Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Sensors, Год журнала: 2024, Номер 24(3), С. 877 - 877

Опубликована: Янв. 29, 2024

The main purpose of this paper is to provide information on how create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was understand the primary aspects creating and fine-tuning CNNs various application scenarios. We considered characteristics signals, coupled with an exploration signal processing data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, dimension among others. In addition, we conduct in-depth analysis well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, combined architecture. This further offers comprehensive evaluation these covering accuracy metrics, hyperparameters, appendix that contains table outlining parameters commonly used architectures feature extraction

Язык: Английский

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Multistage transfer learning for medical images

Artificial Intelligence Review, Год журнала: 2024, Номер 57(9)

Опубликована: Авг. 6, 2024

Abstract Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep algorithms for optimal performance in This paper explores growing demand precise robust analysis by focusing on an advanced technique, multistage transfer learning. Over past decade, has emerged as a pivotal strategy, particularly overcoming associated with limited data model generalization. However, absence well-compiled literature capturing this development remains gap field. exhaustive investigation endeavors to address providing foundational understanding how approaches confront unique posed insufficient datasets. The offers detailed types, architectures, methodologies, strategies deployed Additionally, it delves into intrinsic within framework, comprehensive overview current state while outlining potential directions advancing methodologies future research. underscores transformative analysis, valuable guidance researchers healthcare professionals.

Язык: Английский

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Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024)

Information Fusion, Год журнала: 2025, Номер 118, С. 102982 - 102982

Опубликована: Янв. 30, 2025

Язык: Английский

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Finite Element Modeling-Assisted Deep Subdomain Adaptation Method for Tool Condition Monitoring

Processes, Год журнала: 2025, Номер 13(2), С. 545 - 545

Опубликована: Фев. 15, 2025

To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique employed to construct model for (target domain), and similarity between simulated data assessed obtain valid samples target domain. Subsequently, time–frequency Markov representation method utilized extract imaging features from samples, which serve as input model. Then, DSAN established facilitate transfer simulation reality, source domain comprising sample set conditions that includes various types of obtained through FEM, containing only limited number normal conditions. The application analysis has demonstrated effectiveness proposed method, achieving classification accuracy 99%. can significantly high-precision diagnostics small size.

Язык: Английский

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Combining fiber Bragg grating sensors and artificial intelligence in medicine

APL Photonics, Год журнала: 2025, Номер 10(3)

Опубликована: Март 1, 2025

Healthcare has rapidly evolved in the last decades, driven by demand for personalized therapies and advancements enabling technologies. Among many solutions, fiber Bragg grating (FBG) sensors have gained significant acceptance medical field, due to their good static dynamic performance, small dimensions, biocompatibility immunity electromagnetic interferences. The integration of artificial intelligence (AI) with FBGs is emerging as a breakthrough approach, design smart systems applications, like minimally invasive surgery, physiological monitoring, biomechanics, biosensing. These harness potential advanced data processing capabilities AI improve diagnostics therapeutic procedures. This perspective provides an overview sensing that combine FBG technologies medicine, focusing on working principle, potentials, challenges. It also explores open research directions encouraging further investigations this field.

Язык: Английский

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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis

Frontiers in Digital Health, Год журнала: 2025, Номер 7

Опубликована: Март 27, 2025

Background Type 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents comprehensive bibliometric systematic review of 33 years (1991-2024) research on machine learning (ML) artificial intelligence (AI) applications in T2DM prediction. It highlights the growing complexity field identifies key trends, methodologies, gaps. Methods A methodology guided literature selection process, starting with keyword identification using Term Frequency-Inverse Document Frequency (TF-IDF) expert input. Based these refined keywords, was systematically selected PRISMA guidelines, resulting dataset 2,351 articles from Web Science Scopus databases. Bibliometric analysis performed entire tools such as VOSviewer Bibliometrix, enabling thematic clustering, co-citation analysis, network visualization. To assess most impactful literature, dual-criteria combining relevance impact scores applied. Articles were qualitatively assessed their alignment prediction four-point scale quantitatively evaluated based citation metrics normalized within subject, journal, publication year. scoring above predefined threshold for detailed review. The spans four time periods: 1991–2000, 2001–2010, 2011–2020, 2021–2024. Results findings reveal exponential growth publications since 2010, USA UK leading contributions, followed by emerging players like Singapore India. Key clusters include foundational ML techniques, epidemiological forecasting, modelling, clinical applications. Ensemble methods (e.g., Random Forest, Gradient Boosting) deep Convolutional Neural Networks) dominate recent advancements. Literature reveals that, studies primarily used demographic variables, while efforts integrate genetic, lifestyle, environmental predictors. Additionally, advances integrating real-world datasets, trends federated learning, explainability SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations). Conclusion Future work should address gaps generalizability, interdisciplinary research, psychosocial integration, also focusing clinically actionable solutions applicability combat diabetes epidemic effectively.

Язык: Английский

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Calibrating the prediction model of soluble solids content and firmness in kiwifruit across years based on NIR spectroscopy using model transfer and transfer learning

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Год журнала: 2025, Номер unknown, С. 126122 - 126122

Опубликована: Март 1, 2025

Язык: Английский

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Advancements in Coronary CT Angiography: Innovations in Diagnosis, Risk Stratification, and Prognosis in Atherosclerosis

Current Treatment Options in Cardiovascular Medicine, Год журнала: 2025, Номер 27(1)

Опубликована: Апрель 14, 2025

Язык: Английский

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Enhancing diagnostic accuracy in symptom-based health checkers: a comprehensive machine learning approach with clinical vignettes and benchmarking

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Окт. 1, 2024

The development of machine learning models for symptom-based health checkers is a rapidly evolving area with significant implications healthcare. Accurate and efficient diagnostic tools can enhance patient outcomes optimize healthcare resources. This study focuses on evaluating optimizing using dataset 10 diseases 9,572 samples.

Язык: Английский

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From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management

Hämostaseologie, Год журнала: 2024, Номер 44(06), С. 429 - 445

Опубликована: Дек. 1, 2024

The high incidence of venous thromboembolism (VTE) globally and the morbidity mortality burden associated with disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, treatment. ability this novel technology to process large amounts high-dimensional data help identify new risk factors better stratify patients for thromboprophylaxis. Applications ML include systems that interpret medical imaging, assess severity VTE, tailor treatment according individual patient needs, cases facilitate surveillance. Generative artificial intelligence may be leveraged design molecules such as anticoagulants, generate synthetic expand datasets, reduce clinical by assisting in generating notes. Potential challenges applications these technologies availability multidimensional prospective studies trials ensure safety efficacy, continuous quality assessment maintain algorithm accuracy, mitigation unwanted bias, regulatory legal guardrails protect providers. We propose practical approach clinicians integrate into research, from choosing appropriate problems integrating workflows. offers much promise opportunity researchers translate clinic directly benefit patients.

Язык: Английский

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