2021 13th Biomedical Engineering International Conference (BMEiCON)最新文献_第2页

Biomechanical analysis of screw configurations on plate fixation in humeral shaft fracture: A Finite element analysis 肱骨骨干骨折钢板内固定螺钉配置的生物力学分析:有限元分析

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745254

Nutnicha Naknual, A. Kwanyuang

{"title":"Biomechanical analysis of screw configurations on plate fixation in humeral shaft fracture: A Finite element analysis","authors":"Nutnicha Naknual, A. Kwanyuang","doi":"10.1109/BMEiCON53485.2021.9745254","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745254","url":null,"abstract":"A standard technique of humeral shaft fracture treatment is plate and screw fixation. Bicortical screws are commonly used for fixing in this procedure. However, several previous studies reported that the tip of bicortical screws could damage the surrounding radial nerve. To avoid the problem of nerve injury, this study evaluated various optimization of screw fixation configurations in the humeral shaft fracture treatment which did not penetrate to the nerve region using finite element analysis. Simplified humerus fracture models were fixed by six different configurations of various screw lengths. All models were tested virtually under axial compression, torsion, and bending conditions. The construct stability was determined by stiffness, relative displacement, and von Mises stress output parameters. The non-inserted screw at the risk area of the radial nerve injury model provided lower stability, when considered by the lowest stiffness in torsion, the highest relative displacement in torsion and bending, compared to other configurations. For the prediction of von Mises stress, the model of all unicortical screws fixation configuration provided the highest magnitude. There was not any difference of the stress occurring when replacing among bicortical, unicortical, and unicortical abutting inserted techniques at the high-risk location. These results revealed an equivalent performance when using either unicortical or unicortical abutting screw fixations at the high-risk position, which might introduce the screw fixation configurations to reduce the risk of radial nerve injury.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134165826","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

Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study 运动意象脑电分类的迁移学习比较研究

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745213

T. Limpiti, Kornthum Seetanathum, Natchaya Sricom, N. Puttarak

{"title":"Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study","authors":"T. Limpiti, Kornthum Seetanathum, Natchaya Sricom, N. Puttarak","doi":"10.1109/BMEiCON53485.2021.9745213","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745213","url":null,"abstract":"Motor imagery (MI) is the cognitive process when a person imagines performing a specific movement of their body. The corresponding electroencephalographic (EEG) signals can be measured externally to the head using scalp electrodes. Such signals have been applied in healthcare brain-computer-interface (BCI) systems, for example, motor rehabilitation and prosthetics control. These systems convert different MI EEG input signals to directives, so their performances depend on the efficiency of the embedded signal classification algorithm. In this paper we investigate the effectiveness of transfer learning in classifying the MI EEG data. The Continuous Wavelet Transform (CWT) is used to construct the scalograms, which serve as the inputs to the deep learning structure. The efficacies of five pre-trained networksAlexNet, ResNet18, ResNet50, InceptionV3 and ShuffleNet-are evaluated on the BCI competition IV data set 2a. Binary (left hand vs. right hand) and four-class (left hand, right hand, both feet, and tongue) classifications are trained and tested using fivefold cross validation. The result indicates that using the CWT with transfer learning models provides very high classification accuracies. The ResNet18 network achieves the best accuracies in both cases at 95.03±2.95% and 91.86±2.90%, respectively. In addition, we examine the effect of different time-frequency features on the classification performance by comparing the scalogram of the CWT and the spectogram of the Short-Time Fourier Transform (STFT) as the inputs. It is found that the CWT is the preferred choice as it is superior to the STFT.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117263895","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

Cardiac Arrhythmia Teletriage using Electrocardiogram 心律失常心电图远程分诊

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745224

T. Limpiti, Thunchanok Chokchaichumnankit, Jirayu Sanguanchom, Natchanon Soyphan, N. Puttarak

{"title":"Cardiac Arrhythmia Teletriage using Electrocardiogram","authors":"T. Limpiti, Thunchanok Chokchaichumnankit, Jirayu Sanguanchom, Natchanon Soyphan, N. Puttarak","doi":"10.1109/BMEiCON53485.2021.9745224","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745224","url":null,"abstract":"Telehealth has become a favorable method for receiving medical care during the COVID-19 pandemic. It reduces physical contact and also benefits those who live a distance from hospitals. In this paper we present a cardiac arrhythmia teletriage using electrocardiogram (ECG). The system consists of a diagnostic algorithm for arrhythmias and an Android application. The diagnostic algorithm can detect five types of cardiac problems–arrhythmia, bradycardia, tachycardia, bradyarrhythmia, and tachyarrhythmia. The Android application is the main communication channel between patients and healthcare providers. The user uploads their ECG and receives a preliminary diagnosis of their heart health via the application. The system notifies the user if it detects any abnormalities. The user can then make an appointment online for further examination at the hospital. The capability of the proposed system is evaluated using four databases from PhysioNet—MITBIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and CU Ventricular Tachyarrhythmia Database. It is found that the algorithm is able to detect abnormal ECG signals with an average accuracy of 82.1%.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122806768","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

Temporal Fusion Transformer for forecasting vital sign trajectories in intensive care patients 用于预测重症监护患者生命体征轨迹的时间融合变压器

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745215

Ratchakit Phetrittikun, Kerdkiat Suvirat, Thanakron Na Pattalung, C. Kongkamol, T. Ingviya, Sitthichok Chaichulee

引用次数: 2

A Smart Platform for Stroke Rehabilitation of the Upper Limb 上肢脑卒中康复智能平台

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745202

Bunditt Wangmuang, Kandis Toompila, Purit Suwanpattana, Unchalisa Taetragool, Tassaneewan Laksanasopin

{"title":"A Smart Platform for Stroke Rehabilitation of the Upper Limb","authors":"Bunditt Wangmuang, Kandis Toompila, Purit Suwanpattana, Unchalisa Taetragool, Tassaneewan Laksanasopin","doi":"10.1109/BMEiCON53485.2021.9745202","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745202","url":null,"abstract":"This work aims to build a platform that may be used to improve and support physical therapy for stroke recovery patients by tracking muscle engagement in the upper limb. Since physical therapy can take a long time, patients often give up if they do not notice any improvement right away. Having a tool that can quickly deliver information on their performance will instill hope in both patients and caregivers. Using an electromyography sensor, we designed and built an Internet of Things (IoT) device for muscle contraction measurement in the upper limb. The signal is measured and sent to the mobile application. The mobile application was developed to acquire data from the IoT device via Bluetooth communication as well as to display data and collect additional information from users. A dashboard was also developed to display processed data so that doctors or caregivers could track the progress of patients. This IoT platform may be utilized to provide a higherquality, affordable solution to stroke survivors, ensuring better health outcomes while also reducing healthcare personnely’s workload.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132703232","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

Gait asymmetry and foot regional analysis on spatiotemporal characteristics in stroke patients 脑卒中患者步态不对称及足部时空特征分析

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745221

Thanita Sanghan, Tulaya Dissaneewate, S. Chatpun

{"title":"Gait asymmetry and foot regional analysis on spatiotemporal characteristics in stroke patients","authors":"Thanita Sanghan, Tulaya Dissaneewate, S. Chatpun","doi":"10.1109/BMEiCON53485.2021.9745221","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745221","url":null,"abstract":"Stroke is a neurological condition caused by damaged brain leading to the impairment of motor function, mental and perceptual disabilities. Gait asymmetry, less plantar pressure on one side of body, is a common disability in stroke patients. The purpose of this retrospective study was to assess the gait characteristics and spatiotemporal features in seven foot regions including hind foot, lateral midfoot, medial midfoot, lateral forefoot, medial forefoot, hallux and other toes. The data set of ten stroke patients was considered and analyzed. The finding indicated that stroke patients took longer time in double support phase and fewer time on single support phase on the affected side compared to healthy people. For spatiotemporal data, the cadence and swing time of affected side were significant higher when compared to non-affected side. Stance time of non-affected side was significant higher against to affected side. Higher symmetry indexes in all parameters underline a common characteristic that the asymmetric gait of the patients during walking. The contact area and contact time under the hind foot and lateral side of the foot region were higher. Peak plantar pressure of hind foot is greater than hallux. The results indicated that the stroke patients had a distinguishable character between affected and non-affected side in all gait parameters due to asymmetric walking. The findings of this study provided the useful information to develop more specific plan for gait rehabilitation to improve a gait of stroke patients.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115386757","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

Pre-trained vs. Random Weights for Calculating Fréchet Inception Distance in Medical Imaging 医学成像中预训练权与随机权的比较

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745214

Jamie A. O’Reilly, Fawad Asadi

{"title":"Pre-trained vs. Random Weights for Calculating Fréchet Inception Distance in Medical Imaging","authors":"Jamie A. O’Reilly, Fawad Asadi","doi":"10.1109/BMEiCON53485.2021.9745214","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745214","url":null,"abstract":"Fréchet Inception Distance (FID) is an evaluation metric for assessing the quality of images synthesized by generative models. Conventionally this involves using an Inception v3 convolutional neural network that has been pretrained to classify everyday color images with the ImageNet dataset. The final classification section of this network is omitted, leaving an efficient feature extractor that outputs an encoded representation of each input image in the form of a 2048 element vector. Difference or similarity between samples of images can then be compared by measuring the distance between the distributions of their corresponding feature representations. Researchers have raised concerns about the utility of FID for evaluating unorthodox images (e.g. medical images) that are unlike those used for model training; suggesting that randomly initialized convolutional neural networks may be more appropriate. The aim of this study was to compare pre-trained and random approaches for evaluating medical images. Robustness to synthetic image distortions (Gaussian noise, blurring, swirl, and impulse noise) and different image types (Xray, CT, fundus, and everyday images) was addressed. Feature representations were converted into two-dimensional space and visualized using t-distributed stochastic neighbor embedding (tSNE) and principal component analysis (PCA). Normalized FID between image classes was substantially larger and more consistent for the pre-trained model. Overall, this suggests that the pre-trained model is preferable to the randomly initialized model for evaluating medical images.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122751135","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

Blood agglutination detection by impedimetric measurement using pencil graphite electrode on a hybrid microfluidic chip 混合微流控芯片上铅笔石墨电极阻抗法检测血液凝集

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745208

Mahdee Samae, Porntipa Suttipiboon, D. Buranapanichkit, Somyot Chirasatitsin

引用次数: 0

Quality Enhancement of Dynamic Brain PET Images via unsupervised learning 通过无监督学习提高动态脑PET图像的质量

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745248

S. Kaviani, Mersedeh Mokri, C. Cohalan, D. Juneau, J. Carrier

{"title":"Quality Enhancement of Dynamic Brain PET Images via unsupervised learning","authors":"S. Kaviani, Mersedeh Mokri, C. Cohalan, D. Juneau, J. Carrier","doi":"10.1109/BMEiCON53485.2021.9745248","DOIUrl":"https://doi.org/10.1109/BMEiCON53485.2021.9745248","url":null,"abstract":"Dynamic Positron Emission Tomography (PET) imaging modality is of great importance in nuclear medicine by measuring quantitive parameters to support clinical decisions. However, limitation in time acquisition due to low count rates causes increased noise levels. Furthermore, conventional denoising methods, including filtration, has the disadvantage of decreasing image resolution. Additionally, methods using supervised deep learning require a big dataset for high accuracy. In this paper, we used unsupervised deep learning to enhance the quality of the dynamic brain PET images by noise reduction while preserving spatial resolution.In this method, ten patients’dynamic 18F-FDG brain PET images were assessed. The Images with 10-sec frame reconstruction were considered noisy images, while 60-sec frame reconstruction was appointed as ground truth. A 3D U-Net architecture with skip connections considering optimized parameters was designed, and training was carried out using static PET and CT images as inputs. The results were compared with Gaussian and NLM filtering methods.The results show the Mean PSNR of 18.35(dB) in our proposed method of using DIP with CT images and 18.29(dB) with static images as priors compared to 16.21 and 16.02 for NLM and Gaussian filtering denoising method respectively. Mean SSIM in our framework is 0.711 in DIP by static PET images and 0.744 by CT images while NLM and Gaussian filtering display values of 0.44 and 0.45.Our proposed algorithm and designed 3D-UNet model is capable of enhancing dynamic PET/CT images quality using only its single static PET and CT images. This unsupervised learning method is time-efficient which could be applied clinically.","PeriodicalId":380002,"journal":{"name":"2021 13th Biomedical Engineering International Conference (BMEiCON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133827309","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: 3

Frequency-specific Flicker Modulates the Cognitive Response to Arithmetic Tasks 频率特定闪烁调节对算术任务的认知反应

2021 13th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2021-11-19 DOI: 10.1109/BMEiCON53485.2021.9745218

Liang Pengcheng, H. Nakatani, T. Yagi

引用次数: 0