2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献

{"title":"Live Demonstration : A Portable Multi-Ion Platform with Integrated Microfluidics","authors":"Chiara Cicatiello, Nicolas Moser, M. Boutelle, P. Georgiou","doi":"10.1109/BIOCAS.2019.8919106","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919106","url":null,"abstract":"A novel microfluidic analysis platform is demonstrated for the real-time monitoring of traumatic brain injury (TBI) patients in intensive care. The system uses an on-chip array of Complementary Metal-Oxide-Semiconductor (CMOS) sensors implemented in an unmodified process to perform ion imaging. Specificity to several ionic species is achieved by depositing ion-selective membranes at the surface of the chip. Image processing techniques on the sensing array data enable the visualisation of the pattern of neurochemical changes caused by the evolving injury.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"155 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":"128487337","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}

{"title":"Optimal Ultrasonic Pulse Transmission for Miniaturized Biomedical Implants","authors":"Z. Kashani, M. Kiani","doi":"10.1109/BIOCAS.2019.8918704","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918704","url":null,"abstract":"This paper presents optimal ultrasonic pulse transmission that could be used for data transmission to/from millimeter-sized biomedical implants in general or the selfimage-guided ultrasonic (SIG-US) wireless power transfer (WPT), which we have recently proposed. To reduce the power consumption of the implant, data bits can be applied as sharp pulses across the implant’s ultrasonic transducer, resulting in a signal in the form of a short ringing across the external unit transducer (or array). In SIG-US WPT, also short pulses are transmitted by the implant periodically. The relative delays in received signal by each external transducer in an array are then used to guide the beamformer for optimal steering of the power beam towards the implant. Through simulations and measurements, the effect of transmitted number of pulses (Np) on the amplitude of the received signal (VDRx) has been studied, which is vital for low-power robust transmission. In measurements with two identical disc-shaped transducers (spaced by d = 50 mm inside water) with the diameter of 1.2 mm and thickness of 0.9 mm (series resonance frequency of ~1.3 MHz), VDRx increased almost linearly with Np (50% duty cycle with the period of 1/1.3 MHz ~ 770 ns) up to Np = 5 beyond which VDRX increase was negligible and started to saturate at Np = 10. In the SIG-US context, it was also shown in measurements that for up to 25 mm misalignment of the implant at d = 50 mm, the relative delay in receiving 4 pulses increased from 40 ns to 4.75 μs, which are quite measurable.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"391 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":"115951526","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}

{"title":"Characterisation of a multi-channel multiplexed EMG recording system: towards realising variable electrode configurations","authors":"K. D. Jager, Michael Mentink, H. Lancashire, Y. Al-Ajam, Stephen Taylor, A. Vanhoestenberghe","doi":"10.1109/BIOCAS.2019.8918710","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918710","url":null,"abstract":"An implantable EMG amplifier with a novel multiplexed frontend for in vivo selection of optimal electrode configurations, was designed using commercially available components. The multiplexers are advantageous as optimal electrode configuration are not known before implantation. The system has 6 ADC recording channels (ADS1298 biopotential amplifier, 2 kHz sampling frequency, 16-bit resolution), and three 8 × 8 multiplexer arrays (ADG2188), 2 channels per MUX. The system was characterised by measuring input impedance (5.8 MOhm) and frequency response (CMRR 49.0 dB; SNR 51.4 dB). EMG recordings from implanted epimysial electrodes showed lower signal quality (13.5 dB) compared with a commercial EMG recorder (19.5 dB), nonetheless the signals appeared suitable for myoelectric control applications. An implantable version of the EMG recorder, housed within a hermetically sealed ceramic package, should improve signal quality.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"332 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":"115958950","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}

{"title":"Shifting Clock Jitter and Phase Interval for Impulse-Radar-Based Breast Cancer Detection","authors":"A. Toya, T. Kikkawa, Y. Masui, M. Sugawara, Hiroyuki Ito, Tomoaki Maeda, M. Ono, Y. Murasaka, T. Imamura, A. Iwata","doi":"10.1109/BIOCAS.2019.8919142","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919142","url":null,"abstract":"An equivalent time sampling circuit has been developed for impulse-radio ultra-wideband radar systems. The received signals are digitized by a shifting clock. The accuracy of the clock that affects the sampling interval of analog-to-digital converter (ADC) is measured in terms of jitter and delay time. It is found that the delay time is fluctuated in every 64 times which is related to the multiplexer operation. The jitter which is calculated from the measured phase noise decreases from 85dBc/Hz to -110dBc/Hz in the low offset-frequency. It is also found that the noise from ADC degrades the some part of sampling timing and the error source is identified.","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":"116705735","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}

{"title":"Noise CMOS ISFETs Using In-Pixel Chopping","authors":"Kangping Hu, Xiaoyu Lian, Shanshan Dai, J. Rosenstein","doi":"10.1109/BIOCAS.2019.8919025","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919025","url":null,"abstract":"Ion sensitive field effect transistors (ISFETs) are CMOS-compatible pH sensors which have been adopted for a wide range of biochemical sensing applications. Drift and low-frequency noise are perennial challenges for these small charge-sensitive devices. However, ISFET designers have often avoided the common circuit solution of chopper stabilization due to understandable concern that the switching will disturb the sensing gate. Here we introduce a new configuration which modulates the source and drain voltages of the ISFET, reducing 1/f noise and drift with negligible disturbance of the sensing gate. We experimentally demonstrate this in-pixel chopping scheme with titanium nitride ISFETs in 180-nm CMOS technology. Using in-pixel chopping, the circuit achieves a three-fold reduction in drift along with suppression of 1/f noise.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"50 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":"117291822","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}

{"title":"Live Demonstration: An AIoT Wearable ECG Patch with Decision Tree for Arrhythmia Analysis","authors":"Yu-Jin Lin, Chen-Wei Chuang, Chun-Yueh Yen, Sheng-Hsin Huang, Ju-Yi Chen, Shuenn-Yuh Lee","doi":"10.1109/BIOCAS.2019.8919138","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919138","url":null,"abstract":"This live demonstration presents a novel electrocardiogram (ECG) monitoring system with artificial intelligence of things (AIoT) design, which is based on decision tree (DT). The proposed system includes a front-end device and a software system. The front-end device includes a solar charging circuit, a wireless charging circuit and an analog front-end circuit. First, the solar charging block takes a dye-sensitized sorlar cell from National Chung Hsing University, which is responsible for energy harvesting under indoor illuminance. Second, the wireless charging block gives users an additional charging method to meet the demand of long-term monitoring. Third, the analog front-end block is composed of the ECG-sensing circuit, the microcontroller unit (MCU) and the Bluetooth Low Energy (BLE) module. The ECG-sensing circuit is based on single lead measurement, and it includes level shifter units, differential amplifiers and filters. The circuits are implemented by the commercial components and realized by self-designed print circuit boards (PCB). On the other hand, this paper takes ARM Cortex M4 and BLE 5.0 as the solution for data transmitting and encoding. All the above circuits are integrated into one PCB, and the prototype is designed by 3D-printer. The whole ECG Patch's size is 86.6 mm* 50 mm* 20 mm. The software system includes an application (APP) with DT algorithms, a cloud server is available to execute DT training and to provide a user interface for supporting telemedicine. This paper proposes a simplified DT model, which can be realized in APP based on iOS system. The APP classifies real-time ECG data into different arrhythmias, and the delay latency is 500 ms in average. Meanwhile, according to 4G or Wi-Fi, the collected ECG data are uploaded to the cloud server for training DT. Then, the coefficients of the pre-trained DT will be sent back to the APP for updating. The accuracy is 98.7%. By the proposed AIoT system, doctors and users can realize the task of long-term ECG monitoring, which is valuable for cardiovascular disease diagnosis. Also, doctors can assist users instantly by the web user interface, to meet the demands of telemedicine. The proposed AIoT system has been conducted human trials in National Cheng Kung University Hospital. The power consumption of the proposed front-end device is 8.25 mW, and it can be continuously used up to 32 hours with a 120 mAh lithium-ion battery. If it turns on solar charging, the device can continually operate, until the solar cell is dead.","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":"114391783","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}

{"title":"EIT-CDAE: A 2-D Electrical Impedance Tomography Image Reconstruction Method Based on Auto Encoder Technique","authors":"Yue Gao, Yewangqing Lu, Hui Li, Boxiao Liu, Yongfu Li, Mingyi Chen, Guoxing Wang, Y. Lian","doi":"10.1109/BIOCAS.2019.8918979","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918979","url":null,"abstract":"Electrical Impedance Tomography is considered to be an alternative substitution to CT and MRI technologies as it is a non-invasive, safe medical imaging technology, and free of ionizing or heating radiation. Similar to CT and MRI technologies, reconstructing a two-dimensional EIT image is also considered an ill-posed and non-linear inverse problem, where the image quality is highly sensitive to the measurement data, and often random noise artifacts appear in the image with the different non-linear algorithms. Therefore, in this work, we have proposed a new EIT image reconstruction algorithm based on the convolution denoising autoencoder (CDAE) deep learning algorithm. Our EIT-CDAE used a convolutional neural network in the encoder and decoder network. From our experimental data using phantom data, our EIT-CDAE model has reconstructed a better EIT image quality, removing any noise artifacts, making it more robust compared to the conventional stacked autoencoder and traditional non-linear algorithms. The source code is available in the github: https://github.com/yongfu-li/eit-cdae-algorithm","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"121 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":"127427988","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}

{"title":"Transfer Component Analysis to Reduce Individual Difference of EEG Characteristics for Automated Seizure Detection","authors":"Xinyu Jiang, Ke Xu, Wei Chen","doi":"10.1109/BIOCAS.2019.8919154","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919154","url":null,"abstract":"About 50 million people worldwide are suffering from epilepsy. Automated epileptic seizure detection has been widely studied so far, which brings great support to patients’ health and quality of life. However, a well-trained patient-specific seizure detection model usually shows poor performance when classifying electroencephalogram (EEG) signals of a new patient. This may due to the huge individual differences in physiological signals. More specifically, the distribution of feature space across patients differ greatly from each other. In this study, we firstly extracted highly separable features from dual-tree discrete wavelet parameters. Then we employed transfer component analysis (TCA) to construct a latent feature subspace. Features of different patients share a similar distribution when projected onto the latent subspace, so that a model trained on existing patients can be applied to a new one. Through validation on an open access scalp EEG dataset which contains EEG signals of 24 epileptic patients, the model trained in TCA feature subspace outperforms that trained in original feature space when applied to new patients excluded from the training set.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"81 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":"126231500","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}

{"title":"A Single Channel EEG-based All AASM Sleep Stages Classifier for Neurodegenerative Disorder","authors":"S. Zamin, Muhammad Awais Bin Altaf, Wala Saadeh","doi":"10.1109/BIOCAS.2019.8918738","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918738","url":null,"abstract":"Sleep stages classification is an effective tool for the diagnosis and treatment of neurodegenerative disorders. This paper presents the first non-invasive electroencephalograph (EEG)-based processor for classifying all the sleep stages implemented on hardware. It utilizes a single EEG channel and multi-machine-learning classifiers to form a home-based polysomnography. These multiple one-vs-one binary Linear Support Vector Machine (LSVM) classifiers are combined to classify all the sleep stages using two features only. To implement the desired features efficiently on hardware, an exponent-eliminate (EE) Split-Radix 256-point FFT is proposed that decreases the area by 60% compared to the conventional design by avoiding the majority of complex floating-point multiplications and divisions. The proposed all sleep stages classification system is implemented using 180nm CMOS process and experimentally verified using FPGA based on the EEG recordings of 197 patients from Physionet Sleep database. It utilizes a miniaturized active area of 0.32mm2 and achieves a Cohen Kappa score of 0.847 while consuming 0.7µJ/classification.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"19 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":"126591341","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}

{"title":"Live Demonstration: Low Power Embedded System for Event-Driven Hand Gesture Recognition","authors":"Andrea Mongardi, Fabio Rossi, P. Ros, A. Sanginario, M. R. Roch, M. Martina, D. Demarchi","doi":"10.1109/BIOCAS.2019.8919184","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919184","url":null,"abstract":"This demonstration presents a low power embedded system to classify hand movements. The surface ElectroMyo-Graphic (sEMG) signals acquired from the forearm are preprocessed using the Average Threshold Crossing (ATC) event-driven technique, which heavily reduces hardware complexity and power consumption. The quasi-digital output is sent to an ultra low power microcontroller, which implements a fully-connected Neural Network (NN). A small Arduino-based tank is used to demonstrate the real-time behavior of the system and to show the correctness of the predicted gestures1.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"15 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":"121848927","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}