2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)最新文献_第2页

Frame Work For EEG Based Emotion Recognition Based On Hybrid Neural Network 基于混合神经网络的脑电情感识别框架

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445130

Jancy Jose, A. J

{"title":"Frame Work For EEG Based Emotion Recognition Based On Hybrid Neural Network","authors":"Jancy Jose, A. J","doi":"10.1109/ICBSII51839.2021.9445130","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445130","url":null,"abstract":"In recent years, there were many attempts to classify human emotions based on corporeal signals including ECG, EEG, EMG. EEG based emotion classification is more accurate because it cannot be tainted by subjects' will. The recent development in CNNs has made it easier to systematically extract features from EEG easily. But again, the traditional CNNs fail to comprehend the multi-channel aspect of EEG. In this work, a simple and efficient pre-processing method by considering baseline signals is proposed to enhance the accuracy of recognition and we proposed a hybrid neural network which combines Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) to identify human emotions by extracting spatial and temporal features from raw EEG stream effectively. In CNN, the 1D EEG sequence is then efficiently converted into a 2D frame structure. In order to extract the inter-channel connection between physically adjacent EEG signals, the CNN module is used, and to extract the contextual information, the LSTM module is used. Using this logic, we were able to create a deep learning model which predicts arousal and valence emotions with 86.98% and 85.82% accuracy respectively.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126097562","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: 4

Length of Stay Prediction in Acute Intensive Care Unit in Cardiothoracic Surgery Patients 心胸外科病人急性重症监护病房的住院时间预测

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445145

N. Mollaei, A. Londral, Cátia Cepeda, Salomé Azevedo, J. Santos, P. Coelho, J. Fragata, H. Gamboa

引用次数: 1

Multimodal Emotion Recognition Using Different Fusion Techniques 基于不同融合技术的多模态情绪识别

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445146

Gokul Subramanian, Niranjan Cholendiran, Kotapati Prathyusha, Noviya Balasubramanain, J. Aravinth

引用次数: 12

Computational Approach to guide Mind Controlled Robotic Arm using BCI – A Review 基于脑机接口(BCI)的思维控制机械臂引导计算方法综述

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445182

M. Anna Latha, E. Sathish, Florence Gnana Poovathy

引用次数: 2

COVID-SEGNET: Diagnosis of Covid-19 Cases on Radiological Images using Mask R-CNN COVID-SEGNET:基于掩膜R-CNN的放射图像诊断Covid-19病例

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445190

A. Kundu, C. Mishra, Saurabh Bilgaiyan

{"title":"COVID-SEGNET: Diagnosis of Covid-19 Cases on Radiological Images using Mask R-CNN","authors":"A. Kundu, C. Mishra, Saurabh Bilgaiyan","doi":"10.1109/ICBSII51839.2021.9445190","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445190","url":null,"abstract":"The novel coronavirus (COVID-2019) pandemic has caused a catastrophic effect on health and global economy. Early screening and diagnosis of COVID-19 pneumonia are the critical steps to stop the further spread of the virus. The most common standard for confirming the virus relies on RT-PCR tests. This method generates false-negative results if there is limited viral load. Recent radiological findings suggest that the distinct distribution of ground-glass opacities (GGOs), which are found on certain parts of lungs, can determine the status of the infection among patients. As a complement to RT-PCR, Computed tomography (CT) can be used for diagnosing COVID-19. In this study, the authors have described a Mask R-CNN (region-based convolution neural network) approach for the detection of the ground glass opacities (GGOs) in chest CT images of COVID-19 infected persons. The proposed approach provides an accuracy of 98.25% during instance segmentation. Therefore, the authors believe this proposed method will aid health professionals to fasten the screening and validation of the initial assessment towards COVID-19 patients.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132036416","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

Modelling and Simulation of High Flux Hemodialyzer Membranes of Different Porosities to Identify The Optimal Membrane Design 不同孔隙率高通量血液透析器膜的建模与仿真，以确定膜的最佳设计

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445178

Ahana Fatima Alex, R. Vinoth, R. Dudhe

引用次数: 1

Retinal Vessel Segmentation with Slime-Mould-Optimization based Multi-Scale-Matched-Filter 基于黏菌优化的多尺度匹配滤波视网膜血管分割

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445135

S. Kadry, V. Rajinikanth, R. Damaševičius, D. Taniar

{"title":"Retinal Vessel Segmentation with Slime-Mould-Optimization based Multi-Scale-Matched-Filter","authors":"S. Kadry, V. Rajinikanth, R. Damaševičius, D. Taniar","doi":"10.1109/ICBSII51839.2021.9445135","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445135","url":null,"abstract":"Even though a number of sensory organs are existing, eye plays a necessary role in the sensory system; which converts the incident light into meaningful visual information. If any abnormality arises in eye, then the whole sensory system gets stressed. The disease in eye is due to injury, infection and ageing and the untreated eye disease will lead to vision loss. The proposed research aims to propose a Computer-Aided-Procedure (CAP) to extract the blood-vessel section from Digital-Fundus-Image (DFI). In order to accomplish this task, a Multi-Scale-Matched-Filter (MSMF) is designed using the Slime-Mould-Optimization algorithm. In this work, the necessary test images are collected from the benchmark DRIVE and CHASE_DB1 dataset. After extracting the blood-vessel using the MSMF, an examination among extracted vessel and the Ground-Truth (GT) image is executed and the Image–Performance-Values are separately computed for each database. The attained result with this CAP confirms that the attained Jaccard, Dice and Accuracy of proposed approach is better compared to similar existing approaches in the literature.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128461424","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: 8

Texture Analysis of Reaction Diffusion Level Set Evolution Of Multiple Sclerosis Lesions In Brain MR Images 多发性硬化症脑MR图像反应扩散水平集演化的织构分析

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445141

Pandian Ambairam, Udhayakumar Ganesan

{"title":"Texture Analysis of Reaction Diffusion Level Set Evolution Of Multiple Sclerosis Lesions In Brain MR Images","authors":"Pandian Ambairam, Udhayakumar Ganesan","doi":"10.1109/ICBSII51839.2021.9445141","DOIUrl":"https://doi.org/10.1109/ICBSII51839.2021.9445141","url":null,"abstract":"Multiple sclerosis (MS) is a chronic idiopathic disease that results in multiple areas of inflammatory demyelination in the Human Central Nervous System (CNS) and it causes various troubles with mobility and upper limb function, bladder, vision, speech, swelling, and cognition. Hence, we need early detection and accurate identification of pathological changes of disease progression for MS patients. In this work, automated segmentation method of Reaction-Diffusion Level Set Evolution (RDLSE) is employed for T2-Weighted (T2W) Magnetic Resonance images. Axial view of MR images of Various MS Patients based on Extended Disability Status Scale (EDSS) of the University Medical Centre Ljubljana are used in this analysis. The segmented output image accuracy is validated with Ground Truth images. Texture analyses are employed to Segmented MS region in MR images to improve the accuracy and identification of differences in brain tissue structure. Results show RDLSE methods are able to segment the small MS lesions even in the presence of heterogeneous intensity values and segmented output image feature value show the MS lesion load for various EDSS values in MS patient T2W MR Images.","PeriodicalId":207893,"journal":{"name":"2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121795771","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

A new lead system for improved recording of P-wave amplitude and its significance with existing optimal leads 一种改进纵波振幅记录的新型引线系统及其在现有最佳引线下的意义

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445183

N. Venkatesh, A. Bharadwaj, J. Sivaraman, B. Dhananjay

引用次数: 0

U-Net Supported Segmentation of Ischemic-Stroke-Lesion from Brain MRI Slices U-Net支持的脑缺血-脑卒中损伤MRI切片分割

2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII) Pub Date : 2021-03-25 DOI: 10.1109/ICBSII51839.2021.9445126

S. Kadry, R. Damaševičius, D. Taniar, V. Rajinikanth, I. A. Lawal

引用次数: 23