IEEE Open Journal of Engineering in Medicine and Biology最新文献

{"title":"Stability of Watch-Based Blood Pressure Measurements Analyzed by Pre-Post Calibration Differences","authors":"Youngro Lee;Jongae Park;Sungjoon Park;Jongmo Seo;Hae-Young Lee","doi":"10.1109/OJEMB.2024.3384488","DOIUrl":"10.1109/OJEMB.2024.3384488","url":null,"abstract":"Recent advancements in smartwatch technology have introduced photoplethysmography (PPG)-based blood pressure (BP) estimation, enabling convenient and continuous monitoring of BP. However, concerns about accuracy and validation for clinical use persist. This study uses real-world data from a Samsung Galaxy Watch campaign to assess smartwatch-based BP measurements. The approach examines calibration stability by comparing average systolic BP (SBP) before and after calibration, identifying factors affecting stability through regression analysis. User-level strategies are suggested to mitigate calibration instability and emphasize guideline adherence. Notably, calibration instability is found to decrease during night-time measurements and when averaging multiple readings in the same time frame. Guideline adherence is vital, particularly for the elderly, females, and individuals with hypertension. The research enhances measurement reliability through extensive datasets, shedding light on calibration stability.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"828-836"},"PeriodicalIF":2.7,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10490153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability Assessment of Technologies for Remote Monitoring of Knee Osteoarthritis","authors":"Andrea Cafarelli;Angela Sorriento;Giorgia Marola;Denise Amram;Fabien Rabusseau;Hervé Locteau;Paolo Cabras;Erik Dumont;Sam Nakhaei;Ake Jernberger;Pär Bergsten;Paolo Spinnato;Alessandro Russo;Leonardo Ricotti","doi":"10.1109/OJEMB.2024.3407961","DOIUrl":"10.1109/OJEMB.2024.3407961","url":null,"abstract":"<italic>Goal</i>\u0000: To evaluate the usability of different technologies designed for a remote assessment of knee osteoarthritis. \u0000<italic>Methods:</i>\u0000 We recruited eleven patients affected by mild or moderate knee osteoarthritis, eleven caregivers, and eleven clinicians to assess the following technologies: a wristband for monitoring physical activity, an examination chair for measuring leg extension, a thermal camera for acquiring skin thermographic data, a force balance for measuring center of pressure, an ultrasound imaging system for remote echographic acquisition, a mobile app, and a clinical portal software. Specific questionnaires scoring usability were filled out by patients, caregivers and clinicians. \u0000<italic>Results:</i>\u0000 The questionnaires highlighted a good level of usability and user-friendliness for all the technologies, obtaining an average score of 8.7 provided by the patients, 8.8 by the caregivers, and 8.5 by the clinicians, on a scale ranging from 0 to 10. Such average scores were calculated by putting together the scores obtained for the single technologies under evaluation and averaging them. \u0000<italic>Conclusions:</i>\u0000 This study demonstrates a high level of acceptability for the tested portable technologies designed for a potentially remote and frequent assessment of knee osteoarthritis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"476-484"},"PeriodicalIF":5.8,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection","authors":"Narongrid Seesawad;Piyalitt Ittichaiwong;Thapanun Sudhawiyangkul;Phattarapong Sawangjai;Peti Thuwajit;Paisarn Boonsakan;Supasan Sripodok;Kanyakorn Veerakanjana;Komgrid Charngkaew;Ananya Pongpaibul;Napat Angkathunyakul;Narit Hnoohom;Sumeth Yuenyong;Chanitra Thuwajit;Theerawit Wilaiprasitporn","doi":"10.1109/OJEMB.2024.3407351","DOIUrl":"10.1109/OJEMB.2024.3407351","url":null,"abstract":"<italic>Background:</i>\u0000 Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization. \u0000<italic>Objective:</i>\u0000 To address this limitation, we propose \u0000<italic>PseudoCell</i>\u0000, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist's refined labels. \u0000<italic>Methods:</i>\u0000 \u0000<italic>PseudoCell</i>\u0000 leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations. \u0000<italic>Results:</i>\u0000 Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold, \u0000<italic>PseudoCell</i>\u0000 can eliminate 58.18-99.35% of irrelevant tissue areas on WSI, streamlining the diagnostic process. \u0000<italic>Conclusion:</i>\u0000 This study presents \u0000<italic>PseudoCell</i>\u0000 as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section provides detailed guidance for implementing \u0000<italic>PseudoCell</i>\u0000 in clinical practice.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"514-523"},"PeriodicalIF":2.7,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery","authors":"Mahmood Alzubaidi;Uzair Shah;Marco Agus;Mowafa Househ","doi":"10.1109/OJEMB.2024.3382487","DOIUrl":"10.1109/OJEMB.2024.3382487","url":null,"abstract":"<italic>Goal:</i>\u0000 FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. \u0000<italic>Methods:</i>\u0000 Utilizing a comprehensive dataset–the largest to date for fetal head metrics–FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance. Performance benchmarks against prominent models such as U-Net, DeepLabV3, and Segformer highlight its efficacy. \u0000<italic>Results:</i>\u0000 FetSAM delivers unparalleled segmentation accuracy, demonstrated by a DSC of 0.90117, HD of 1.86484, and ASD of 0.46645. \u0000<italic>Conclusion:</i>\u0000 FetSAM sets a new benchmark in AI-enhanced prenatal ultrasound analysis, providing a robust, precise tool for clinical applications and pushing the envelope of prenatal care with its groundbreaking dataset and segmentation capabilities.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"281-295"},"PeriodicalIF":5.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection","authors":"Meng Jiao;Changyue Song;Xiaochen Xian;Shihao Yang;Feng Liu","doi":"10.1109/OJEMB.2024.3405666","DOIUrl":"10.1109/OJEMB.2024.3405666","url":null,"abstract":"Sleep Apnea (SA) is a prevalent sleep disorder with multifaceted etiologies that can have severe consequences for patients. Diagnosing SA traditionally relies on the in-laboratory polysomnogram (PSG), which records various human physiological activities overnight. SA diagnosis involves manual scoring by qualified physicians. Traditional machine learning methods for SA detection depend on hand-crafted features, making feature selection pivotal for downstream classification tasks. In recent years, deep learning has gained popularity in SA detection due to its capability for automatic feature extraction and superior classification accuracy. This study introduces a Deep Attention Network with Multi-Temporal Information Fusion (DAN-MTIF) for SA detection using single-lead electrocardiogram (ECG) signals. This framework utilizes three 1D convolutional neural network (CNN) blocks to extract features from R-R intervals and R-peak amplitudes using segments of varying lengths. Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. Comprehensive experiments and comparisons between two paradigms of classical machine learning approaches and deep learning approaches are conducted. Our experiment results demonstrate that (1) compared with benchmark methods, the proposed DAN-MTIF exhibits excellent performance with 0.9106 accuracy, 0.9396 precision, 0.8470 sensitivity, 0.9588 specificity, and 0.8909 \u0000<inline-formula><tex-math>$F_{1}$</tex-math></inline-formula>\u0000 score at per-segment level; (2) DAN-MTIF can effectively extract features with a higher degree of discrimination from ECG segments of multiple timescales than those with a single time scale, ensuring a better SA detection performance; (3) the overall performance of deep learning methods is better than the classical machine learning algorithms, highlighting the superior performance of deep learning approaches for SA detection.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"792-802"},"PeriodicalIF":2.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10539178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction","authors":"Ivan-Daniel Sievering;Ortal Senouf;Thabo Mahendiran;David Nanchen;Stephane Fournier;Olivier Muller;Pascal Frossard;Emmanuel Abbé;Dorina Thanou","doi":"10.1109/OJEMB.2024.3403948","DOIUrl":"10.1109/OJEMB.2024.3403948","url":null,"abstract":"<italic>Goal:</i>\u0000 In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI from clinical data and Invasive Coronary Angiography (ICA) images. \u0000<italic>Methods:</i>\u0000 The images are analyzed by Convolutional Neural Networks (CNNs) guided by anatomical information, and the clinical data by an Artificial Neural Network (ANN). Embeddings from both sources are then merged to provide a patient-level prediction. \u0000<italic>Results:</i>\u0000 The results of our framework on a clinical study of 445 patients admitted with acute coronary syndromes confirms that multimodal learning increases the predictive power and achieves good performance (AUC: \u0000<inline-formula><tex-math>$0.67pm 0.04$</tex-math></inline-formula>\u0000 & F1-Score: \u0000<inline-formula><tex-math>$0.36pm 0.12$</tex-math></inline-formula>\u0000), which outperforms the prediction obtained by each modality independently as well as that of interventional cardiologists (AUC: 0.54 & F1-Score: 0.18). \u0000<italic>Conclusions:</i>\u0000 To the best of our knowledge, this is the first attempt towards combining multimodal data through a deep learning framework for future MI prediction. Although it demonstrates the superiority of multi-modal approaches over single modality, the results do not yet meet the necessary criteria for practical application.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"837-845"},"PeriodicalIF":2.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Investigation of Manifold-Based Direct Control for a Brain-to-Body Neural Bypass","authors":"E. Losanno;M. Badi;E. Roussinova;A. Bogaard;M. Delacombaz;S. Shokur;S. Micera","doi":"10.1109/OJEMB.2024.3381475","DOIUrl":"10.1109/OJEMB.2024.3381475","url":null,"abstract":"<italic>Objective:</i>\u0000 Brain-body interfaces (BBIs) have emerged as a very promising solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present a preliminary investigation in a monkey of a brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, aiming at achieving ease of learning and long-term robustness. \u0000<italic>Results:</i>\u0000 We identified an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkey's neural activity associated to reach-to-grasp movements. We then tested the animal's ability to directly control a computer cursor using cortical activation along the manifold axes. By daily recalibrating only scaling factors, we achieved rapid learning and stable high performance in simple, incremental 2D tasks over more than 12 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger voluntary hand movements. \u0000<italic>Conclusions:</i>\u0000 These results represent a proof of concept of manifold-based direct control for BBI applications.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"271-280"},"PeriodicalIF":5.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10478790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140302370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals","authors":"Vladimir Atanasoski;Jovana Petrović;Lana Popović Maneski;Marjan Miletić;Miloš Babić;Aleksandra Nikolić;Dorin Panescu;Marija D. Ivanović","doi":"10.1109/OJEMB.2024.3380352","DOIUrl":"10.1109/OJEMB.2024.3380352","url":null,"abstract":"<italic>Goal:</i>\u0000 Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. \u0000<italic>Methods:</i>\u0000 Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. \u0000<italic>Results:</i>\u0000 IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. \u0000<italic>Conclusions</i>\u0000: IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"296-305"},"PeriodicalIF":5.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140302635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial Introduction to the Special Section on Weakly-Supervised Deep Learning and Its Applications","authors":"Yu-Dong Zhang","doi":"10.1109/OJEMB.2024.3404653","DOIUrl":"10.1109/OJEMB.2024.3404653","url":null,"abstract":"Researchers in biomedical engineering are increasingly turning to weakly-supervised deep learning (WSDL) techniques [1] to tackle challenges in biomedical data analysis, which often involves noisy, limited, or imprecise expert annotations [2]. WSDL methods have emerged as a solution to alleviate the manual annotation burden for structured biomedical data like signals, images, and videos [3] while enabling deep neural network models to learn from larger-scale datasets at a reduced annotation cost. With the proliferation of advanced deep learning techniques such as generative adversarial networks (GANs), graph neural networks (GNNs) [4], vision transformers (ViTs) [5], and deep reinforcement learning (DRL) models [6], research endeavors are focused on solving WSDL problems and applying these techniques to various biomedical analysis tasks.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"393-395"},"PeriodicalIF":5.8,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10537991","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level","authors":"Chiara Romano;Emanuele Maiorana;Annunziata Nusca;Simone Circhetta;Sergio Silvestri;Schena Emiliano;Gian Paolo Ussia;Carlo Massaroni","doi":"10.1109/OJEMB.2024.3402151","DOIUrl":"10.1109/OJEMB.2024.3402151","url":null,"abstract":"<italic>Goal:</i>\u0000 To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. \u0000<italic>Methods:</i>\u0000 SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. \u0000<italic>Results:</i>\u0000 The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3% accuracy) and at the pulmonary valve for GCG (92.1%). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5%). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2% accuracy with a single sensor on the pulmonary valve. \u0000<italic>Conclusions:</i>\u0000 Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"867-876"},"PeriodicalIF":2.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10534834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}