Pub Date : 2024-03-25DOI: 10.1109/OJEMB.2024.3381475
E. Losanno;M. Badi;E. Roussinova;A. Bogaard;M. Delacombaz;S. Shokur;S. Micera
Objective: 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. Results: 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. Conclusions: These results represent a proof of concept of manifold-based direct control for BBI applications.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-25DOI: 10.1109/OJEMB.2024.3380352
Vladimir Atanasoski;Jovana Petrović;Lana Popović Maneski;Marjan Miletić;Miloš Babić;Aleksandra Nikolić;Dorin Panescu;Marija D. Ivanović
Goal: 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. Methods: 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. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions : IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.
目标：心电图（ECG）的临床解读可能会受到噪声的不利影响。由于肌电图（EMG）噪声的频谱与 QRS 波群重叠，因此去除肌电图噪声尤其具有挑战性。现有的肌电图去噪算法往往会扭曲信号形态，从而掩盖诊断相关信息。方法：本文提出了一种新的迭代再生方法（IRM），用于有效抑制肌电图噪声。其主要假设是，暂时去除主要的心电图成分可在提取噪声的同时将对信号的改变降到最低。该方法在同时记录参考信号和噪声信号的 SimEMG 数据库、MIT-BIH 心律失常数据库和合成心电信号上进行了验证。结果IRM 去噪和形态保持性能优于基于小波和 FIR 的基准方法。结论：IRMIRM 可靠、计算不密集、速度快，适用于移动或标准心电图设备记录的任何数量的心电图通道。
{"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":null,"pages":null},"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}
Pub Date : 2024-03-23DOI: 10.1109/OJEMB.2024.3404653
Yu-Dong Zhang
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-20DOI: 10.1109/OJEMB.2024.3402151
Chiara Romano;Emanuele Maiorana;Annunziata Nusca;Simone Circhetta;Sergio Silvestri;Schena Emiliano;Gian Paolo Ussia;Carlo Massaroni
Goal: 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. Methods: 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. Results: 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. Conclusions: Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.
{"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":null,"pages":null},"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}
Goal: 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. Methods: 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. Results: 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. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-20DOI: 10.1109/OJEMB.2024.3402998
Sabrina Zumbo;Stefano Mandija;Ettore F. Meliadò;Peter Stijnman;Thierry G. Meerbothe;Cornelis A.T. van den Berg;Tommaso Isernia;Martina T. Bevacqua
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.
{"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":null,"pages":null},"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}
Goal : 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. Methods : 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. Results : 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). Conclusions : 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-18DOI: 10.1109/OJEMB.2024.3377923
Saman Khazaei;Md Rafiul Amin;Maryam Tahir;Rose T. Faghih
Goal: 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. Methods: We employ a Bayesian filtering approach within an expectation-maximization framework to track the hidden states during the $n$ -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. Results: 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. Conclusions: 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-17DOI: 10.1109/OJEMB.2024.3402393
Simar P. Singh;Amir Mehdi Shayan;Jianxin Gao;Joseph Bible;Richard E. Groff;Ravikiran Singapogu
Goal: 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. Methods: 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. Results: 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. Conclusions: 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-17DOI: 10.1109/OJEMB.2024.3402531
A. Scotland;G. Cosne;A. Juraver;A. Karatsidis;J. Penalver-Andres;E. Bartholomé;C. M. Kanzler;C. Mazzà;D. Roggen;C. Hinchliffe;S. Del Din;S. Belachew
Goal : This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods : Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results : DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion : DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials’ data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.
{"title":"DISPEL: A Python Framework for Developing Measures From Digital Health Technologies","authors":"A. Scotland;G. Cosne;A. Juraver;A. Karatsidis;J. Penalver-Andres;E. Bartholomé;C. M. Kanzler;C. Mazzà;D. Roggen;C. Hinchliffe;S. Del Din;S. Belachew","doi":"10.1109/OJEMB.2024.3402531","DOIUrl":"10.1109/OJEMB.2024.3402531","url":null,"abstract":"<italic>Goal</i>\u0000: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. \u0000<italic>Methods</i>\u0000: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. \u0000<italic>Results</i>\u0000: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. \u0000<italic>Conclusion</i>\u0000: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials’ data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10533679","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063428","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}
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