2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)最新文献_第2页

An In-Silico Study on Effect of KCNQI P535T Mutation in Cardiac Ventricular Cells KCNQI P535T突变对心脏心室细胞影响的芯片研究

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079281

Helan Satish, Ramasubba Reddy

引用次数: 1

Depression Detection on Malay Dialects Using GPT-3 用GPT-3检测马来语方言的抑制性

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079554

Mohamad Farid Mohd Hayati, M. Ali, Ahmad Nabil Md. Rosli

{"title":"Depression Detection on Malay Dialects Using GPT-3","authors":"Mohamad Farid Mohd Hayati, M. Ali, Ahmad Nabil Md. Rosli","doi":"10.1109/IECBES54088.2022.10079554","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079554","url":null,"abstract":"The increasing number of depression cases and lack of manpower in mental healthcare services call for alternatives use of Artificial Intelligence. Natural language processing (NLP) can offer help in the form of early detection and frequent assessments. However, such technology is still limited in the local Malaysian context. In this paper we aimed to deploy Malay NLP in the local mental healthcare services. We performed depression detection on dialectal Malay speeches, specifically Kuala Lumpur, Pahang, and Terengganu dialects. Generative Pre-Trained Transformer-3 (GPT-3), a large language model, was used to perform the task. We experimented with different hyperparameters to test the capability of GPT-3 in few-shot learning. The results obtained are promising considering the size of our dataset that is very limited. We hope to see more studies in the field for the better development of Malay NLP in future. Clinical Relevance— This research tests the possibility of using NLP technology in the local setting of Malaysia. The advancement of this technology will be beneficial in mental healthcare especially in term of the availability of services such as early detection and frequent assessment.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Investigation of a Simplified Photometer Design for the Measurement of Ozone Gas Concentration 一种用于测量臭氧气体浓度的简易光度计设计研究

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079356

Lisa Petani, R. Barth, Lorenz Wührl, I. Sieber, L. Koker, M. Reischl, U. Gengenbach, C. Pylatiuk

引用次数: 0

Correlation between Multimodal Radiographic Features and Preoperative Seizure in Brain Tumor using Machine Learning 基于机器学习的脑肿瘤多模态影像学特征与术前癫痫发作的相关性研究

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079242

Reuben George, L. Chow, K. Lim, Tan Li Kuo, N. Ramli

{"title":"Correlation between Multimodal Radiographic Features and Preoperative Seizure in Brain Tumor using Machine Learning","authors":"Reuben George, L. Chow, K. Lim, Tan Li Kuo, N. Ramli","doi":"10.1109/IECBES54088.2022.10079242","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079242","url":null,"abstract":"Tumor-related epilepsy (TRE) refers to the condition in which primary brain tumors cause recurring seizures. A model that classifies brain tumors as epileptogenic or non-epileptogenic could improve prognosis and treatment methods for TRE. This study aims to identify which MRI sequences and machine learning algorithms (MLAs) could be used to build the most accurate epileptogenic tumor classification model. T1W, T2W, T2W FLAIR and T1W contrast-enhanced scans were acquired from 24 glioma patients, 8 with and 16 without pre-operative epilepsy. A total of 88 features were extracted from the MRI sequences, including tumor location, volume, and several first order textural features derived from gray level co-occurrence matrices (GLCM). Each feature was then used as a predicting variable for 9 MLAs (7 variants of support vector machines (SVMs) and 2 variants of logistic regression) to construct classification models. The top 11 classification models had testing accuracies above or equal to 75%. These models all used SVM variants instead of logistic regression variants. The classification model that used tumor location, and the one that used tumor volume, had testing accuracies of 100% and 87.5% respectively. The 9 other top classification models used GLCM features extracted from the contrast T1W sequence.Clinical Relevance—Our study showed that models which used SVMs were more accurate at classifying tumors by epileptogenicity than those that used logistic regression variants, and contrast T1W radiographic features could also be used in epileptogenic tumor classification models.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thermosensitive and pH-sensitive Drug Release from Taxifolin-Containing Collagen-Acrylic Hydrogels 含taxifolin的胶原-丙烯酸水凝胶的热敏和ph敏药物释放

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079601

O. V. Luneva, Ksenija Bikova, Alexander I. Kashurin, V. V. Motovilov, M. Uspenskaya

{"title":"Thermosensitive and pH-sensitive Drug Release from Taxifolin-Containing Collagen-Acrylic Hydrogels","authors":"O. V. Luneva, Ksenija Bikova, Alexander I. Kashurin, V. V. Motovilov, M. Uspenskaya","doi":"10.1109/IECBES54088.2022.10079601","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079601","url":null,"abstract":"The variety of skin diseases encourage the creation of new coatings, the search for a suitable wound dressing. Significant success has been achieved with hydrogel dressings containing biologically active compounds for the healing of wounds of varying severity. The aim of this work was to obtain taxifolin-containing hydrogels based on acrylic acid and collagen as the basis for a wound dressing. The gelation time, sorption kinetics based on a pseudo-second order model and a mathematical model, as well as the drug release of the obtained samples based on the Korsmeyer-Peppas, Higuchi, Baker-Lonsdale, Hopfenberg, Weibull models, respectively, with variation of solvent pH and sorption temperature have been studied. Sorption characteristics of polymer hydrogel matrix containing 0.5 wt.% taxifolin reaches the values from 25 to 30 g/g, respectively at the various pH solvent. The release of taxifolin in the buffer solutions with pH=5.8 and pH=7.2 carries out in two stages. The best release of the drug substance is observed for the sample containing taxifolin in an alkaline medium and is described by the Hopfenberg model.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127091317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of Radiation Delivery Parameters of Medical Linear Accelerators using Data Analytics Pipeline 用数据分析管道测定医用直线加速器的辐射输送参数

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079318

H. Zubair, Yi-Fei Tan, A. Basaif, A. Oresegun, H. Zin, David Bradley, H. Azhar, Abdul Rashid

{"title":"Determination of Radiation Delivery Parameters of Medical Linear Accelerators using Data Analytics Pipeline","authors":"H. Zubair, Yi-Fei Tan, A. Basaif, A. Oresegun, H. Zin, David Bradley, H. Azhar, Abdul Rashid","doi":"10.1109/IECBES54088.2022.10079318","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079318","url":null,"abstract":"Radiotherapy treatments involve the delivery of sharp radiation pulses of 2 to 4 microseconds duration over typical total periods of 30 to 300 seconds at a rate of up to 400 pulses per second. Recent developments in optical fiber-based radioluminescence/scintillator systems offer radiation-sensing capabilities that capture signals from individual pulses. Each of these signals has unique characteristics which provide insights into the parameters of radiation delivery. Current data acquisition methods commonly rely on hardware-based charge integration methods for radiation dose calculations and have limited utilization of the acquired data for further insights or applications. In this paper, a data analytics pipeline for the extraction and processing of data from a Ge-doped real-time dosimetry system is presented. The data, as obtained for an Elekta Synergy radiotherapy system, is then analyzed for dose distribution, dose-rates determination, and signal clustering. The gathering and processing of such time-resolved data would enable applications such as fault analysis, auto-calibration, and equipment fault prediction in medical radiation facilities in addition to enhancing the routine QA process.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127384287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Classification of Stress Levels during Real-World Driving Tasks 基于深度学习的真实世界驾驶任务压力水平分类

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079333

F.N.H. Mohd Isam, E. Shair, A. R. Abdullah, N. Nazmi, N. Saad

{"title":"Deep Learning-Based Classification of Stress Levels during Real-World Driving Tasks","authors":"F.N.H. Mohd Isam, E. Shair, A. R. Abdullah, N. Nazmi, N. Saad","doi":"10.1109/IECBES54088.2022.10079333","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079333","url":null,"abstract":"Stress has been identified as one of the contributing reasons to vehicle crashes, which cost governments and society a large amount of money in terms of lost lives and productivity. Any alteration that creates physical, emotional, or physiological strain when driving is referred to as driving stress. Driving stress may vary depending on the different road conditions of driving. Understanding drivers’ discontent is one of the most important areas for improving intelligent transportation systems over the existing system. This study presents methods for analyzing and classifying EMG data collected during real-world driving tasks at different driving locations using a convolutional neural network (CNN). In this paper, there are 9 subjects (driver records) of at least 60 minutes duration. Developing CNN from scratch is difficult and it also demands specialized knowledge. As it was previously trained on the ImageNet dataset and could operate effectively with the small amount of training set, pre-trained CNN minimizes the effort of developing models from scratch. CNN is employed in the proposed work to classify driving stress levels by evaluating discriminatory patterns in spectrogram images. In the proposed work, the performance of pre-trained CNN SqueezeNet, GoogLeNet, and ResNet50 in identifying the level of stress (low, medium, and high) is compared. GoogLeNet performed best, with an accuracy of training and validation of 85.71% and 66.67%. Followed by ResNet50 with an accuracy of 71.43% and 66.67% and SqueezeNet with an accuracy of 71.43% and 55.56%, respectively.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130257458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hand Segmentation and Joint Localization in Fluorescence Optical Imaging 荧光光学成像中的手部分割与关节定位

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079311

Richard Fiebelkorn, S. Kupper, E. Gedat, Rachel Escueta, Felix Rothe

引用次数: 0

3D Multimodal k-means and Morphological Operations (3DMKM) Segmentation of Brain Tumors from MR Images 三维多模态k-均值和形态学操作(3dkm)分割脑肿瘤的MR图像

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079510

Reuben George, L. Chow, K. Lim

{"title":"3D Multimodal k-means and Morphological Operations (3DMKM) Segmentation of Brain Tumors from MR Images","authors":"Reuben George, L. Chow, K. Lim","doi":"10.1109/IECBES54088.2022.10079510","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079510","url":null,"abstract":"Tumor segmentation algorithms can aid in prognosis and treatment, and are a better alternative to manual segmentation. This study combined thresholding, morphological operations and k-means segmentation to create a new algorithm called 3D multimodal k-means and morphological operations algorithm (3D-MKM) for segmenting tumors. This algorithm used the fast spoiled gradient (FSPGR), T2 weighted fast spin echo (T2-FSE), T2 weighted fluid-attenuated inversion recovery (T2-FLAIR) and contrast enhanced FSPGR (C-FSPGR) as input images. It adjusted the histograms of each sequence to highlight the tumor regions, then performed a thresholding on the T2FLAIR scan to obtain the region of interest (ROI) mask containing the tumor, edema and surrounding tissue. A multichannel view of the ROI was then made by combining the images from different sequences. The multichannel ROI was then segmented by the k-means algorithm into clusters. Next, the clusters were assembled into the enhancing tumor, non-enhancing tumor and edema masks, and further refined using morphological operations. The 3D-MKM algorithm was tested on 9 datasets. It demonstrated promising results in segmenting the entire lesion, with a Sørensen-Dice similarity coefficient of $0.88 pm 0.05$ and a Hausdorff distance of $12.08 pm 7.07$ mm from ground truth.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122304521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based Analysis of Respiratory Mechanics and Parameters in Critically Ill Mechanically Ventilated Patients 危重机械通气患者呼吸力学及参数的模型分析

2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2022-12-07 DOI: 10.1109/IECBES54088.2022.10079532

Christopher Yew Shuen Ang, Y. Chiew, Xin Wang, M. Nor

{"title":"Model-based Analysis of Respiratory Mechanics and Parameters in Critically Ill Mechanically Ventilated Patients","authors":"Christopher Yew Shuen Ang, Y. Chiew, Xin Wang, M. Nor","doi":"10.1109/IECBES54088.2022.10079532","DOIUrl":"https://doi.org/10.1109/IECBES54088.2022.10079532","url":null,"abstract":"Mechanical ventilation (MV) parameters and other physiological parameters determined from mechanically ventilated respiratory failure patients can be used to estimate patient-centred outcomes. However, there is a lack of analysis of these ventilation parameters continuously to provide useful clinical insight into patients’ disease state, progression as well as evaluation of MV treatment management. This paper presents a model-based analysis on patient-specific respiratory mechanics and MV breath parameters for a clinical observational trial. This study includes 15 patients, with 24 hours of MV data analysed per patient. A total of 385,438 breaths were analysed for this patient cohort. Model-based identification of patient-specific respiratory mechanics yielded a median [interquartile range, IQR] Ers and Rrs of 28.66 cmH2 O/L [24.81-35.92] and 8.86 cmH2 O/L/s [6.11-12.78]. Out of 15 patients, 10 patients have less than 10% of MV parameters (VT, PPlat, PIP, driving pressure, PEEP and MP) falling into the safe ranges described by lung protective strategies and in literature. Analysis of patient arterial blood gas values (ABG) yielded a median PaCO2, PaO2 and pH of 38.0 mmHg [31.7-43.0], 90.2 mmHg [73.9-119.0], and 7.38 [7.32-7.42] respectively. Respiratory mechanics, parameters and haemodynamics are patient-specific and time-varying. Model-based methods enable continuous, concurrent, and real-time monitoring of breath parameters, aiding clinicians in titrating and providing an optimal balance between various ventilator settings while preventing patient harm.Clinical Relevance–Simultaneous and real-time analysis of patient-specific respiratory mechanics and parameters in this clinical observational trial show low compliance rates with respect to lung protective strategies in mechanical ventilation treatment.","PeriodicalId":146681,"journal":{"name":"2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1