Biomedical Engineering Letters最新文献

{"title":"Convolutional channel modulator for transformer and LSTM networks in EEG-based emotion recognition.","authors":"Hyunwook Kang, Jin Woo Choi, Byung Hyung Kim","doi":"10.1007/s13534-025-00475-7","DOIUrl":"https://doi.org/10.1007/s13534-025-00475-7","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signal is receiving much attention from recent studies since it is highly associated with intrinsic emotion. However, EEG signals contain underlying factors of variations across different sessions of the same subject, which make it difficult to learn temporal relationships between successive time steps. To disentangle invariant features, we propose a feature re-weighting mechanism on the extracted EEG features for temporal sequence modeling. Based on this method, our proposed model, called Convolutional Channel Modulator for Transformer and LSTM networks (CCMTL), extracts emotion-related inter-channel correlations using convolution operations and emphasizes important features by generating a channel attention map. This attention map is then used to perform matrix multiplication on the extracted features, which helps the subsequent Transformer to focus on important affective features. Furthermore, the sequential temporal modeling enhances the overall model's capability to understand temporal relationships both in global and sequential contexts. Experimental settings on public EEG emotion datasets demonstrate the superiority of the proposed CCMTL, surpassing six state-of-the-art models. Our code is publicly available at https://github.com/affctivai/CCMTL.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"749-761"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-Phys: noise-robust heart rate estimation from facial videos via diffusion models.","authors":"Yong-Hoon Jeong, Young-Seok Choi","doi":"10.1007/s13534-025-00472-w","DOIUrl":"10.1007/s13534-025-00472-w","url":null,"abstract":"<p><p>Remote photoplethysmography (rPPG) offers significant potential for health monitoring and emotional analysis through non-contact physiological measurement from facial videos. However, noise remains a crucial challenge, limiting the generalizability of current rPPG methods. This paper introduces Diffusion-Phys, a novel framework using diffusion models for robust heart rate (HR) estimation from facial videos. Diffusion-Phys employs Multi-scale Spatial-Temporal Maps (MSTmaps) to preprocess input data and introduces Gaussian noise to simulate real-world conditions. The model is trained using a denoising network for accurate HR estimation. Experimental evaluations on the VIPL-HR, UBFC-rPPG and PURE datasets demonstrate that Diffusion-Phys achieves comparable or superior performance to state-of-the-art methods, with lower computational complexity. These results highlight the effectiveness of explicitly addressing noise through diffusion modeling, improving the reliability and generalization of non-contact physiological measurement systems.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"575-585"},"PeriodicalIF":3.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a silicone-based flexible dry electrode for measuring human bioelectrical signals.","authors":"Chang-Hee Han, Seong-Uk Kim, Kyung-Soo Lim, Young-Jin Jung, Sangho Lee, Sung Hoon Kim, Han-Jeong Hwang","doi":"10.1007/s13534-025-00471-x","DOIUrl":"10.1007/s13534-025-00471-x","url":null,"abstract":"<p><p>The development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior wearability and reliable bioelectrical signal monitoring. To evaluate its performance, we compared its impedance and flexibility with those of a commercial electrode. Additionally, its compatibility for measuring biological signals was assessed through performance comparisons across various bioelectrical signals. Fourteen healthy participants performed three experimental paradigms: (1) eyes closed and open to measure alpha electroencephalography (EEG) as well as resting-state electrocardiography (ECG), (2) eye blinking to measure electrooculography (EOG), and (3) wrist movement to measure electromyography (EMG). All bioelectrical signals were measured simultaneously using both the silicone-based dry electrode and a commercial dry electrode. The performance of the silicone-based dry electrode was evaluated by comparing the signal quality of both electrodes. The silicone-based dry electrode exhibited lower electrical impedance (39.43 kΩ on average, <i>p</i> = 0.0058) and greater flexibility (Young's modulus: silicone 1.51 ± 0.10 MPa vs. commercial 2.46 ± 0.38 MPa) compared to the commercial dry electrode. Overall, there were minimal differences in signal quality between the two electrodes: i) EEG (α power SNR: silicone 1.39 ± 0.34 vs. commercial 1.36 ± 0.29), ii) ECG (R-peak recall: 99.20 ± 2.50%, correlation coefficient: 0.96 ± 0.08), iii) EOG (eye blink recall: 100.00%, correlation coefficient: 0.98 ± 0.03), and iv) EMG (no significant difference in SNR values). These findings indicate that the developed electrode not only ensures superior flexibility but also maintains compatible electrical properties for measuring various bioelectrical signals.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"563-574"},"PeriodicalIF":3.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time monitoring and quantitative analysis of 3D tumor spheroids using portable cellular imaging system.","authors":"Ji Heon Lim, Ji Wook Choi, Na Yeon Kim, Taewook Kang, Bong Geun Chung","doi":"10.1007/s13534-025-00470-y","DOIUrl":"10.1007/s13534-025-00470-y","url":null,"abstract":"<p><p>Three-dimensional (3D) tumor spheroid models closely mimic in vivo tumor environment and play a vital role in studying oncological research. Despite their significance, the existing methods for analyzing 3D tumor spheroids often suffer from limitations, including low throughput, high cost, and insufficient resolution. To address these challenges, we developed a portable imaging system for the real-time sensing and quantitative analysis of the 3D tumor spheroids. The system integrated the seamless workflow of spheroid generation, cell morphology tracking, and drug screening. The spheroid generation was successfully characterized using MCF-7 breast cancer cells by optimizing cell concentration (5-20 × 10⁶ cells/mL), incubation time (24-96 h) and microwell diameter (400-600 μm). A custom-written algorithm was developed for automated analysis of spheroids, exhibiting high sensitivity (98.99%) and specificity (98.21%). Confusion matrices and receiver operating characteristic curve analysis further confirmed the robustness of the algorithm with an area under the curve value of 93.75% and an equal error rate of 0.79%. Following the characterization, the real-time sensing of spheroid generation and the response of spheroids to drug treatment were successfully demonstrated. Furthermore, the live/dead assays with chemotherapy provided a detailed insight into the efficacy and cytotoxic effects of the drug, demonstrating a significant dose-dependent decrease in a spheroid viability. Therefore, our system offers considerable potential for enhancing drug development processes and personalized treatment strategies, thereby contributing to more effective cancer therapies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13534-025-00470-y.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"549-561"},"PeriodicalIF":3.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.","authors":"Hyung-Tak Lee, Miseon Shim, Xianghong Liu, Hye-Ran Cheon, Sang-Gyu Kim, Chang-Hee Han, Han-Jeong Hwang","doi":"10.1007/s13534-025-00469-5","DOIUrl":"https://doi.org/10.1007/s13534-025-00469-5","url":null,"abstract":"<p><p>Human-computer interaction (HCI) focuses on designing efficient and intuitive interactions between humans and computer systems. Recent advancements have utilized multimodal approaches, such as electroencephalography (EEG)-based systems combined with other biosignals, along with deep learning to enhance performance and reliability. However, no systematic review has consolidated findings on EEG-based multimodal HCI systems. This review examined 124 studies published from 2016 to 2024, retrieved from the Web of Science database, focusing on hybrid EEG-based multimodal HCI systems employing deep learning. The keywords used for evaluation were as follows: 'Deep Learning' AND 'EEG' AND ('fNIRS' OR 'NIRS' OR 'MEG' OR 'fMRI' OR 'EOG' OR 'EMG' OR 'ECG' OR 'PPG' OR 'GSR'). Main topics explored are: (1) types of biosignals used with EEG, (2) neural network architectures, (3) fusion strategies, (4) system performance, and (5) target applications. Frequently paired signals, such as EOG, EMG, and fNIRS, effectively complement EEG by addressing its limitations. Convolutional neural networks are extensively employed for spatio-temporal-spectral feature extraction, with early and intermediate fusion strategies being the most commonly used. Applications, such as sleep stage classification, emotion recognition, and mental state decoding, have shown notable performance improvements. Despite these advancements, challenges remain, including the lack of real-time online systems, difficulties in signal synchronization, limited data availability, and insufficient explainable AI (XAI) methods to interpret signal interactions. Emerging solutions, such as portable systems, lightweight deep learning models, and data augmentation techniques, offer promising pathways to address these issues. This review highlights the potential of EEG-based multimodal HCI systems and emphasizes the need for advancements in real-time interaction, fusion algorithms, and XAI to enhance their adaptability, interpretability, and reliability.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 4","pages":"587-618"},"PeriodicalIF":3.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECGEL: a multimodal 12-lead ECG classification model for heart failure prediction.","authors":"Xintong Liang, Nan Jiang, Pengjia Qi, Zhengkui Chen, Jijun Tong, Shudong Xia","doi":"10.1007/s13534-025-00468-6","DOIUrl":"10.1007/s13534-025-00468-6","url":null,"abstract":"<p><p>Cardiovascular diseases (CVD) are the leading cause of death worldwide, with heart failure (HF) being one of the most fatal conditions within CVD, greatly impacting patients' quality of life and imposing a heavy socioeconomic burden. Early intervention can significantly reduce HF mortality and hospitalization rates. However, current diagnostic methods are often expensive and complex, leading to delayed detection. To address this issue, this paper proposes a multimodal model, ECGEL, which combines electrocardiogram (ECG) and clinical text data for heart failure prediction. The model first denoises 12-lead ECG signals using LUNet, then converts the ECG signals into spectrograms via fast Fourier transform, extracting ECG features using EfficientNetv2. Simultaneously, clinical text is preprocessed with Bert, and textual features are extracted using BiLSTM. Finally, the ECG and text features are fused for heart failure prediction. Experimental results show that the ECGEL model achieved outstanding performance on a private dataset, with accuracy of 97.9%, recall of 98.3%, and F1 score of 97.6%. This model offers an efficient and accurate solution for the early diagnosis of heart failure, showing significant potential for clinical application.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"537-547"},"PeriodicalIF":3.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.","authors":"S I M M Raton Mondol, Ryul Kim, Sangmin Lee","doi":"10.1007/s13534-025-00465-9","DOIUrl":"10.1007/s13534-025-00465-9","url":null,"abstract":"<p><p>Recent research has made significant progress with definitively identifying individuals with Parkinson's disease (PD) using speech analysis techniques. However, these studies have often treated the early and advanced stages of PD as equivalent, overlooking the distinct speech impairments and symptoms that can vary significantly across the various stages. This research aims to enhance diagnostic accuracy by utilizing advanced optimization strategies to combine speech recognition results (character error rates) with the acoustic features of vowels for more rigorous diagnostic precision. The dysphonia features of three sustained Korean vowels /아/ (a), /이/ (i), and /우/ (u) were examined for their diversity and strong correlations. Four recognized machine-learning classifiers: Random Forest, Support Vector Machine, k-Nearest Neighbors, and Multi-Layer Perceptron, were employed for consistent and reliable analysis. By fine-tuning the Whisper model specifically for PD speech recognition and optimizing it for each severity level of PD, we significantly improved the discernibility between PD severity levels. This enhancement, when combined with vowel data, allowed for a more precise classification, achieving an improved detection accuracy of 5.87% for a 3-level severity classification over the PD \"ON\"-state dataset, and an improved detection accuracy of 7.8% for a 3-level severity classification over the PD \"OFF\"-state dataset. This comprehensive approach not only evaluates the effectiveness of different feature extraction methods but also minimizes the variance across final classification models, thus detecting varying severity levels of PD more effectively.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"497-511"},"PeriodicalIF":3.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning classifier solving the problem of sleep stage imbalance between overnight sleep.","authors":"Chanwoo Park, Jung-Ick Byun, Sang Ho Choi, Won Chul Shin","doi":"10.1007/s13534-025-00466-8","DOIUrl":"10.1007/s13534-025-00466-8","url":null,"abstract":"<p><p>Feature extraction follows the American Academy of Sleep Medicine (AASM) sleep score manually and applies it to machine learning with a focus on the generalization of sleep data to enable data-centric artificial intelligence. In real-world clinical testing, the manual scoring of sleep stages is time-consuming and requires significant expertise. Additionally, it is subject to interobserver subjective bias. Machine-learning techniques offer a way to overcome these limitations through automation. However, machine learning for sleep phase prediction can perform poorly for small classes. If the distribution of the training data was unbalanced, the model was trained with a bias toward the majority class. To address this, we experimented with loss function adjustment and resampling methods that assign more weight to the prediction errors of minority classes in sleep scoring to determine how to overcome the data imbalance problem. Machine learning can also be used to compare the accuracy of each channel in identifying electrodes, which should be monitored more closely in real-world clinical testing. Owing to the small amount of data available for machine learning in this study, we used various machine learning classifiers by increasing or decreasing the dataset using sampling techniques and weighting different classes of sleep stages. In our experiments, the best-performing model for classifying sleep stages had an accuracy of 91.9%, kappa of 0.899, and F1-score of 86.9%.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"513-523"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy estimation methods for positron emission tomography detectors composed of multiple scintillators.","authors":"Hyeong Seok Shim, Min Jeong Cho, Jae Sung Lee","doi":"10.1007/s13534-025-00464-w","DOIUrl":"https://doi.org/10.1007/s13534-025-00464-w","url":null,"abstract":"<p><p>The performance and image quality of positron emission tomography (PET) systems can be enhanced by strategically employing multiple different scintillators, particularly those with different decay times. Two cutting-edge PET detector technologies employing different scintillators with different decay times are the phoswich detector and the emerging metascintillator. In PET imaging, accurate and precise energy measurement is important for effectively rejecting scattered gamma-rays and estimating scatter distribution. However, traditional measures of light output, such as amplitude or integration values of photosensor output pulses, cannot accurately indicate the deposit energy of gamma-rays across multiple scintillators. To address these issues, this study explores two methods for energy estimation in PET detectors that employ multiple scintillators. The first method uses pseudo-inverse matrix generated from the unique pulse profile of each crystal, while the second employs an artificial neural network (ANN) to estimate the energy deposited in each crystal. The effectiveness of the proposed methods was experimentally evaluated using three heavy and dense inorganic scintillation crystals (BGO, LGSO, and GAGG) and three fast plastic scintillators (EJ200, EJ224, and EJ232). The energy estimation method employing ANNs consistently demonstrated superior accuracy across all crystal combinations when compared to the approach utilizing the pseudo-inverse matrix. In the pseudo-inverse matrix approach, there is a negligible difference in accuracy when applying integral-based energy labels as opposed to amplitude-based energy labels. On the other hand, in ANN approach, employing integral-based energy labels consistently outperforms the use of amplitude-based energy labels. This study contributes to the advancement of PET detector technology by proposing and evaluating two methods for estimating the energy in the detector using multiple scintillators. The ANN approach appears to be a promising solution for improving the accuracy of energy estimation, addressing challenges posed by mixed scintillation pulses.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"489-496"},"PeriodicalIF":3.2,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic prediction of stroke treatment outcomes: latest advances and perspectives.","authors":"Zeynel A Samak, Philip Clatworthy, Majid Mirmehdi","doi":"10.1007/s13534-025-00462-y","DOIUrl":"https://doi.org/10.1007/s13534-025-00462-y","url":null,"abstract":"<p><p>Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports, and other sensor information, such as EEG, ECG, EMG, and so on. Despite the common data standardisation challenge within the medical image analysis domain, the future of deep learning in stroke outcome prediction lies in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.</p>","PeriodicalId":46898,"journal":{"name":"Biomedical Engineering Letters","volume":"15 3","pages":"467-488"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}