2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献_第10页

Detection of Gait Intention with an Insole Device 用鞋垫装置检测步态意图

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919171

Jinwook Lee, C. Kim

{"title":"Detection of Gait Intention with an Insole Device","authors":"Jinwook Lee, C. Kim","doi":"10.1109/BIOCAS.2019.8919171","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919171","url":null,"abstract":"Anticipatory postural adjustments (APAs) refer to the phenomenon in which the human body contracts muscles to minimize postural disturbances and maintain balance before performing a certain action. The purpose of this study was to investigate whether insole devices are capable of detecting walking intentions using APAs. Five healthy subjects participated in the walk test. Ground reaction force (GRF) and center of pressure (COP) were evaluated with an insole device. Electromyography (EMG) sensors and motion capture system was used to capture muscle response and to determine the reference point for the start of walking. And Insole device was evaluated comparing the times of APA occurrence during walking with EMG sensors, and analyzing the consistency of the data. The EMG signal was found to be faster than that of the insole device. Moreover, changes in the signals from both sensors earlier than the movement of the knee on the swing leg side. However, EMG signal showed lower consistency than insole device. Therefore, it is possible to determine the gait intention of a wearer using GRF measured through the insole device rather than using EMG for which signal acquisition is difficult. It is expected that the determined gait intention could be used as a reference time point to create the trigger signal required for control of a walking assistive device.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130370849","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

Severity Analysis of Upper Airway Obstructions: Oesophageal Pressure Versus Snoring Sounds 上气道阻塞的严重程度分析:食道压力与鼾声

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919149

M. Markandeya, U. Abeyratne, R. Sharan, C. Hukins, B. Duce, K. McCloy

{"title":"Severity Analysis of Upper Airway Obstructions: Oesophageal Pressure Versus Snoring Sounds","authors":"M. Markandeya, U. Abeyratne, R. Sharan, C. Hukins, B. Duce, K. McCloy","doi":"10.1109/BIOCAS.2019.8919149","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919149","url":null,"abstract":"Obstructive sleep apnea (OSA) is a sleep related breathing disorder. Identifying severity of airway obstruction is important in OSA severity analysis as well as for treatment success. The apnea hypopnea index (AHI), defined as the total number of full and partial upper airway obstructions per hour, is widely used to diagnose and characterize the severity of OSA. However, recent research shows that AHI provides a crude summary of overnight dynamics of upper airway obstructions. Oesophageal pressure manometry (Pes) is the gold standard method for identifying the severity of individual airway obstruction but, due to the invasive nature, it is less commonly used in sleep laboratories. There is a need for simple automated technology to characterize the severity of airway obstruction. In this work, we propose a method to classify the severity of airway obstruction by analyzing snoring sounds collected through an iPhone 7 smartphone, which requires no physical contact with a subject. For the development of methods, we segmented more than 2000 snoring sound epochs of 5 seconds duration from 7 patients undergoing a polysomnography (PSG) along with Pes. Based on Pes data, we labelled snoring epochs as mild, moderate or severe airway obstruction. We extracted audio features from snoring epochs and used them to train a classifier for multiclass classification. Using 10-fold cross-validation, our methods achieved average accuracy greater than 80% in classifying the severity of airway obstructions. Our results indicate the feasibility of snoring sound in characterizing the severity of airway obstructions. Our non-contact, snoring sound-based technology has the potential to develop into an automatic individual airway obstruction severity analysis system.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132016238","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

Bio-molecular Circuit Design with Electronic Circuit Software and Cytomorphic Chips 利用电子电路软件和细胞形态芯片设计生物分子电路

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918684

Jonathan J. Y. Teo, Jaewook Kim, S. Woo, R. Sarpeshkar

{"title":"Bio-molecular Circuit Design with Electronic Circuit Software and Cytomorphic Chips","authors":"Jonathan J. Y. Teo, Jaewook Kim, S. Woo, R. Sarpeshkar","doi":"10.1109/BIOCAS.2019.8918684","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918684","url":null,"abstract":"We have previously described a technique for rigorously converting arbitrary biological circuits to electronic circuit schematics that represent them exactly and quantitatively [1], [5], [12]. The technique enables us to simulate and model an experimental microbial synthetic microbial amplifier with electronic circuits in Cadence, a widely used integrated-circuit design tool for very-large-scale silicon chips [2]. Our model is in excellent accord with measured biological data for both the closed-loop and open-loop operation of the biological operational amplifier. In addition, because chemical reaction flux and electronic transistor current obey the same Boltzmann laws of thermodynamics, such analog circuit schematics can be emulated rapidly in custom integrated circuit cytomorphic silicon chip hardware [4]–[9] including sophisticated nonlinear, stochastic, non-modular, and dynamical effects. We show that we can rapidly simulate and fit experimental biological data from our synthetic microbial operational amplifier with cytomorphic chips. Since cytomorphic chips are an example of digitally programmable analog chips [10], [11], they are easily amenable to electronic evolution, parameter exploration, and machine learning. The use of industry-standard circuit software and the rapid emulation on digitally programmable cytomorphic silicon chips suggests that biological design of synthetic circuits can be automated onto electronic platforms in the future.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132021190","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

Clustering of Respirations as a Biometric Using ARS and Machine Learning Techniques 基于ARS和机器学习技术的呼吸聚类生物特征分析

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918972

Ryota Takao, Yasutane Okuma, Y. Kamiya

引用次数: 0

Decoding Forelimb Muscle Activity with Local Field Potentials from Mouse Motor Cortex 用小鼠运动皮质局部场电位解码前肢肌肉活动

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919034

Yizuo Ren, Xingchen Ran, Weidong Chen, Shaomin Zhang

{"title":"Decoding Forelimb Muscle Activity with Local Field Potentials from Mouse Motor Cortex","authors":"Yizuo Ren, Xingchen Ran, Weidong Chen, Shaomin Zhang","doi":"10.1109/BIOCAS.2019.8919034","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919034","url":null,"abstract":"Electromyography (EMG) decoding is an important tool to study how the cortex controls the muscles of the limbs. Both spike and local field potentials (LFPs) have been used to decode EMG in previous studies where good performances have been achieved in both rats and monkeys. However, it is a big challenge to carry out studies in mice because only a few electrodes are available for neural recording. In this study, we tried to decode the EMG signal from the biceps brachii muscle of the forelimb by using the LFP signals of their motor cortex. When mice were performing the lever-pressing task, the EMG and 4-channel LFP signals were synchronously collected. Three decoding algorithms, Kalman Filter, General Regression Neural Network (GRNN) and Recurrent Neural Network (RNN), were employed to extract the envelope of EMG signals from the LFP signals. Our results showed that all three algorithms are able to achieve good decoding performance even only a few channels were used. In addition, RNN achieved the best decoding performance among these algorithms, whose CC and MSE were 0.83 and 0.013 respectively.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580444","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

Passive Implantable Wireless Intracranial Pressure Monitoring Based on Near Field Communication 基于近场通信的无源植入式无线颅内压监测

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919064

Wen Jia, Hanjun Jiang, Xiaofeng Yang, Wan Wang, Zhihua Wang, James Jin Wang, Youtu Wu

引用次数: 3

Rapid detection of Klebsiella pneumoniae using an auto-calibrated ISFET-array Lab-on-Chip platform 使用自动校准isfet阵列芯片实验室平台快速检测肺炎克雷伯菌

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919160

J. Rodriguez-Manzano, N. Miscourides, K. Malpartida-Cardenas, Ivana Pennisi, Nicolas Moser, A. Holmes, P. Georgiou

引用次数: 2

A CNN-Based Blind Denoising Method for Endoscopic Images 一种基于cnn的内镜图像盲去噪方法

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918994

Shaofeng Zou, Mingzhu Long, Xuyang Wang, Xiang Xie, Guolin Li, Zhihua Wang

{"title":"A CNN-Based Blind Denoising Method for Endoscopic Images","authors":"Shaofeng Zou, Mingzhu Long, Xuyang Wang, Xiang Xie, Guolin Li, Zhihua Wang","doi":"10.1109/BIOCAS.2019.8918994","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918994","url":null,"abstract":"The quality of images captured by wireless capsule endoscopy (WCE) is key for doctors to diagnose diseases of gastrointestinal (GI) tract. However, there exist many low-quality endoscopic images due to the limited illumination and complex environment in GI tract. After an enhancement process, the severe noise become an unacceptable problem. The noise varies with different cameras, GI tract environments and image enhancement. And the noise model is hard to be obtained. This paper proposes a convolutional blind denoising network for endoscopic images. We apply Deep Image Prior (DIP) method to reconstruct a clean image iteratively using a noisy image without a specific noise model and ground truth. Then we design a blind image quality assessment network based on MobileNet to estimate the quality of the reconstructed images. The estimated quality is used to stop the iterative operation in DIP method. The number of iterations is reduced about 36% by using transfer learning in our DIP process. Experimental results on endoscopic images and real-world noisy images demonstrate the superiority of our proposed method over the state-of-the-art methods in terms of visual quality and quantitative metrics.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228085","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

Analog Neurons with Dopamine-Modulated STDP 多巴胺调节STDP的模拟神经元

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919047

K. Yue, A. C. Parker

引用次数: 1

Real-time HR Estimation from wrist PPG using Binary LSTMs 基于二值LSTMs的腕部PPG实时HR估计

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918726

Leandro Mateus Giacomini Rocha, N. V. Helleputte, Muqing Liu, Dwaipayan Biswas, B. Verhoef, S. Bampi, C. Kim, C. Hoof, M. Konijnenburg, M. Verhelst

引用次数: 9