2022 14th Biomedical Engineering International Conference (BMEiCON)最新文献

Technical Program and Abstract 技术方案及摘要

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/bmeicon56653.2022.10012106

引用次数: 0

Diagnosis of Heart Failure using High Quality Ballistocardiography and Respiratory Effort Signals: A Pilot Study 使用高质量的弹道心动图和呼吸努力信号诊断心力衰竭:一项初步研究

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012098

Shen Feng, Han Zhang, Andong Bao, Pengtao Sun, Xiaomu Luo, Guanyang Lin, Huan Cen, Sinan Chen, Yuexia Liu, Wenning He, Zhiqiang Pang

{"title":"Diagnosis of Heart Failure using High Quality Ballistocardiography and Respiratory Effort Signals: A Pilot Study","authors":"Shen Feng, Han Zhang, Andong Bao, Pengtao Sun, Xiaomu Luo, Guanyang Lin, Huan Cen, Sinan Chen, Yuexia Liu, Wenning He, Zhiqiang Pang","doi":"10.1109/BMEiCON56653.2022.10012098","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012098","url":null,"abstract":"Purpose: To enable the in-home diagnosis of heart failure (HF) based on morphological features of high quality ballistocardiography (BCG) signals and respiratory effort. Methods: Non-contact vital signs including BCG and respiratory effort signals from 25 subjects (11 HF, 14 non-heart failure (Non-HF)) were collected using a force sensor-based medical equipment. By assessing the recorded BCG signals w.r.t signal quality indexes, a steady-state BCG template is modeled by using consecutive high quality BCG signals, from which morphological features including the amplitude, time, area and energy features of signal wave groups are extracted to distinguish the HF and Non-HF subjects. Results: It is validated that a total 13 morphological features of BCG and respiratory effort signals showed differences between HF and Non-HF subjects. Using typical classifiers for discriminating HF and Non-HF subjects yields the accuracy, sensitivity and specificity of 92%, 80% and 100%. Conclusion: The acquisition and analysis of high quality BCG signals has the potential of identifying HF disease.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130569900","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

Embedded Photographic Tomography Using Raspberry Pi 嵌入式摄影断层扫描使用树莓派

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012104

Rujipas Janthraprasert, C. Pintavirooj

引用次数: 0

Electroporation of Canine MCT Cells and the Examination by Impedance Measurement 犬MCT细胞的电穿孔及阻抗测量

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012078

Patcharapon Kangwarnchokchai, B. Techaumnat, N. Nuntawong, K. Mishima, T. Sharmin, Takuso Aida

引用次数: 0

Prototype – Wearable Device for Detecting Extravasation Using Temperature Sensor and IOT Monitoring System 原型-使用温度传感器和物联网监测系统检测渗漏的可穿戴设备

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10011583

Niti Petranon, Naphatsawan Vongmanee, Nutthanan Wanluk, C. Pintavirooj

引用次数: 0

Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma 利用半监督迁移学习分类太阳斑、恶性斑和恶性斑黑色素瘤

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10011586

Nattapong Thungprue, Nathakorn Tamronganunsakul, Manasanun Hongchukiat, Kanes Sumetpipat, Tanawan Leeboonngam

{"title":"Using Semi-supervised Transfer Learning for Classification of Solar Lentigo, Lentigo Maligna, and Lentigo Maligna Melanoma","authors":"Nattapong Thungprue, Nathakorn Tamronganunsakul, Manasanun Hongchukiat, Kanes Sumetpipat, Tanawan Leeboonngam","doi":"10.1109/BMEiCON56653.2022.10011586","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10011586","url":null,"abstract":"Skin cancer is the most frequent malignancy worldwide, with the number of new cases increasing yearly. Computer-aided diagnosis from skin images has recently become a critical procedure to detect early melanoma stages before becoming metastasis. This study intended to classify three stages of skin cancer: solar lentigo (SL), lentigo maligna (LM), and lentigo maligna melanoma (LMM) using transfer learning and semi-supervised transfer learning of a convolutional neural network algorithm based on VGG-16 and VGG-19. Skin images were obtained from various databases, including labeled and unlabeled data, and were preprocessed using hair removal software and a data balancing technique. The image data were then trained in ten experiments: supervised learning, supervised transfer learning, and semi-supervised transfer learning using VGG-16 and VGG-19 with and without augmentation. The results show that supervised learning gives an accuracy of 0.47. Based on VGG-16 and VGG19 which are comparable in performance, the accuracies increase to 0.72 and 0.72 for supervised transfer learning, and 0.92 and 0.98 for semi-supervised transfer learning, respectively. However, when applying augmentation, the accuracies decrease. Therefore, the use of semi-supervised transfer learning based on VGG-19 gives the best prediction in our study.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123242051","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

CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning 基于深度学习的自动股骨分割模型中附加特征插入的CT数据增强

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012070

Miss Kamonchat Apivanichkul, P. Phasukkit, Dankulchai Pittaya

{"title":"CT Dataset Enhancement using Additional Feature Insertion for Automatic Femur Segmentation Model Based on Deep Learning","authors":"Miss Kamonchat Apivanichkul, P. Phasukkit, Dankulchai Pittaya","doi":"10.1109/BMEiCON56653.2022.10012070","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012070","url":null,"abstract":"This paper proposed to insert additional feature into input datasets (i.e., CT scans) for automatic femur segmentation model, U-Net, with respect to increase the accuracy of model performance. An additional feature is available reference information representing identity on each CT scans and has an effect on results of deep learning model training. In this experiment, choose the left-femur as the target organ, which is common organs-at-risk (OARs) for lower abdominal cancers. The automatic femur segmentation model training was separately executed through two different datasets, one cropped-dataset with additional feature and one original dimension dataset without additional feature. For additional feature, lying posture of patient when entered the CT scanner was selected. The performance results of both trained U-Net models were compered in order to observe the difference of effect. Evaluation results reported that the additional feature could increase an accuracy and precision including support prediction for the left-femur segmentation, with the Dice Similarity Coefficient (DSC) of 61.573% and Intersection Over Union (IoU) of 45.621%, respectively. Specifically, deep learning combining additional feature insertion on cropped-datasets was the novelty in this experiment to effectively segment the left femur.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121184727","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

Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance 表面等离子体共振角度询问机器学习模型的性能分析

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012105

Siratchakrit Shinnakerdchoke, Kitsada Thadson, Suejit Pechprasarn, T. Treebupachatsakul

{"title":"Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance","authors":"Siratchakrit Shinnakerdchoke, Kitsada Thadson, Suejit Pechprasarn, T. Treebupachatsakul","doi":"10.1109/BMEiCON56653.2022.10012105","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012105","url":null,"abstract":"Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of $3.83 times 10^{mathbf{-4}}$ RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model’s accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628540","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

Acute Kidney Injury Detection using Real Human Urine NGAL Biomarker Sensor based on 3D Graphene 基于3D石墨烯的真实人尿NGAL生物标志物传感器检测急性肾损伤

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012085

Netnapa Sittihakote, Sirirat Anutrakulchai, A. Tuantranont, Pobporn Danvirutai, Chavis Srichan

{"title":"Acute Kidney Injury Detection using Real Human Urine NGAL Biomarker Sensor based on 3D Graphene","authors":"Netnapa Sittihakote, Sirirat Anutrakulchai, A. Tuantranont, Pobporn Danvirutai, Chavis Srichan","doi":"10.1109/BMEiCON56653.2022.10012085","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012085","url":null,"abstract":"Acute kidney injury (AKI) is not a specified symptom in the early stages. Frequency of AKI occurrence is highly correlated to Chronic Kidney Disease (CKD). Therefore, development of non-invasive, ultra-sensitive, and highly accurate sensing platform is crucial for early AKI diagnosis. Serum creatinine (SCr) level usually takes 24-72 hours to response to the incident of AKI. Meanwhile, urine Neutrophil Gelatinase-Associated Lipocalin (NGAL) takes only 2 hours to response after the AKI occurrence. In this work, we investigated the use of microporous graphene and dipole-dipole enhancement between graphene/nickel layers to enhance electrode sensitivity for urine NGAL level determination. Selectivity was assured using enzymatic electrochemistry. Once NGAL level was measured, a doctor can diagnose AKI under additional information on patient’s conditions. The result is promising since the detection range was 0.110 to 93.9 ng/ml and the correlation coefficient is 0.8235. The detection covered AKI primary diagnostic cutoff level at 87 ng/ml in urine. The electrochemical immunosensor was able to determine NGAL in Urine with results compared to those provided by the standard ELISA method. This work is a part of development of handheld NGAL determination strip in human urine samples and prepared portable NGAL sensing devices. Despite our investigation’s limitation, the acquired data indicates that non-invasive acute kidney injury detection using actual human urine with graphene foam/nickel-based electrochemical sensor should be further explored as an auxiliary diagnostic tool for AKI.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134540731","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

On the generalized inverse for MRI reconstruction MRI重构的广义逆

2022 14th Biomedical Engineering International Conference (BMEiCON) Pub Date : 2022-11-10 DOI: 10.1109/BMEiCON56653.2022.10012099

Tzu-Hsueh Tsai, Hsin-Chia Chen, Hao Yang, Yu-Chieh Chao, Jyh-Miin Lin, Chih-Ching Chen, Hing-Chiu Chang, Chin-Kuo Chang, Wei-Hsuan Yu, F. Hwang, M. Graves

{"title":"On the generalized inverse for MRI reconstruction","authors":"Tzu-Hsueh Tsai, Hsin-Chia Chen, Hao Yang, Yu-Chieh Chao, Jyh-Miin Lin, Chih-Ching Chen, Hing-Chiu Chang, Chin-Kuo Chang, Wei-Hsuan Yu, F. Hwang, M. Graves","doi":"10.1109/BMEiCON56653.2022.10012099","DOIUrl":"https://doi.org/10.1109/BMEiCON56653.2022.10012099","url":null,"abstract":"Recent studies have suggested that the boundary between data-driven deep-learning non-Cartesian magnetic resonance imaging (MRI) reconstruction methods and conventional optimization-based, iterative reconstruction methods is becoming blurred. For instance, the unrolled iterative reconstruction method can be regarded as a trainable neural network. Another example is that the Moore-Penrose pseudoinverse plays a central role in finding the predefined solution to many imaging processes. However, the application of pseudoinverse in MRI reconstruction was obstructed in clinical imaging, mostly due to the excessive storage required for singular vectors. Since the spatial encoding of MRI is fully determined by the known k-space trajectory, the generalized inverse can be ”iteratively learning in a data-free fashion”, which leads to surprising but realizable properties. To compare our method with other conventional methods, numerical simulations were performed using in vivo MRI. The proposed method leads to nearly equivalent image quality with a much shorter run-time (only 0.68%) than the conjugate gradient (CG) method. We discuss the potential impact of the generalized inverse as a feasible reconstruction method for non-Cartesian MRI.","PeriodicalId":177401,"journal":{"name":"2022 14th Biomedical Engineering International Conference (BMEiCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114788689","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