Article
Computer Science, Artificial Intelligence
Li Zhang, Xiaohan Zheng, Qingqing Pang, Weida Zhou
Summary: This paper investigates the issue of computational complexity in GKSVM-RFE and proposes two fast versions for feature ranking. By introducing approximate Gaussian kernels, two ranking scores based on different approximate schemes are designed to calculate and rank features quickly in iterations.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Interdisciplinary Applications
Yousef Rezaei Tabar, Kaare B. Mikkelsen, Mike Lind Rank, Martin Christian Hemmsen, Preben Kidmose
Summary: This study aimed to represent sleep EEG patterns using a minimum number of features without significant loss in performance. Through feature selection algorithms, it was found that 5 to 11 features could represent the whole feature set without performance loss. Features were divided into groups, with relative power features identified as the most informative.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Environmental Sciences
Christopher A. A. Ramezan
Summary: Feature selection is important in remote sensing analysis to improve classification accuracy and reduce computational complexity. However, the generalizability and transferability of feature selection results depend on different classification models and datasets. While feature selection results can provide insights for analysis, they may not always provide comparable accuracies when applied to other classification models or similar remotely sensed datasets. Therefore, feature selection should be individually conducted for each training set to determine the optimal feature set for the classification model.
Article
Computer Science, Interdisciplinary Applications
Xiaojian Ding, Fan Yang, Fuming Ma
Summary: The paper addresses the issue of model selection in support vector machine-based recursive feature elimination (SVM-RFE), proposing an approximation method to evaluate the generalization error and a new criterion to tune the penalty parameter C. The expensive computational cost of the algorithm is mitigated by several alpha seeding approaches, showing superior performance on bioinformatics datasets and empirical time savings.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Neurosciences
Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Sid E. O'Bryant
Summary: The study demonstrated the effectiveness of SVM-RFE-LOO algorithm in reducing the number of biomarkers in the early detection model of AD, achieving high sensitivity and specificity. SVM-RFE-LOO outperformed other methods, showing robustness in handling noisy data and improving prediction performance.
JOURNAL OF ALZHEIMERS DISEASE
(2021)
Article
Computer Science, Artificial Intelligence
Santosh Kumar Satapathy, D. Loganathan
Summary: Sleep is crucial for human health and quality of life, but sleep problems can lead to neurological and physical disorders, decreasing overall life quality. This study proposes a high-effective and high-accuracy based multiple sleep staging classification model using machine learning, which goes through four stages: signal preprocessing, feature extraction, classification algorithms, and performance evaluation.
Article
Computer Science, Artificial Intelligence
Panfeng An, Zhiyong Yuan, Jianhui Zhao
Summary: In this study, a novel unsupervised multisubepoch feature learning and hierarchical classification method for automatic sleep staging based on EEG signals is proposed. Experimental results show that the method has better sleep staging performance, which can effectively promote the development and application of EEG sleep staging system.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Public, Environmental & Occupational Health
Lan Zhuang, Minhui Dai, Yi Zhou, Lingyu Sun
Summary: This study introduces a deep learning network into the study of sleep stages and proposes a feature fusion method to improve the accuracy of sleep stage recognition. The experimental results show that this method provides an effective approach for the diagnosis and treatment of sleep disorders.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Engineering, Biomedical
Zheng Chen, Ziwei Yang, Lingwei Zhu, Wei Chen, Toshiyo Tamura, Naoaki Ono, Md Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang
Summary: In this paper, a novel framework is proposed for automated sleep staging based on sleep medicine guidance. The framework captures time-frequency characteristics of sleep EEG signals and utilizes a Transformer model with an attention-based module for staging decisions. The method achieves state-of-the-art results and demonstrates high inter-rater reliability, with important implications for healthcare and neuroscience research.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Medicine, General & Internal
Vijendra Singh, Vijayan K. Asari, Rajkumar Rajasekaran
Summary: This study aims to detect and predict Chronic Kidney Disease (CKD) early using a deep learning model, which achieved 100% accuracy by selecting key features and applying machine learning models for classification purposes.
Article
Engineering, Electrical & Electronic
Zhou Shuai, Li Tao, Li Yongzhao
Summary: This paper proposes a modulation recognition algorithm based on feature selection. By using the hyperplane of the support vector machine and the weight vector of features, cumulative features are selected and the modulation type employed at the transmitter is identified. Simulation results show that the proposed algorithm can optimize feature selection for modulation recognition and improve identification efficiency when compared with existing feature selection algorithms.
CHINESE JOURNAL OF ELECTRONICS
(2023)
Article
Operations Research & Management Science
M. A. Ganaie, M. Tanveer, Jatin Jangir
Summary: In this study, a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) is proposed for the classification of EEG signals. The Pin-UTSVM model is more robust to noise compared to existing models and performs better in experimental results.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Engineering, Biomedical
A. S. Anusha, S. P. Preejith, Tony J. Akl, Mohanasankar Sivaprakasam
Summary: This study focuses on utilizing autonomic physiological signals to characterize sleep stages, specifically using electrodermal activity and skin temperature. The results demonstrate that these signals are effective for sleep staging and have good generalizability.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Neurosciences
Wu Wen
Summary: This study proposes a sleep quality detection method based on EEG signals, which showed high classification performance in evaluating sleep quality. The effectiveness of the proposed method was further validated through experimental results.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)