2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献_第7页

A Time-Domain Current-Mode MAC Engine for Analogue Neural Networks in Flexible Electronics 柔性电子中模拟神经网络的时域电流模式MAC引擎

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919190

M. Douthwaite, F. García-Redondo, P. Georgiou, Shidhartha Das

引用次数: 7

A 1mW Vitals Monitoring System for Asthmatic Patients based on Photoplethysmography 基于光电容积脉搏波的1mW哮喘患者生命体征监测系统

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918724

Afsah Syed, Khadijah Khan, Aleena Ahmad, Muhammad Asad, Wala Saadeh

{"title":"A 1mW Vitals Monitoring System for Asthmatic Patients based on Photoplethysmography","authors":"Afsah Syed, Khadijah Khan, Aleena Ahmad, Muhammad Asad, Wala Saadeh","doi":"10.1109/BIOCAS.2019.8918724","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918724","url":null,"abstract":"The number of patients suffering from asthma, especially in air-polluted regions, has dramatically risen. Thus, the need for a wearable system that is able to monitor the patient's condition is essential. Continuous heart rate (HR) and blood oxygen concentration (SpO2) measurements are crucial for asthmatic patients. This paper presents a low power, portable/wearable system to measure the HR, heart rate variability (HRV) and SpO2 based on Photoplethysmography. The analog front end (AFE) consists of a switched capacitor trans-impedance amplifier and switched capacitor low pass filter to realize a high gain (120dB) and low cut off frequency (10Hz) on-chip. The proposed AFE enables a photodiode input DC current up to 20µA with input-referred current noise of 7.3pA/√Hz. The system is implemented in 180nm CMOS with a die area of 2.6mm2 while consuming 1mW/2.3µW from a 0.5V supply for LED drivers and AFE, respectively. The HR/HRV/SpO2 extraction processor is implemented on FPGA. The maximum absolute error percentage in HR/SpO2 measurements from an experiment involving 21 subjects comes out to be <1%.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 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":"128903034","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: 10

A Wireless Rechargeable Implantable System for Monitoring and Pacing the Gut in Small Animals* 一种用于小动物肠道监测和起搏的无线可充电植入系统*

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919125

Amir Javan-Khoshkholgh, Joseph C. Sassoon, A. Farajidavar

{"title":"A Wireless Rechargeable Implantable System for Monitoring and Pacing the Gut in Small Animals*","authors":"Amir Javan-Khoshkholgh, Joseph C. Sassoon, A. Farajidavar","doi":"10.1109/BIOCAS.2019.8919125","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919125","url":null,"abstract":"Long-term studies of gastrointestinal bioelectrical activity, termed slow-waves (SWs), emphasizes the necessity of implantable systems featured with a reliable wireless power transfer (WPT) link. This paper presents the development and benchtop validation of a system that can wirelessly acquire SWs, modulate the gastrointestinal activity through delivering short and long pulses to the stomach, and be wirelessly recharged through a 13.56 MHz inductive link. The developed system is composed of an implantable unit, an under-the-cage charging unit, and a stationary unit connected to a computer. An application-specific graphical user interface was designed in LabVIEW to process and display the recorded SWs in real time and to configure the stimulation pulses, wirelessly. The system was successfully validated in benchtop settings. The validation of the system showed appropriate frequency response of analog conditioning and digitization resolution to acquire SWs. Moreover, the system was able to deliver electrical pulses at amplitudes up to ±12 mA to a maximum load of 1 kΩ. In addition, the voltage of the 110 mAh LiPo battery at the implantable unit was increased from 3.3 V to 4.2 V in 30 minutes by the charging unit while the recording system was fully functional.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"76 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":"129595306","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

A 32 Input Multiplexed Channel Analog Front-End with Spatial Delta Encoding Technique and Differential Artifacts Compression 采用空间增量编码技术和差分伪影压缩的32输入复用信道模拟前端

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919043

Norberto Pérez-Prieto, R. Fiorelli, José Luis Valtierra, Pablo Pérez-García, M. Delgado-Restituto, Á. Rodríguez-Vázquez

引用次数: 4

Multi-Leads ECG Premature Ventricular Contraction Detection using Tensor Decomposition and Convolutional Neural Network 基于张量分解和卷积神经网络的多导联心电早衰检测

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919049

Tung Hoang, Nicolas Fahier, W. Fang

{"title":"Multi-Leads ECG Premature Ventricular Contraction Detection using Tensor Decomposition and Convolutional Neural Network","authors":"Tung Hoang, Nicolas Fahier, W. Fang","doi":"10.1109/BIOCAS.2019.8919049","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919049","url":null,"abstract":"Premature Ventricular Contraction refers to irregular heartbeat and is one common symptom to several heart diseases. Currently, physiological databases are not only large in volume but also complex in dimensional aspect, so that intelligent systems that can process multi-dimensional data to detect Premature Ventricular Contraction (PVC) are highly needed. In this paper, we propose novel models of combinations of multi-leads ECG from the 12 lead ECG St. Petersburg Arrhythmias database to detect PVCs and optimize the required data pre-processing resources for Convolutional Neural Network(CNN) implemented on wearable devices. Although exhibiting fewer performances than previous works, the proposed method is able to perform automatic features extraction, reduce the CNN complexity and is scalable to be applied to 3-Lead to 16-Lead ECG systems. The combination scenarios include Wavelet fusion method and Tucker-decomposition before CNN is deployed as a classifier. The achieved accuracy to detect PVC for tensor-based feature extraction, the most optimized processing technique, is 90.84% with a sensitivity of 78.60% and a specificity of 99.86%.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"124 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":"133028610","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

Toward a Wearable Data Assimilation Platform 迈向可穿戴数据同化平台

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919195

Philip P. Graybill, B. Gluckman, M. Kiani

引用次数: 2

A Fully Differential Potentiostat Circuit with Integrated Time-based ADCs 集成基于时间的adc的全差分恒电位器电路

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919031

S. Subhan, Sachin Khapli, Yong-Ak Song, S. Ha

{"title":"A Fully Differential Potentiostat Circuit with Integrated Time-based ADCs","authors":"S. Subhan, Sachin Khapli, Yong-Ak Song, S. Ha","doi":"10.1109/BIOCAS.2019.8919031","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919031","url":null,"abstract":"A fully differential CMOS analog front-end circuit for electrochemical sensing is presented. Its first stage is based on a differential transimpedance amplifier (TIA) with an input common-mode feedback to maintain the potentials at the differential working electrodes. The differential output of the first stage is further amplified at the second stage to resolve a smaller range of current differences down to less than 100 fA. The integration capacitor in the first stage and the gain of the second stage are programmable to cover a wide range of the input current ranges up to a range of μA. The proposed potentiostat incorporates two ADC stages, one for each stage. While the outputs of the first stage are compared with a threshold voltage, the whole integration time for both the currents is converted to digital by counters. When difference between the two inputs is too small to be resolved by this ADC, the second stage now becomes a dual-slope ADC to quantize the amplified difference. The ADC in the second stage incorporates a charge/discharge current source, which allows a time-domain measurement of the amplified signal. This method avoids the use of a separate ADC stage, which will consume additional power and area. The proposed potentiostat circuit is designed in 180nm CMOS SOI process technology. Simulation results show that this architecture can accurately measure the differential input current from 1 pA to 1 μA. The minimum input referred integrated current noise in 1 Hz to 10 kHz bandwidth is less than 100 fA with power consumption of 15 μA from ±1-V supply. This fully differential design is adequate to be integrated in an implantable amperometric electrochemical sensing device with low area and power requirements while offering robustness to background current variations.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"27 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":"132485896","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

An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks 基于连续小波变换和卷积神经网络的肌电识别方法

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919230

Lijing Lu, Jingna Mao, Wuqi Wang, Guangxin Ding, Zhiwei Zhang

{"title":"An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks","authors":"Lijing Lu, Jingna Mao, Wuqi Wang, Guangxin Ding, Zhiwei Zhang","doi":"10.1109/BIOCAS.2019.8919230","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919230","url":null,"abstract":"With the increasing development of internet, the security of personal information becomes more and more important. Thus, variety of personal identification methods have been introduced to ensure persons’ information security. Traditional identification methods such as Personal Identification Number (PIN), or Identification tag (ID) are vulnerable to hackers. Then the biometric technology, which uses the unique physiological characteristics of human body to identify user information has come into being. But the biometrics widely used at present such as human face, fingerprint and iris can also be forged and falsified. Thus, the biometric with living body features such as electromyography (EMG) signal is a good method to achieve aliveness detection and prevent the spoofing attacks. However, there are few studies on personal identification based on EMG signal. In this paper, an EMG-based personal identification method using continuous wavelet transform (CWT) and convolutional neural networks (CNN) is proposed. First, the EMG signal is collected from different subjects by MYO armbands. Then, the collected one-dimensional EMG data is transformed into two-dimensional data by using the CWT method. Finally, the CNN algorithm is employed to identify the subjects. Experiments with 21 subjects show that the recognition accuracy of this method can achieve 99.203%, proving the feasibility of using EMG signal for personal identification.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"21 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":"129393399","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: 9

Live Demo: LungSys - Automatic Digital Stethoscope System For Adventitious Respiratory Sound Detection 现场演示:LungSys -自动数字听诊器系统，用于检测不确定的呼吸声音

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918752

Yi Ma, Xinzi Xu, Qing Yu, Yuhang Zhang, Yongfu Li, Jian Zhao, Guoxing Wang

引用次数: 7

Compact Continuous Non-Invasive Blood Glucose Monitoring using Bluetooth 使用蓝牙的紧凑型连续无创血糖监测

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918744

C. Sreenivas, S. Laha

{"title":"Compact Continuous Non-Invasive Blood Glucose Monitoring using Bluetooth","authors":"C. Sreenivas, S. Laha","doi":"10.1109/BIOCAS.2019.8918744","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918744","url":null,"abstract":"A working prototype of a compact wearable affordable continuous non-invasive blood glucose measurement system based on the electromagnetic properties of glucose in blood has been implemented and demonstrated in-vivo with a Bluetooth Low Energy (BLE) based microcontroller kit. When the transmitted BLE signal is passed through the human tissue (fingertip), the proposed non-invasive measurement system uses the power level of the Received Signal Strength Indicator (RSSI) of the received BLE signal to identify the blood glucose level. The novel measurement technique for the non-invasive blood glucose monitoring using RSSI of the BLE signal, allows the prototype to be used in wearable form factor while making it affordable and reliable, something not reported in the past. Oral Glucose Tolerant Test (OGTT) have been performed on fasting human subjects by the intake of 50gm glucose solution. Concurrent measurements of the proposed non-invasive techniques and conventional invasive blood pricking were executed for correlation. The temporal trend observed on OGTT protocol from the proposed non-invasive measurement and the invasing blood pricking strongly correlates, validating the clinical significance of the proposed affordable and wearable prototype.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"11 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":"122136581","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