Biocybernetics and Biomedical Engineering最新文献

{"title":"Novel copolymers of poly(sebacic anhydride) and poly(ethylene glycol) as azithromycin carriers to the lungs","authors":"Konrad Kwiecień ,&nbsp;Karolina Knap ,&nbsp;Rick Heida ,&nbsp;Jonasz Czajkowski ,&nbsp;Alan Gorter ,&nbsp;Dorota Ochońska ,&nbsp;Przemysław Mielczarek ,&nbsp;Agata Dorosz ,&nbsp;Daria Niewolik ,&nbsp;Katarzyna Reczyńska-Kolman ,&nbsp;Katarzyna Jaszcz ,&nbsp;Monika Brzychczy-Włoch ,&nbsp;Tomasz R. Sosnowski ,&nbsp;Peter Olinga ,&nbsp;Elżbieta Pamuła","doi":"10.1016/j.bbe.2025.01.002","DOIUrl":"10.1016/j.bbe.2025.01.002","url":null,"abstract":"<div><div>By many chronic lung diseases, there is a problem of recurrent bacterial infections that require frequent usage of antibiotics. They can be more effective and cause fewer side effects when administrated directly via the pulmonary route. For such purposes, various types of inhalers are used of which dry powder inhalers (DPIs) are one of the most common. Formulations such as dry powders usually consist of an active pharmaceutical ingredient (API) and a carrier material that is supposed to provide adequate properties to deliver the bioactive molecules to the site of action, effectively. Copolymers of sebacic acid (SA) and poly(ethylene glycol) (PEG) have been regarded as suitable materials for such formulations. Here, we present a study about the manufacturing of microparticles from such materials dedicated to inhalation which have been loaded with azithromycin (AZM). The microparticles (MPs) were 0.5 to 5 µm in size, presenting either a spherical or elongated shape depending on the material type and composition. The encapsulation efficiency (EE) of the MPs were almost complete with the drug loading up to 23.1 %. The powders had fair or good flowability based on Carr’s index and Hausner ratio. Due to the presence of the drug, the tendency to agglomerate decreased. As a result, up to 90 % of the obtained powders showed diameters below 5 µm. Also, the fine particles fraction (FPF) of the chosen formulation reached 66.3 ± 4.5 % and the mass median aerodynamic diameter was 3.8 ± 0.4 µm. The microparticles degraded quickly <em>in vitro</em> losing up to 50 % of their mass within 24 h and up to 80 % within 96 h of their incubation in phosphate-buffered saline (PBS). They were also nontoxic up to 100 µg/ml when added to cultures of A549 and BEAS-2B lung epithelial cells as well as to rat lung tissue slices tested <em>ex vivo</em>. The microparticles showed bactericidal effects against various strains of <em>Staphylococcus aureus</em> in lower than cytotoxic concentrations.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 114-136"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional constraint consistency contrastive learning for automatic detection of urinary sediment in microscopic images","authors":"Fufei Li ,&nbsp;Li Chen ,&nbsp;Ge Song ,&nbsp;Lianzheng Su ,&nbsp;Shian Wang ,&nbsp;Qiuyue Fu ,&nbsp;Yongqi Nie ,&nbsp;Peng Wang","doi":"10.1016/j.bbe.2025.01.003","DOIUrl":"10.1016/j.bbe.2025.01.003","url":null,"abstract":"<div><div>Diagnosing renal and urinary system illnesses usually entails analysing the sediment found in urine. The components in microscopic urine images are diverse and show high similarity, with low contrast due to noise, impeding the progress of automated urine analysis. In order to tackle this difficulty, we propose a region-constrained consistency contrastive learning approach for automated urine analysis. In the first stage, we tackle the complex overlap phenomena in microscopic urine images by innovating the Urine Sediment Paste (US-Paste) positive sample construction method based on supervised contrastive learning. This method uses label information to apply regional constraints and improves the performance of out-of-distribution detection. We also rebuilt the Global Guidance Module (GG Module) and the Enhanced Supervision Module(ES Module). The former improves contrast in urine sediment images by restoring important image details guided by an encoder–decoder structure, while the latter achieves strong feature consistency by combining the most pertinent feature responses from four sets of attention feature maps, which are further mapped via a projection network. In the second phase, we enhance the representations acquired in the initial phase by incorporating a linear classification layer. Our region-constrained consistency contrastive learning algorithm attained an average classification accuracy of 98.30%, precision of 98.33%, recall of 98.30%, and F1-score of 98.30% on the private dataset. Furthermore, in the public urine sediment dataset, the approach achieved an average classification accuracy of 96.19%, precision of 95.79%, recall of 96.19%, and F1-score of 95.94%. The public chromosomal dataset yielded an average classification accuracy of 95.46%, precision of 94.84%, recall of 95.47%, and F1-score of 95.15%. Our methodology surpasses the most advanced methods and demonstrates exceptional performance in urine analysis. This showcases the efficiency of our label-based regional limitations, the outstanding out-of-distribution detection performance of US-Paste, and the robust feature consistency achieved by the Guided Supervision Encoder (GS Encoder). This substantially enhances diagnostic efficiency for clinicians and significantly advances the progress of automated urine sediment analysis.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 74-89"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092967","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":"Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization","authors":"R. Sudha ,&nbsp;K.M. Uma Maheswari","doi":"10.1016/j.bbe.2024.12.002","DOIUrl":"10.1016/j.bbe.2024.12.002","url":null,"abstract":"<div><div>Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as pre-processing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 90-104"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127983","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":"Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis","authors":"Diego Castillo-Barnes ,&nbsp;Nicolás J. Gallego-Molina ,&nbsp;Marco A. Formoso ,&nbsp;Andrés Ortiz ,&nbsp;Patrícia Figueiredo ,&nbsp;Juan L. Luque","doi":"10.1016/j.bbe.2024.09.003","DOIUrl":"10.1016/j.bbe.2024.09.003","url":null,"abstract":"<div><div>This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 814-823"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of motor imagery EEG signals using wavelet scattering transform and Bi-directional long short-term memory networks","authors":"Hongyuan Zhang ,&nbsp;Zijian Zhao ,&nbsp;Chong Liu ,&nbsp;Miao Duan ,&nbsp;Zhiguo Lu ,&nbsp;Hong Wang","doi":"10.1016/j.bbe.2024.11.003","DOIUrl":"10.1016/j.bbe.2024.11.003","url":null,"abstract":"<div><div>A brain-computer interface (BCI) is a technology that creates a communication path between the brain and external devices. Raw EEG data in BCI contain a large amount of complex information, but only some of it needs to be focused on in research. So Feature extraction and classification play an important role in BCI by reducing the data dimensionality and improving the accuracy of subsequent classification. Wavelet scattering transform is an emerging feature extraction method that generates time-shift invariant representations of EEG signals. We applied the wavelet scattering transform to extract features from motor imagery EEG signals, and utilized these features for classification purposes. To achieve this, we proposed a new method that combines wavelet scattering transform with a bidirectional long short-term memory (BiLSTM) network in a fusion deep learning network. Wavelet scattering transform can deeply mine the feature information in EEG signals. In the classification stage, multiple time window features obtained in the scattering transform are sent to the BiLSTM network for classification. The final result will be determined by a vote. In addition, for the processing of raw EEG data, we proposed a time-step based time window strategy that can better utilize the small dataset. This operation can obtain EEG data of multiple time steps. The proposed method was validated using BCI competition II dataset III and BCI competition IV dataset 2b. The results show that the proposed method in this paper can effectively improve the accuracy of motor imagery EEG and provide a new idea for the feature extraction and classification research of motor imagery brain-computer interface.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 874-884"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158887","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":"Automating synaptic plasticity analysis: A deep learning approach to segmenting hippocampal field potential signal","authors":"Sabri Altunkaya","doi":"10.1016/j.bbe.2024.09.005","DOIUrl":"10.1016/j.bbe.2024.09.005","url":null,"abstract":"<div><div>Hippocampal field potentials are widely used in research on neurodegenerative diseases, epilepsy, neuropharmacology, and particularly long- and short-term synaptic plasticity. To conduct these studies, it is necessary to identify specific components within hippocampal field potential signals. However, manually marking the relevant signal points for analysis is a time-consuming, error-prone, and subjective process. Currently, there is no specialized software dedicated to automating this task. In this study, three different recurrent neural network-based deep learning architectures were examined for the automatic segmentation of hippocampal field potential signals in two separate experimental studies. In the first experimental study, 10,836 epochs of field potential signals recorded from 54 rats were used, and in the second experimental study, field potential signals with noise added to the above data at different rates were used. The best model achieved an average f-score of 98.1% on noise-free data and 97.15% on data with noise, highlighting its robustness in real-world scenarios. Furthermore, we assessed system stability using the repeated holdout method, which randomly split the data into training and testing sets 100 times, and each time trained a new version of the system. As a result, the proposed system was proven to be reliable and generalizable by showing similar average scores and low variability across all 100 iterations of the test.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 804-813"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420204","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":"Profiled delivery of bicarbonate during weekly cycle of hemodialysis","authors":"Mauro Pietribiasi ,&nbsp;John K. Leypoldt ,&nbsp;Monika Wieliczko ,&nbsp;Malgorzata Twardowska-Kawalec ,&nbsp;Malgorzata Debowska ,&nbsp;Jolanta Malyszko ,&nbsp;Jacek Waniewski","doi":"10.1016/j.bbe.2024.10.002","DOIUrl":"10.1016/j.bbe.2024.10.002","url":null,"abstract":"<div><h3>Background</h3><div>Delivery of bicarbonate during hemodialysis (HD) is aimed at correcting metabolic acidosis in end-stage renal disease patients. We tested modified prescriptions of bicarbonate concentration in dialysis fluid (C_D,bic), aimed to achieve an optimal pre-dialytic bicarbonate plasma concentration (C_P,bic).</div></div><div><h3>Methods</h3><div>We used a mathematical model to prescribe individualized HD treatments consisting of 1) adjustment of C_D,bic to get the pre-dialytic C_P,bic in a prescribed range, 2) increase of bicarbonate load before the long interdialytic break, and 3) a single step of increase in C_D,bic after two hours. The outcomes were tested in 24 stable HD patients, monitored during a week of standard HD (Test Week) and a week of modified treatment (Intervention Week).</div></div><div><h3>Results</h3><div>The response to the model-based prescription was different whether the average C_D,bic during the Intervention Week was higher or lower than the constant value used for the Test Week. For patients with lower average C_D,bic during the Intervention Week, a significant fraction achieved the target (22 ≤ C_P,bic ≤ 24 mEq/L). In the group with higher average C_D,bic, the interventions were effective only in increasing post-dialytic C_P,bic. The simple step-increase profile was effective in linearizing the intradialytic increase in bicarbonate and decreasing the amount of time spent by patients at high plasma C_P,bic.</div></div><div><h3>Conclusions</h3><div>The interventions were effective mostly in patients who needed to lower their pre-dialytic CP_,bic. The resistance of the system to increasing pre-dialytic C_P,bic in other patients might be caused by modifications of breathing or in hydrogen generation that were not accounted for by our model.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 836-843"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662769","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":"Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms","authors":"Matheus B. Rocha ,&nbsp;Flavio P. Loss ,&nbsp;Pedro H. da Cunha ,&nbsp;Madson Poltronieri Zanoni ,&nbsp;Leandro M. de Lima ,&nbsp;Isadora Tavares Nascimento ,&nbsp;Isabella Rezende ,&nbsp;Tania R.P. Canuto ,&nbsp;Luciana de Paula Vieira ,&nbsp;Renan Rossoni ,&nbsp;Maria C.S. Santos ,&nbsp;Patricia Lyra Frasson ,&nbsp;Wanderson Romão ,&nbsp;Paulo R. Filgueiras ,&nbsp;Renato A. Krohling","doi":"10.1016/j.bbe.2024.10.001","DOIUrl":"10.1016/j.bbe.2024.10.001","url":null,"abstract":"<div><div>Skin lesions are classified in benign or malignant. Among the malignant, melanoma is a very aggressive cancer and the major cause of deaths. So, early diagnosis of skin cancer is very desired. In the last few years, there is a growing interest in computer aided diagnostic (CAD) of skin lesions. Near-Infrared (NIR) spectroscopy may provide an alternative source of information to automated CAD of skin lesions to be used with the modern techniques of machine learning and deep learning (MDL). One of the main limitations to apply MDL to spectroscopy is the lack of public datasets. Since there is no public dataset of NIR spectral data to skin lesions, as far as we know, an effort has been made and a new dataset named NIR-SC-UFES, has been collected, annotated and analyzed generating the gold-standard for classification of NIR spectral data to skin cancer. Next, the machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional neural network (1D-CNN) and standard algorithms as SVM and PLS-DA were investigated to classify cancer and non-cancer skin lesions. Experimental results indicate that the best performance was obtained by LightGBM with pre-processing using standard normal variate (SNV), feature extraction and data augmentation with Generative Adversarial Networks (GAN) providing values of 0.839 for balanced accuracy, 0.851 for recall, 0.852 for precision, and 0.850 for F-score. The obtained results indicate the first steps in CAD of skin lesions aiming the automated triage of patients with skin lesions <em>in vivo</em> using NIR spectral data.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 824-835"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535971","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":"Influence of 3D-printed cellular shoe soles on plantar pressure during running − Experimental and numerical studies","authors":"Paweł Baranowski ,&nbsp;Aleksandra Kapusta ,&nbsp;Paweł Płatek ,&nbsp;Marcin Sarzyński","doi":"10.1016/j.bbe.2024.11.004","DOIUrl":"10.1016/j.bbe.2024.11.004","url":null,"abstract":"<div><div>The paper explores the potential of additive manufacturing (AM), experiments and simulations to develop a personalized shoe sole, with cellular topology used as the insert that minimizes the plantar pressure during running. Five different topologies were manufactured by Fused Filament Fabrication 3D printing technique using thermoplastic polyurethane TPU 95 filaments and tested experimentally and using FEA under compression conditions. The error between the maximum peak force and specific energy absorbed (SEA) from the model and experiment were less than 4.0 % and 6.0 %, respectively. A deformable FE foot model was developed, which was validated against data from the literature on balanced standing and the landing impact test carried out in the study. For the first case, the predicted maximum pressure (<em>P_peak</em> = 0.20 MPa) was positioned between the data presented in previous papers (0.16 MPa ÷ 0.30 MPa). In the second case, the experimentally measured and numerically predicted force peak values were nearly identical: 1760 N and 1720 N, respectively, falling with the range of 2.2 ÷ 2.5 BW similarly to other studies. Finally, a shoe sole design was proposed based on these topologies, which was simulated in the rearfoot impact to investigate the deformation of the sole and its influence on the foot plantar pressure peak and its distribution. The findings indicated that the sole with cellular structure could drastically reduce plantar pressure and improve overall footwear performance. This research provides valuable guidance and insights for designing, modelling, and simulating customized shoe sole manufactured using the 3D printing technique.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 858-873"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight beat score map method for electrocardiogram-based arrhythmia classification","authors":"Kyeonghwan Lee,&nbsp;Jaewon Lee,&nbsp;Miyoung Shin","doi":"10.1016/j.bbe.2024.11.002","DOIUrl":"10.1016/j.bbe.2024.11.002","url":null,"abstract":"<div><div>We recently investigated beat score map (BSM)-based methods for electrocardiogram (ECG)-based arrhythmia classification. Although BSM-based methods show impressive performance, they are somewhat resource-intensive owing to the arrangement of beat score vectors generated from 1D ECG sequences with zero-padding across time points. To address this issue, we propose a lightweight BSM (Lw-BSM) method that significantly reduces the size of the original BSM while capturing the characteristics of beat arrangement patterns as does the original BSM. Specifically, two types of Lw-BSMs are generated without zero-padding and evaluated for multiclass arrhythmia prediction. Experimental results on two public datasets, MIT-BIH and SPH, demonstrate that arrhythmia classification using Lw-BSM images is quite comparable to that using the original BSM images as an input to CNN-based classification models. At the same time, the image size can be reduced significantly. Moreover, it is observed that this approach is almost insensitive to the selection of the R-peak detection algorithm, showing stable performance across different R-peak algorithms.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 844-857"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}