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

{"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}

{"title":"Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis","authors":"Ennio Idrobo-Ávila;Gergő Bognár;Dagmar Krefting;Thomas Penzel;Péter Kovács;Nicolai Spicher","doi":"10.1109/OJEMB.2024.3379733","DOIUrl":"10.1109/OJEMB.2024.3379733","url":null,"abstract":"<italic>Goal:</i>\u0000 Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. \u0000<italic>Methods:</i>\u0000 We applied widely known algorithms entitled “signal quality indicators” to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. \u0000<italic>Results:</i>\u0000 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. \u0000<italic>Conclusions:</i>\u0000 The majority of data within VitalDB is suitable for multimodal analysis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"250-260"},"PeriodicalIF":5.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10476670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202163","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":"Unrolled Optimization via Physics-Assisted Convolutional Neural Network for MR-Based Electrical Properties Tomography: A Numerical Investigation","authors":"Sabrina Zumbo;Stefano Mandija;Ettore F. Meliadò;Peter Stijnman;Thierry G. Meerbothe;Cornelis A.T. van den Berg;Tommaso Isernia;Martina T. Bevacqua","doi":"10.1109/OJEMB.2024.3402998","DOIUrl":"10.1109/OJEMB.2024.3402998","url":null,"abstract":"Magnetic Resonance imaging based Electrical Properties Tomography (MR-EPT) is a non-invasive technique that measures the electrical properties (EPs) of biological tissues. In this work, we present and numerically investigate the performance of an unrolled, physics-assisted method for 2D MR-EPT reconstructions, where a cascade of Convolutional Neural Networks is used to compute the contrast update. Each network takes in input the EPs and the gradient descent direction (encoding the physics underlying the adopted scattering model) and returns as output the updated contrast function. The network is trained and tested in silico using 2D slices of realistic brain models at 128 MHz. Results show the capability of the proposed procedure to reconstruct EPs maps with quality comparable to that of the popular Contrast Source Inversion-EPT, while significantly reducing the computational time.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"505-513"},"PeriodicalIF":2.7,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10534835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150877","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":"Lymphocyte-Infiltrated Periportal Region Detection With Structurally-Refined Deep Portal Segmentation and Heterogeneous Infiltration Features","authors":"Hung-Wen Tsai;Chien-Yu Chiou;Wei-Jong Yang;Tsan-An Hsieh;Cheng-Yi Chen;Che-Wei Hsu;Yih-Jyh Lin;Min-En Hsieh;Matthew M. Yeh;Chin-Chun Chen;Meng-Ru Shen;Pau-Choo Chung","doi":"10.1109/OJEMB.2024.3379479","DOIUrl":"10.1109/OJEMB.2024.3379479","url":null,"abstract":"<italic>Goal</i>\u0000: The early diagnosis and treatment of hepatitis is essential to reduce hepatitis-related liver function deterioration and mortality. One component of the widely-used Ishak grading system for the grading of periportal interface hepatitis is based on the percentage of portal borders infiltrated by lymphocytes. Thus, the accurate detection of lymphocyte-infiltrated periportal regions is critical in the diagnosis of hepatitis. However, the infiltrating lymphocytes usually result in the formation of ambiguous and highly-irregular portal boundaries, and thus identifying the infiltrated portal boundary regions precisely using automated methods is challenging. This study aims to develop a deep-learning-based automatic detection framework to assist diagnosis. \u0000<italic>Methods</i>\u0000: The present study proposes a framework consisting of a Structurally-REfined Deep Portal Segmentation module and an Infiltrated Periportal Region Detection module based on heterogeneous infiltration features to accurately identify the infiltrated periportal regions in liver Whole Slide Images. \u0000<italic>Results</i>\u0000: The proposed method achieves 0.725 in F1-score of lymphocyte-infiltrated periportal region detection. Moreover, the statistics of the ratio of the detected infiltrated portal boundary have high correlation to the Ishak grade (Spearman's correlations more than 0.87 with p-values less than 0.001) and medium correlation to the liver function index aspartate aminotransferase and alanine aminotransferase (Spearman's correlations more than 0.63 and 0.57 with p-values less than 0.001). \u0000<italic>Conclusions</i>\u0000: The study shows the statistics of the ratio of infiltrated portal boundary have correlation to the Ishak grade and liver function index. The proposed framework provides pathologists with a useful and reliable tool for hepatitis diagnosis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"261-270"},"PeriodicalIF":5.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10476647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202129","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":"Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music","authors":"Saman Khazaei;Md Rafiul Amin;Maryam Tahir;Rose T. Faghih","doi":"10.1109/OJEMB.2024.3377923","DOIUrl":"10.1109/OJEMB.2024.3377923","url":null,"abstract":"<italic>Goal:</i>\u0000 Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. \u0000<italic>Methods:</i>\u0000 We employ a Bayesian filtering approach within an expectation-maximization framework to track the hidden states during the \u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000-back task in the presence of calming and exciting music. We decode the arousal and performance states from the skin conductance and behavioral signals, respectively. We derive an arousal-performance model based on the Yerkes—Dodson law. We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation. \u0000<italic>Results:</i>\u0000 The quantified arousal and performance are presented. The existence of Yerkes—Dodson law can be interpreted from the arousal-performance relationship. Findings display higher matrices of performance within the exciting music. \u0000<italic>Conclusions:</i>\u0000 The performance-based arousal decoder has a better agreement with the Yerkes—Dodson law. Our study can be implemented in designing non-invasive closed-loop systems.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"627-636"},"PeriodicalIF":2.7,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140169852","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":"Objective and Automated Quantification of Instrument Handling for Open Surgical Suturing Skill Assessment: A Simulation-Based Study","authors":"Simar P. Singh;Amir Mehdi Shayan;Jianxin Gao;Joseph Bible;Richard E. Groff;Ravikiran Singapogu","doi":"10.1109/OJEMB.2024.3402393","DOIUrl":"10.1109/OJEMB.2024.3402393","url":null,"abstract":"<italic>Goal:</i>\u0000 Vascular surgical procedures are challenging and require proficient suturing skills. To develop these skills, medical training simulators with objective feedback for formative assessment are gaining popularity. As hardware advancements offer more complex, unique sensors, determining effective task performance measures becomes imperative for efficient suturing training. \u0000<italic>Methods:</i>\u0000 97 subjects of varying clinical expertise completed four trials on a suturing skills measurement and feedback platform (SutureCoach). Instrument handling metrics were calculated from electromagnetic motion trackers affixed to the needle driver. \u0000<italic>Results:</i>\u0000 The results of the study showed that all metrics significantly differentiated between novices (no medical experience) from both experts (attending surgeons/fellows) and intermediates (residents). Rotational motion metrics were more consistent in differentiating experts and intermediates over traditionally used tooltip motion metrics. \u0000<italic>Conclusions:</i>\u0000 Our work emphasizes the importance of tool motion metrics for open suturing skills assessment and establishes groundwork to explore rotational motion for quantifying a critical facet of surgical performance.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"485-493"},"PeriodicalIF":2.7,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10533671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063460","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}