the clinical gold standard. To address the need for rapid AHF diagnosis, this study proposes a feature extraction and diagnostic framework using short heart sound recordings.</div></div><div><h3>Methods:</h3><div>Discrete wavelet transform was employed for heart sound denoising, and Mel Frequency Cepstral Coefficients (MFCCs) were used for feature extraction. A lightweight DenseHF-Net with 0.33M parameters was developed for heart failure diagnosis. Two auscultation strategies were designed and evaluated: Multi-region fusion auscultation (mitral, aortic, and pulmonic valves) and Mitral valve auscultation.</div></div><div><h3>Results:</h3><div>We established an auscultation dataset comprising 2,999 recordings with detailed clinical annotations. The enhanced wavelet-based denoising method increased the average signal-to-noise ratio to 7.8 dB. Using DenseHF-Net, Multi-region fusion auscultation achieved an average accuracy of 99.25%, whereas Mitral valve auscultation reached 92.60%.</div></div><div><h3>Conclusions:</h3><div>The proposed framework enables rapid AHF diagnosis from 3-second auscultation recordings. Multi-region fusion auscultation achieves the highest accuracy, while Mitral valve auscultation balances efficiency and hardware simplicity, making it suitable for ambulances and wards. With its lightweight design, the framework is deployable on edge devices. Future work will include multi-center validation, prospective testing, and regulatory compliance. Data and codes are available at:<span><span>https://github.com/qiuzhaoyu/AHF-Rapid-Diagnosis</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108730"},"PeriodicalIF":4.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FE-MAANet: A frequency-enhanced multi-scale adaptive attention network for medical image segmentation","authors":"Ke Peng,&nbsp;Bi Hong,&nbsp;YiYang Liu,&nbsp;Chunyu Chen,&nbsp;Yu Pan,&nbsp;Yu Jiang,&nbsp;XiaoJuan Liu","doi":"10.1016/j.bspc.2025.108884","DOIUrl":"10.1016/j.bspc.2025.108884","url":null,"abstract":"<div><div>Medical image segmentation is vital for clinical diagnostic support. In recent years, convolutional neural networks (CNNs), particularly U-Net, have made significant advancements in this field. However, in most existing methods, the reliance on fixed-size convolution operations for feature extraction restricts their adaptability in capturing multi-scale features. Furthermore, these methods are also face challenges in effectively modeling global contextual information. To address these limitations, we propose a frequency-enhanced multi-scale adaptive attention network (FE-MAANet) based on a U-shaped architecture. Specifically, we propose a novel Multi-scale Adaptive Large Kernel (MSALK) module. MSALK extracts multi-scale features through cascaded depthwise separable convolutions of different types and sizes, along with a two-step feature calibration strategy to progressively integrate features from different receptive fields, thus optimizing feature representations and improving the model’s adaptability to multi-scale features. Moreover, we design a Frequency-Spatial Parallel Attention (FSPA) module integrated within the skip connections. FSPA adopts a dual-branch strategy to collaboratively leverage global information in the frequency domain and spatial detail information, avoiding the loss of local fine-grained details while enhancing the capability for global contextual modeling. We evaluate the effectiveness of our method on three challenging public datasets: the MICCAI 2015 Multi-Atlas Abdominal Labeling Challenge (Synapse) dataset, the Automated Cardiac Diagnosis (ACDC) dataset, and the Aortic Vessel Tracing (AVT) dataset. Extensive experiments demonstrate that, compared to previous state-of-the-art methods, our approach achieves superior segmentation performance with fewer parameters. Additionally,We conduct ablation studies validating significant segmentation improvement by optimizing two MSALK module key parameters (kernel size and dilation rate).</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"113 ","pages":"Article 108884"},"PeriodicalIF":4.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prior-enhanced attribute embedding for predicting chemoradiotherapy sensitivity in SNSCC patients","authors":"Zhihan Zuo ,&nbsp;Huatao Quan ,&nbsp;Li Yan ,&nbsp;Yuchun Fang","doi":"10.1016/j.bspc.2025.108802","DOIUrl":"10.1016/j.bspc.2025.108802","url":null,"abstract":"<div><div>Preoperative chemoradiotherapy plays a good role in organ preservation in sinonasal tumors, but some patients are not sensitive to chemoradiotherapy. Since the samples of sinonasal squamous cell carcinoma (SNSCC) are insufficient and unbalanced, it is challenging to establish a good model for sensitivity prediction. To solve this problem, this paper proposes an end-to-end Prior Enhancement framework based on Attribute Embedding (PEAE) to predict the sensitivity of SNSCC patients to chemoradiotherapy. The whole prediction task is divided into an image-wise task and a subject-wise task. PEAE is applied to the image-wise task, which can fully mine the prior imaging and non-imaging information in the existing data and can be easily embedded into the mainstream backbone network for end-to-end optimization. Specifically, we propose a multi-level attribute structure to express the prior information of images, which consists of general, spatial, and textual attributes. Furthermore, graph convolutional network is used to establish the relationship between images during prediction, where the adjacency matrix is obtained from the correlation of images calculated according to the multi-level attribute structure. In the subject-wise task, the prediction result of each subject is obtained by averaging the probability values obtained in the image-wise task that each image belongs to each category. The experimental results on SNSCC and an additional public dataset, ADNI-SEG, show that models with PEAE perform better than traditional neural networks. The accuracy, AUC and recall are improved by more than 10% on several mainstream networks.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108802"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LM3DFN: An end-to-end model for non-invasive prediction of EGFR mutation in non-small cell lung cancer","authors":"Hui Xie ,&nbsp;Yihuai Tang ,&nbsp;Hualong She ,&nbsp;Qing Li","doi":"10.1016/j.bspc.2025.108890","DOIUrl":"10.1016/j.bspc.2025.108890","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the feasibility of constructing a deep learning model based on chest CT images to predict epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC), providing an innovative solution for non-invasive molecular typing.</div></div><div><h3>Methods</h3><div>This study retrospectively included 623 pathologically confirmed NSCLC patients admitted to our hospital from January 2020 to December 2024 (EGFR mutant: 326 cases, 52.3 %; EGFR non-mutant: 297 cases, 47.7%). All cases had complete CT images and EGFR test results. A Lightweight Multimodal 3D Fusion Network (LM3DFN) deep learning framework was developed, incorporating an attention mechanism to enhance key regional image features and integrate critical imaging information. The dataset was randomly divided into a training set (467 cases) and a test set (156 cases) in a 3:1 ratio. Model performance was evaluated using multi-dimensional metrics, including accuracy (ACC), precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>The LM3DFN model demonstrated excellent predictive performance in the test set (ACC = 0.836[0.818–0.854], Precision = 0.825[0.813–0.863], Recall = 0.779[0.751–0.802], F1 = 0.801[0.772–0.834], AUC = 0.889[0.885–0.923]). Visualization of attention analysis indicated a correlation between EGFR mutations and tumor texture and grayscale.</div></div><div><h3>Conclusion</h3><div>This study confirmed that the LM3DFN model can effectively mine phenotypic features in CT images related to EGFR mutations, providing a non-invasive and reproducible alternative for molecular typing in clinical practice. This model is particularly suitable for dynamic monitoring of gene status evolution during targeted therapy, offering important technical support for the optimization and translational application of precision diagnosis and treatment systems for lung cancer.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108890"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tortuosity and discrete compactness biomarkers for machine learning-based classification of mild cognitive impairment","authors":"Didier Torres Guzmán ,&nbsp;José Daniel Pinzón Vivas ,&nbsp;Eduardo Barbará Morales","doi":"10.1016/j.bspc.2025.108848","DOIUrl":"10.1016/j.bspc.2025.108848","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to assess the effectiveness of tortuosity and discrete compactness metrics in analyzing the amygdala’s morphology to differentiate healthy control individuals from patients diagnosed with mild cognitive impairment using magnetic resonance imaging.</div></div><div><h3>Methods</h3><div>The analysis included a total of 74 participants, comprising 37 healthy control subjects and 37 mild cognitive impairment patients. Imaging data were sourced from the ADNI database. The amygdala regions (both hemispheres) were segmented, and measurements for volume, normalized volume, discrete compactness, and tortuosity were computed. Statistical tests and automatic classifier training of Support Vector Machines, K-nearest Neighbors, Randon Forest and Artificial Neural Network were conducted to identify significant group differences. The machine learning algorithms were trained with the proposed metrics with a partition of 60–40 subjects for training and testing. The training consisted of hyperparameter optimization with a 5-fold cross validation.</div></div><div><h3>Results</h3><div>The statistical analysis revealed significant differences (p &lt; 0.01) across all evaluated metrics, with the most pronounced alterations observed in discrete compactness and tortuosity within the right hemisphere.</div><div>The application of the previously described algorithms demonstrated that the proposed biomarkers—tortuosity and discrete compactness—offered greater discriminative power compared to traditional volume-based measures. When incorporated into the classification models, these features enhanced performance, yielding a test accuracy of 82.14 %, area under the curve values between 88.27 % and 91.33 %, and F-scores ranging from 81.48 % to 83.87 %. These findings underscore the potential of tortuosity and discrete compactness as sensitive and robust imaging biomarkers for the early detection of mild cognitive impairment.</div></div><div><h3>Conclusions</h3><div>These findings demonstrate that tortuosity and discrete compactness are more sensitive than conventional volume-based metrics in capturing morphological alterations of the amygdala in mild cognitive impairment. When integrated into machine learning models—Support Vector Machines, K-nearest Neighbors, Random Forest, and Artificial Neural Networks—these features enhanced classification performance, achieving a test accuracy of 82.14 %, area under the curve values between 88.27 % and 91.33 %, and F-scores ranging from 81.48 % to 83.87 %.</div></div><div><h3>Significance</h3><div>The results suggest that tortuosity and discrete compactness may serve as robust and informative imaging biomarkers for the early detection of mild cognitive impairment. Their ability to outperform traditional morphological metrics in both statistical discrimination and machine learning classification highlights their potential for clinical application in computer-aided diagnosis systems.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108848"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SwinEff-AttentionNet: a dual hybrid model for breast image segmentation and classification using multiple ultrasound modality","authors":"Iqra Nissar,&nbsp;Shahzad Alam,&nbsp;Sarfaraz Masood","doi":"10.1016/j.bspc.2025.108795","DOIUrl":"10.1016/j.bspc.2025.108795","url":null,"abstract":"<div><div>Breast cancer is the most prevalent malignancy among women globally, with early detection playing a pivotal role in improving survival rates. However, ultrasound image interpretation remains a challenge due to noise, indistinct lesion boundaries, and the need for skilled radiologists, especially in resource-limited settings. This research introduces <em>SwinEff-AttentionNet</em>, a novel hybrid deep learning framework combining Swin transformers, EfficientNet layers, and Efficient Local Self-Attention (ELSA) modules to enhance breast ultrasound image analysis. Utilizing hierarchical feature extraction, the proposed architecture excels in segmentation and classification tasks. It was evaluated on two benchmark datasets: BUSI and Breast-Lesions-USG. For classification, <em>SwinEff-AttentionNet</em> achieved an accuracy of 98.50% and 95.84% on the BUSI and Breast-Lesions-USG datasets, respectively, outperforming state-of-the-art models such as ViT, DeiT, PVT, CrossViT and CvT. Similarly, segmentation performance yielded Dice scores of 92% and 87.82%, IoU scores of 88.7% and 83%, and AUC values of 91.38% and 89.72% on the BUSI and Breast-Lesions-USG datasets, respectively, underscoring its robustness across diverse imaging conditions. The dual-task nature of <em>SwinEff-AttentionNet</em> demonstrates its versatility, offering clinicians a reliable tool for both lesion localization and diagnosis. This study highlights the potential of advanced hybrid architectures in addressing the limitations of traditional imaging frameworks, paving the way for improved diagnostic accuracy and clinical decision-making in breast cancer care.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108795"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"X-YOLO: A method for detecting wrist fractures in children based on dynamic feature enhancement and lightweight design","authors":"Haifeng Qiu ,&nbsp;Yong He","doi":"10.1016/j.bspc.2025.108874","DOIUrl":"10.1016/j.bspc.2025.108874","url":null,"abstract":"<div><h3>Objectives:</h3><div>Our goal here is to introduce a nifty, multi-task wrist fracture detection technique with a leaner footprint, leveraging an upgraded YOLO11s setup. This approach tackles the tricky balance between accuracy and efficiency in real-world medical settings.</div></div><div><h3>Methods:</h3><div>We have got our hands on the HGNetV2 module, which gets our backbone network trim and tight by cutting down on parameters and computational bloat. The DySample module joins the neck network to up our game in pinpointing tiny fractures and bone issues, all thanks to its dynamic sampling strategy that boosts multi-scale feature mapping. We swapped out the regular Conv blocks for GSConv in the neck network, which not only slashes the computational load but also keeps the info flowing smoothly. And to top it off, we have concocted the Focaler-CIoU loss function, a tweak on the Focaler-IoU that gives priority to different samples and sharpens the model’s learning across various scales.</div></div><div><h3>Results:</h3><div>We have tested our system on GRAZPEDWRI-DX and FracAtlas datasets, and the improvements are nothing short of spectacular. Our model now clocks in at a mere 7.0M parameters, a 25.5% shrink from the original YOLO11s. Ablation studies show that our X-YOLO model slashes computational costs by a hearty 21.5% without missing a beat, hitting [email protected] and [email protected]:0.95 scores of 65% and 41.8%, respectively. This confirms that our lightweight design and dynamic feature-boosting strategies are the real deal.</div></div><div><h3>Conclusion:</h3><div>In summary, we have crafted a detection framework that is a perfect fit for pediatric wrist X-rays, thanks to the clever combo of HGNetV2, DySample, GSConv, and Focaler-CIoU. Our model has a keen eye for microfractures and bone lesions, all while keeping the computational footprint small and the latency low.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108874"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate segmentation and labeling of coronary artery segments in X-ray angiography with an improved UNet-based cGAN architecture","authors":"Qiuju Yang ,&nbsp;Hang Yi ,&nbsp;Liangping Yi ,&nbsp;Mian Liu ,&nbsp;Xuliang Chen","doi":"10.1016/j.bspc.2025.108812","DOIUrl":"10.1016/j.bspc.2025.108812","url":null,"abstract":"<div><div>X-ray coronary angiography (XCA) is the gold standard for the diagnosis and treatment of coronary artery disease (CAD). Accurate segmentation and labeling of coronary artery segments is critical in the CAD diagnostic process. This study introduces UCNet, an instance segmentation method that combines conditional generative adversarial networks (cGAN) with an improved UNet architecture, to improve the labeling and segmentation of coronary segments in XCA images. By leveraging binary segmentation images of coronary vessels as condition variables, our approach facilitates data generation based on specific criteria. To accurately identify and delineate each coronary segment, we propose a novel segment loss function that utilizes the intersection between predicted masks and ground truth for each segment, thereby improving the accuracy of instance segmentation. In addition, to mitigate class imbalance among vessel segments, we incorporate focal loss and multi-class dice loss to improve the detection of underrepresented segments. Evaluation of UCNet on the ARCADE Challenge datasets at MICCAI 2023 shows an average F1 score of 84.43% across 20 coronary segments. This segmentation performance is superior to state-of-the-art coronary segment labeling methods, despite being trained on a smaller amount of labeled data. Furthermore, our improved UNet significantly outperforms six mainstream U-shaped architectures (including UNet, UNet++, nnUNet, AttentionUNet, SwinUNet, and TransUNet) for vessel labeling and boundary segmentation in terms of accuracy, sensitivity, specificity, precision, intersection over union (IoU), and F1 scores. These results confirm the effectiveness and practicality of our proposed method.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108812"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the differences between five basic tastes and tasteless stimuli on brain responses in young adult males utilizing scalp electroencephalography","authors":"Wanxiu Xu ,&nbsp;Yunqi Zhu ,&nbsp;Yuansheng Zhou ,&nbsp;Siyu Liu ,&nbsp;Bin Zhou ,&nbsp;Weidong Jiao ,&nbsp;Yongsheng Pei ,&nbsp;Yonghua Jiang ,&nbsp;Shuangxi Li ,&nbsp;Gang Li ,&nbsp;Yu Sun","doi":"10.1016/j.bspc.2025.108883","DOIUrl":"10.1016/j.bspc.2025.108883","url":null,"abstract":"<div><div>Different tastes help people distinguish the nutritional content and potential harmful substances in food. Understanding the physiological and neural mechanisms of tastes, especially how the brain processes and decodes these taste signals, is essential for comprehending the nature of taste perception. This study aims to explore neural mechanisms of taste. Sixteen male university students participated, and electroencephalography (EEG) data were collected during 40 s exposures to five taste (i.e., sour, sweet, bitter, salty, umami) and one tasteless liquid stimuli. Analysis of power spectrum features for these stimuli was then conducted and quantitatively assessed. The findings reveal that the most sensitive EEG responses to taste stimuli occur in the alpha1 and alpha2 bands. Significant differences in EEG responses between taste stimuli and tasteless stimuli were observed around 1–3 s post stimulation. Notably, there is a double-hump response for alpha1 and alpha2. Temporal, occipital, and central lobes exhibited more pronounced differences. Brain topography maps revealed differences in alpha2 rhythm for the five basic tastes throughout the 1–3 s. These findings offer valuable insights into the relationship between taste perception and the brain’s detection and processing of taste stimuli, contributing substantial theoretical and practical value to fields including food science, sensory perception, and neurocognitive research.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108883"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced cardiovascular disease classification using the mayfly algorithm and real-time data","authors":"R Deepika ,&nbsp;A Bharathi","doi":"10.1016/j.bspc.2025.108755","DOIUrl":"10.1016/j.bspc.2025.108755","url":null,"abstract":"<div><div>Cardiovascular diseases are the leading cause of death worldwide, therefore precise and timely diagnosis improves patient outcomes while lowering medical costs. Effectiveness of classification models is still limited by the challenges associated with managing high-dimensional medical datasets, despite advancements in machine learning. Traditional feature selection strategies are often ineffective due to the data dimensionality and complexity. This study improves feature selection and classification accuracy in cardiovascular disease datasets by utilising the Mayfly Algorithm (MA), a novel <em>meta</em>-heuristic optimisation technique inspired by mayfly mating behaviour. The MA is used in the study to find the best features using five real-time cardiovascular datasets. The chosen features are evaluated using classifiers such as Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM). Traditional optimization techniques, including Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Ant Colony Optimisation (ACO), are examined and contrasted. With a large reduction in feature space and good classification accuracy, the MA showed superior performance in feature selection. Compared to previous approaches, which varied between 80% and 85%, the classification accuracies projected to be attained are in the range of 90% to 95% across the five datasets. Significant gains in classification accuracy and feature reduction were attained by the MA’s consistent selection of the most pertinent features. The Mayfly Algorithm’s potential for medical data analysis is demonstrated by this work, especially for high-dimensional cardiovascular datasets. When it comes to real-time disease classification, MA outperforms other optimization strategies in terms of accuracy and effectiveness. The higher accuracy and reduced feature set demonstrate the efficacy of machine learning, pointing to potential wider uses in medical diagnostics.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"112 ","pages":"Article 108755"},"PeriodicalIF":4.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}