IEEE transactions on biomedical circuits and systems最新文献

{"title":"A Bioimpedance Spectroscopy Interface for EIM Based on IF-Sampling and Pseudo 2-Path SC Bandpass ΔΣ ADC.","authors":"Alejandro D Fernandez Schrunder, Yu-Kai Huang, Saul Rodriguez, Ana Rusu","doi":"10.1109/TBCAS.2024.3370399","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3370399","url":null,"abstract":"<p><p>This paper presents a low-noise bioimpedance (bio-Z) spectroscopy interface for electrical impedance myography (EIM) over the 1 kHz to 2 MHz frequency range. The proposed interface employs a sinusoidal signal generator based on direct-digital-synthesis (DDS) to improve the accuracy of the bio-Z reading, and a quadrature low-intermediate frequency (IF) readout to achieve a good noise-to-power efficiency and the required data throughput to detect muscle contractions. The readout is able to measure baseline and time-varying bio-Z by employing robust and power-efficient low-gain IAs and sixth-order single-bit bandpass (BP) ΔΣ ADCs. The proposed bio-Z spectroscopy interface is implemented in a 180 nm CMOS process, consumes 344.3 - 479.3 μW, and occupies 5.4 mm² area. Measurement results show 0.7 m Ω/√{Hz} sensitivity at 15.625 kHz, 105.8 dB SNR within 4 Hz bandwidth, and a 146.5 dB figure-of-merit. Additionally, recording of EIM in time and frequency domain during contractions of the bicep brachii muscle demonstrates the potential of the proposed bio-Z interface for wearable EIM systems.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139975131","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":"GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers","authors":"Weiqing Ji;Xingzhuo Guo;Shouan Pan;Fei Long;Tsung-Yi Ho;Ulf Schlichtmann;Hailong Yao","doi":"10.1109/TBCAS.2024.3366691","DOIUrl":"10.1109/TBCAS.2024.3366691","url":null,"abstract":"Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941414","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":"High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers With 2D Oscillator Array and Inter-Sensor Spectrogram Cross-Correlation","authors":"Hao Tang;Suresh Venkatesh;Zhongtian Lin;Xuyang Lu;Hooman Saeeidi;Mehdi Javanmard;Kaushik Sengupta","doi":"10.1109/TBCAS.2024.3367668","DOIUrl":"10.1109/TBCAS.2024.3367668","url":null,"abstract":"In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors—one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u00007 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941415","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":"Miniature Magnetic Resonance Imaging System for in Situ Monitoring of Bacterial Growth and Biofilm Formation.","authors":"Qi Zhou, Shuhao Fan, Ka-Meng Lei, Donhee Ham, Rui P Martins, Pui-In Mak","doi":"10.1109/TBCAS.2024.3369389","DOIUrl":"10.1109/TBCAS.2024.3369389","url":null,"abstract":"<p><p>In situ monitoring of bacterial growth can greatly benefit human healthcare, biomedical research, and hygiene management. Magnetic resonance imaging (MRI) offers two key advantages in tracking bacterial growth: non-invasive monitoring through opaque sample containers and no need for sample pretreatment such as labeling. However, the large size and high cost of conventional MRI systems are the roadblocks for in situ monitoring. Here, we proposed a small, portable MRI system by combining a small permanent magnet and an integrated radio-frequency (RF) electronic chip that excites and reads out nuclear spin motions in a sample, and utilize this small MRI platform for in situ imaging of bacterial growth and biofilm formation. We demonstrate that MRI images taken by the miniature--and thus broadly deployable for in situ work--MRI system provide information on the spatial distribution of bacterial density, and a sequential set of MRI images taken at different times inform the temporal change of the spatial map of bacterial density, showing bacterial growth.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941416","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 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting","authors":"Nader Sherif Kassem Fathy;Ritwik Vatsyayan;Andrew M. Bourhis;Shadi A. Dayeh;Patrick P. Mercier","doi":"10.1109/TBCAS.2024.3366649","DOIUrl":"10.1109/TBCAS.2024.3366649","url":null,"abstract":"This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 and 4.3\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 and 2.8\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1\u0000<inline-formula><tex-math>$ times $</tex-math></inline-formula>\u0000 in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm\u0000²\u0000 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μV\u0000_rms\u0000 and 2.4 μV\u0000_rms\u0000 of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute \u0000<italic>in-vivo</i>\u0000 recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941402","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 3-mV Precision Dual-Mode-Controlled Fast Charge Balancing for Implantable Biphasic Neural Stimulators","authors":"Kai Cui;Yaxue Jin;Xiaoya Fan;Yanzhao Ma","doi":"10.1109/TBCAS.2024.3366518","DOIUrl":"10.1109/TBCAS.2024.3366518","url":null,"abstract":"This paper 5 presents a novel charge balancing (CB) with a current-control (CC) mode and a voltage-control (VC) mode for implantable biphasic stimulators, which can achieve one-step accurate anodic pulse generating. Compared with the conventional short-pulse-injection-based CB, the proposed method could reduce the balancing time and avoid inducing undesired artifact. The CC operation compensates the majority stimulation charge at high speed, while the VC operation guarantees a high CB precision. In order to eliminate the oscillation during the mode transition, a smooth CC-VC transition method is adopted. In addition, a digital auxiliary monitoring loop is introduced against the variations of the tissue-electrode interface impedance during the stimulation process to meet long-term CB requirement. The proposed stimulator has been fabricated in a 0.18 μm BCD process with 10 V voltage compliance, and the measured CB precision is less than 3 mV. The functionalities of the proposed CB have been verified successfully through \u0000<italic>in vitro</i>\u0000 experiments.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941403","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":"FPGA-based Lightweight QDS-CNN System for sEMG Gesture and Force Level Recognition.","authors":"Yusen Guo, Guangyang Gou, Pan Yao, Fupeng Gao, Tianjun Ma, Jianhai Sun, Mengdi Han, Jianqun Cheng, Chunxiu Liu, Ming Zhao, Ning Xue","doi":"10.1109/TBCAS.2024.3364235","DOIUrl":"10.1109/TBCAS.2024.3364235","url":null,"abstract":"<p><p>Deep learning (DL) has been used for electromyographic (EMG) signal recognition and achieved high accuracy for multiple classification tasks. However, implementation in resource-constrained prostheses and human-computer interaction devices remains challenging. To overcome these problems, this paper implemented a low-power system for EMG gesture and force level recognition using Zynq architecture. Firstly, a lightweight network model structure was proposed by Ultra-lightweight depth separable convolution (UL-DSC) and channel attention-global average pooling (CA-GAP) to reduce the computational complexity while maintaining accuracy. A wearable EMG acquisition device for real-time data acquisition was subsequently developed with size of 36mm×28mm×4mm. Finally, a highly parallelized dedicated hardware accelerator architecture was designed for inference computation. 18 gestures were tested, including force levels from 22 healthy subjects. The results indicate that the average accuracy rate was 94.92% for a model with 5.0k parameters and a size of 0.026MB. Specifically, the average recognition accuracy for static and force-level gestures was 98.47% and 89.92%, respectively. The proposed hardware accelerator architecture was deployed with 8-bit precision, a single-frame signal inference time of 41.9μs, a power consumption of 0.317W, and a data throughput of 78.6 GOP/s.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139713549","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":"Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System","authors":"Young-Chan Lee;Doo-Hyeon Ko;Min-Hyeong Son;Se-Hwan Yang;Ji-Yong Um","doi":"10.1109/TBCAS.2024.3363134","DOIUrl":"10.1109/TBCAS.2024.3363134","url":null,"abstract":"This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139704276","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 Low-Power Impedance-to-Frequency Converter for Frequency-Multiplexed Wearable Sensors","authors":"Weilun Li;Junyi Zhao;Yong Wang;Chuan Wang;Shantanu Chakrabartty","doi":"10.1109/TBCAS.2024.3362329","DOIUrl":"10.1109/TBCAS.2024.3362329","url":null,"abstract":"We propose a low-power impedance-to-frequency (I-to-F) converter for wearable transducers that change both its resistance and capacitance in response to mechanical deformation or changes in ambient pressure. At the core of the proposed I-to-F converter is a fixed-point circuit comprising of a voltage-controlled relaxation oscillator and a proportional-to-temperature (PTAT) current reference that locks the oscillation frequency according to the impedance of the transducer. Using both analytical and measurement results we show that the operation of the proposed I-to-F converter is well matched to a specific class of sponge mechanical transducer where the system can achieve higher sensitivity when compared to a simple resistance measurement techniques. Furthermore, the oscillation frequency of the converter can be programmed to ensure that multiple transducer and I-to-F converters can communicate simultaneously over a shared channel (physical wire or virtual wireless channel) using frequency-division multiplexing. Measured results from proof-of-concept prototypes show an impedance sensitivity of \u0000<inline-formula><tex-math>$19.66 ,mathrm{Hz}$</tex-math></inline-formula>\u0000/\u0000<inline-formula><tex-math>$Omega$</tex-math></inline-formula>\u0000 at \u0000<inline-formula><tex-math>$1.1 ,mathrm{k}Omega$</tex-math></inline-formula>\u0000 load impedance magnitude and a current consumption of 128 μ\u0000<inline-formula><tex-math>$mathrm{A}$</tex-math></inline-formula>\u0000. As a demonstration we show the application of the I-to-F converter for human gesture recognition and for radial pulse sensing.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139699165","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":"High Accuracy Localization for Miniature Ingestible Devices Using Mutual Inductance","authors":"Lichen Yao;Sadeque Reza Khan;Guido Dolmans;Jac Romme;Srinjoy Mitra","doi":"10.1109/TBCAS.2024.3361045","DOIUrl":"10.1109/TBCAS.2024.3361045","url":null,"abstract":"This article demonstrates an inductively coupled high-accuracy localization system for miniature ingestible devices. It utilizes an inductance double capacitances-series capacitance (LCC-S) compensation architecture that enables mutual inductance measurement at primary side that is positioned outside the human body and less constrained by power budget and size than the miniature ingestible. Depending on the secondary circuit architecture, only limited and simple cooperative measurements are needed from the ingestible secondary side, which saves power and area in the miniature device. The errors in the system are modeled thoroughly, providing insights about system require-ments for a particular localization accuracy target for efficient design and to identify key building blocks with large influence on overall performance. The model shows that sub-centimeter localization root-mean-square error (RMSE) can be achieved with a modest external ADC (18bit) using three primary coils and three secondary coils. The localization is verified along a complete small intestine tract with realistic dimensions. The proposed model is verified by simulation and experiment showing that at the selected frequency range up to 5 MHz the body has no influence on the accuracy. The use of 0.9% saline as phantom is proposed which guarantees the analysis validity for all body types.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139673918","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}