2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献_第2页

High Frequency Oscillations Detection in Patients Combining Wavelet Decomposition and Back Propagation Neural Network* 基于小波分解和反向传播神经网络的患者高频振荡检测*

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584770

Dakun Lai, Zenghui Kan, Wenjing Chen, Heng Zhang

{"title":"High Frequency Oscillations Detection in Patients Combining Wavelet Decomposition and Back Propagation Neural Network*","authors":"Dakun Lai, Zenghui Kan, Wenjing Chen, Heng Zhang","doi":"10.1109/BIOCAS.2018.8584770","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584770","url":null,"abstract":"Localization of epileptic focus is a worldwide problem. Many studies find that high frequency oscillations (HFOs) seem to be a very specific indicator of the seizure onset zone (SOZ) in recent years. However, it is hard to detect HFOs accurately and many algorithms have a high sensitivity but unsatisfactory specificity. In this study, a novel method combining wavelet decomposition and back propagation (BP) neural network was proposed to increase the specificity of HFOs detection. Three epileptic patients with medically intractable epilepsy were recruited and underwent an individually presurgical monitoring with around 54–90 channels of intracranial electroencephalograph (iEEG). Over 3970 analyzed HFOs events from 420 hours of iEEG data, the sensitivity and the false discovery rate (FDR) of the present approach were 90% and 8.50%, respectively. The obtained results showed that the proposed BP neural network combined with wavelet decomposition could obviously decrease the influence of spikes and high-frequency noise caused by patient motion, which both are similar to HFOs, meanwhile effectively improve the specificity of HFOs detection.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121958171","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: 2

A Half-Shared Transimpedance Amplifier Architecture for High-throughput CMOS Bioelectronics 用于高通量CMOS生物电子学的半共享跨阻放大器架构

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584792

Geoffrey Mulberry, Kevin A. White, Brian N. Kim

{"title":"A Half-Shared Transimpedance Amplifier Architecture for High-throughput CMOS Bioelectronics","authors":"Geoffrey Mulberry, Kevin A. White, Brian N. Kim","doi":"10.1109/BIOCAS.2018.8584792","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584792","url":null,"abstract":"A common problem in single-cell measurement is the low-throughput nature of measurements. Monolithic CMOS microsystems have enabled many parallel measurements to take place simultaneously to increase throughput due to the integration of electrodes and amplifiers into a single chip. This paper explores a CMOS chip containing an array of 1024 parallel transimpedance amplifiers that takes advantage of a “half-shared” operational amplifier architecture. This architecture splits a traditional 5-transistor operational amplifier into two, the inverting half and the non-inverting half. Splitting an amplifier into two allows for the non-inverting half to be “shared” with several inverting halves, reducing the die area required for each individual amplifier. This allows for an increased number of amplifiers to be embedded into the same chip; in this case, 32 amplifiers are able to fit in the same space as 17 traditional 5-transistor operational amplifiers. The amplifiers exhibit low mismatch of 1.65 mV across the entire 1024 amplifier array, as well as high linearity in transimpedance gain. The technique will enable larger arrays to be created in future designs to allow electrophysiologists, among others, access to even higher-throughput measurement tools.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127944905","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

Design and Custom Fabrication of a Smart Temperature Sensor for an Organ-on-a-chip Platform 芯片上器官平台智能温度传感器的设计与定制制造

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584834

R. Ponte, V. Giagka, W. Serdijn

引用次数: 4

Slip suppression in prosthetic hands using a reflective optical sensor and MPI controller 使用反射光学传感器和MPI控制器的假手滑动抑制

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584711

Andrei Nakagawa Silva, Sai Praneeth Reddy Sunkesula, Anna Prach, J. Cabibihan, N. Thakor, A. Soares

引用次数: 4

Predictive trajectory estimation during rehabilitative tasks in augmented reality using inertial sensors 基于惯性传感器的增强现实康复任务预测轨迹估计

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584805

Christopher L. Hunt, Avinash Sharma, Luke E. Osborn, R. Kaliki, N. Thakor

{"title":"Predictive trajectory estimation during rehabilitative tasks in augmented reality using inertial sensors","authors":"Christopher L. Hunt, Avinash Sharma, Luke E. Osborn, R. Kaliki, N. Thakor","doi":"10.1109/BIOCAS.2018.8584805","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584805","url":null,"abstract":"This paper presents a wireless kinematic tracking framework used for biomechanical analysis during rehabilitative tasks in augmented and virtual reality. The framework uses low-cost inertial measurement units and exploits the rigid connections of the human skeletal system to provide egocentric position estimates of joints to centimeter accuracy. On-board sensor fusion combines information from three-axis accelerometers, gyroscopes, and magnetometers to provide robust estimates in real-time. Sensor precision and accuracy were validated using the root mean square error of estimated joint angles against ground truth goniometer measurements. The sensor network produced a mean estimate accuracy of 2.81° with 1.06° precision, resulting in a maximum hand tracking error of 7.06 cm. As an application, the network is used to collect kinematic information from an unconstrained object manipulation task in augmented reality, from which dynamic movement primitives are extracted to characterize natural task completion in N = 3 able-bodied human subjects. These primitives are then leveraged for trajectory estimation in both a generalized and a subject-specific scheme resulting in 0.187 cm and 0.161 cm regression accuracy, respectively. Our proposed kinematic tracking network is wireless, accurate, and especially useful for predicting voluntary actuation in virtual and augmented reality applications.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133988493","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: 7

Proto-Object Based Saliency Model with Second-Order Texture Feature 基于二阶纹理特征的原对象显著性模型

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584749

T. Uejima, E. Niebur, R. Etienne-Cummings

引用次数: 3

Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using Stacked Sparse Autoencoder 基于堆叠稀疏自编码器的半监督阿尔茨海默病分类缺失结构和临床特征输入

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584844

Emimal Jabason, M. Ahmad, M. Swamy

{"title":"Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using Stacked Sparse Autoencoder","authors":"Emimal Jabason, M. Ahmad, M. Swamy","doi":"10.1109/BIOCAS.2018.8584844","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584844","url":null,"abstract":"In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been given paramount importance in the medical field. However, in reality, the input datasets contain lots of missing values due to several factors such as increasing mortality rate, avoiding invasive procedures, and dropping out from the study. In this work, after analyzing the pattern of structural and clinical data from tadpole study in Alzheimer's disease neuroimaging initiative (ADNI) database, it has been found that the unobserved data are not missing completely at random. In view of this fact, with the assumption that the missing data patterns are in blocks, we propose a novel stacked sparse autoencoder based method to assign a value in the missing places and to select the significant structural and clinical features in order to discriminate the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results, it is shown that the proposed imputation algorithm achieves better performance for semi-supervised AD classification in terms of accuracy, sensitivity, and specificity in 5-fold cross validation when compared to the state-of-the-art methods.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133549810","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

An Efficient Hardware Architecture Design of EEMD Processor for Electrocardiography Signal 一种高效的心电图信号EEMD处理器硬件架构设计

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584764

I-Wei Chen, Shang-Yi Chuang, W. Wu, W. Fang

引用次数: 1

A Fully Wireless Implantable Multi-Channel Muscle Stimulator with Closed-Loop Feedback Control 具有闭环反馈控制的全无线植入式多通道肌肉刺激器

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584753

Li Jing Ong, Shih-Chiang Liu, Marshal Dian Sheng Wong, Tafadzwa Sibindi, G. Gammad, C. Tsai, Astrid Rusly, K. Ng, C. Libedinsky, S. Nag, S. Yen

{"title":"A Fully Wireless Implantable Multi-Channel Muscle Stimulator with Closed-Loop Feedback Control","authors":"Li Jing Ong, Shih-Chiang Liu, Marshal Dian Sheng Wong, Tafadzwa Sibindi, G. Gammad, C. Tsai, Astrid Rusly, K. Ng, C. Libedinsky, S. Nag, S. Yen","doi":"10.1109/BIOCAS.2018.8584753","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584753","url":null,"abstract":"We have developed an implantable four-channel high-current biphasic stimulator device for controlling the muscles of the hand, and successfully tested it in a non-human primate (NHP). The charge-balanced stimulator features an external unit which connects to a personal computer via WiFi, and provides wireless power and data control commands to the implant across biological tissue. The stimulator obtained realtime grip force from the hand using a force sensor to perform automated closed-loop control of the stimulation amplitude to ensure that we were able to produce sustained gripping force in the hand of the animal in the event of muscle fatigue. The device was encapsulated using a FDA-compliant biocompatible polymer for reliable long-term study. In-vivo experiments performed after the stimulator (with electrodes embedded in different muscles) had been implanted for one month demonstrated that the system was able to evoke, and automatically maintain, a targeted range of gripping force in the animal's hand. Our results demonstrate the utility of our closed-loop muscle stimulator as a neuroprosthesis for restoring functional hand movements in patients with upper-limb peripheral nerve injuries.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124205657","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: 6

Unsupervised Synaptic Pruning Strategies for Restricted Boltzmann Machines 受限玻尔兹曼机的无监督突触剪枝策略

2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2018-10-01 DOI: 10.1109/BIOCAS.2018.8584839

Surabhi Kalyan, Siddharth Joshi, Sadique Sheik, B. Pedroni, Gert Cauwcnbcrghs

引用次数: 1