IEEE transactions on biomedical circuits and systems最新文献_第9页

A 0.48$^{circ}$ Phase Error 91.1 dB SNR Bioimpedance Measurement IC for Monitoring Cardiopulmonary Diseases 用于心肺疾病监测的0.48°相位误差91.1dB信噪比生物阻抗测量芯片。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-21 DOI: 10.1109/TBCAS.2025.3572374

Jiarun Yuan;Yanxing Suo;Qiao Cai;Hui Wang;Yongfu Li;Yong Lian;Yang Zhao

{"title":"A 0.48$^{circ}$ Phase Error 91.1 dB SNR Bioimpedance Measurement IC for Monitoring Cardiopulmonary Diseases","authors":"Jiarun Yuan;Yanxing Suo;Qiao Cai;Hui Wang;Yongfu Li;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3572374","DOIUrl":"10.1109/TBCAS.2025.3572374","url":null,"abstract":"This article presents a low-power and low phase error bioimpedance (BioZ) measurement IC designed for monitoring cardiopulmonary diseases. To compensate for the phase shift introduced along the signal path by current generator (CG), electrodes and sensor analog front-end (AFE), a novel phase shift calibration logic is proposed. Utilizing this calibration logic, a single-channel in-phase demodulation-based impedance measurement scheme is developed. A noise shaping pseudo-sine wave CG with data-weighted averaging (DWA) is used to minimize modulation harmonics. Fabricated in a 0.18µm CMOS process, the chip occupies 0.73 mm2 and consumes between 52.7 to 97.5 µA current from a 1.8V supply. The CG achieves 74.1 dB SFDR and −70dB THD at 15.5 kHz with a 50µApk stimulation current. The chip achieves 2mΩ/√Hz input-referred impedance noise at 1Hz, 91.1 dB SNR (BW = 4 Hz), 36 kΩ input range and less than 0.48$^{circ}$ phase error (0 − 90$^{circ}$, 1 – 20 kHz). On-body BioZ experiments using a 4-electrode configuration demonstrate clear recordings of Impedance Cardiography (ICG) and respiration signals.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"908-919"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121784","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

Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface 基于电极-组织界面亚阈值查询的高能效自适应神经刺激器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-21 DOI: 10.1109/TBCAS.2025.3570264

Sudip Nag;Aryasree Remadevi;Jin Che;Matvii Prytula;Hanzhang Xing;Hanrui Xing;Xiaoxuan Xiao;Andreas Constas-Malvanets;Hengjia Zhang;Yinghe Sun;Joshua Olorocisimo;Jose Zariffa;Roman Genov

{"title":"Energy-Efficient Adaptive Neural Stimulator With Waveform Prediction by Sub-Threshold Interrogation of the Electrode-Tissue Interface","authors":"Sudip Nag;Aryasree Remadevi;Jin Che;Matvii Prytula;Hanzhang Xing;Hanrui Xing;Xiaoxuan Xiao;Andreas Constas-Malvanets;Hengjia Zhang;Yinghe Sun;Joshua Olorocisimo;Jose Zariffa;Roman Genov","doi":"10.1109/TBCAS.2025.3570264","DOIUrl":"10.1109/TBCAS.2025.3570264","url":null,"abstract":"This paper presents an implantable low-power neural stimulator that generates electrical stimulation pulses based on subject-specific edge-learning of electrode-tissue voltage profiles. The system deploys a low-magnitude constant-current stimulation pulse to create a training dataset, which is subsequently utilized to predict the desired electrode voltage waveforms for higher magnitudes of constant-current stimulation. The predicted waveform dataset has been used to control a custom switched-capacitor output stage, thereby avoiding <italic>Vdriver_transistor <italic>· Istimulation power loss as in the conventional neural stimulator drivers. The proposed system incorporates on-chip learning and prediction implemented within an ultra-low-power microcontroller, which has been optimized for memory and power-constrained implantable environments. The stimulator output stage reduces power loss by up to 20% as compared to dynamic power supply scaling method, and consumes up to 3.63<italic>× lower as compared to conventional constant-current output stages. The intelligent neural interface system has been powered by a wireless inductive energy transfer link and is remotely controlled through a WiFi-based internet network. A custom-developed application interface, compatible with both mobile devices and personal computers, facilitates secure remote adjustments of stimulation parameters. The proposed system has been validated through a combination of <italic>in vivo rat peripheral nerve stimulation, <italic>in vitro saline tests, and benchtop experiments. These results collectively demonstrate the potential to advance future neural implant technologies by enabling intelligence, safety, energy efficiency, and remotely controllable neural organ modulation.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1142-1159"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121873","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

From Wearables to Implantables: Harnessing Sensor Technologies for Continuous Health Monitoring 从可穿戴设备到植入式设备：利用传感器技术进行持续健康监测。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-09 DOI: 10.1109/TBCAS.2025.3568754

Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall

引用次数: 0

Artifact-Tolerant Electrophysiological Sensor Interface With 3.6V/1.8V DM/CM Input Range and 52.3mVpp/${mu}$s Recovery Using Asynchronous Signal Folding 具有3.6V/1.8V DM/CM输入范围和52.3mVpp/μs异步信号折叠恢复的伪影容电生理传感器接口。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-03-06 DOI: 10.1109/TBCAS.2025.3567524

Qiao Cai;Xinzi Xu;Yanxing Suo;Guanghua Qian;Yongfu Li;Guoxing Wang;Yong Lian;Yang Zhao

{"title":"Artifact-Tolerant Electrophysiological Sensor Interface With 3.6V/1.8V DM/CM Input Range and 52.3mVpp/${mu}$s Recovery Using Asynchronous Signal Folding","authors":"Qiao Cai;Xinzi Xu;Yanxing Suo;Guanghua Qian;Yongfu Li;Guoxing Wang;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3567524","DOIUrl":"10.1109/TBCAS.2025.3567524","url":null,"abstract":"In the practical implementations of wearable sensors, motion artifacts with large amplitudes often cause signal chain saturation, significantly degrading biopotential signal integrity. Similarly, rapid stimulation artifacts are inevitable during closed-loop brain stimulation therapy, posing additional challenges for real-time signal acquisition. To address motion and stimulation artifacts with amplitudes reaching hundreds of mV while minimizing information loss, a sensor interface with high input range and fast artifacts recovery capability is essential. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique designed to handle large artifacts events without saturation. The proposed system achieves a 3.6V/1.8V differential-mode/common-mode full-scale input range. Fabricated in a 180nm CMOS process, the prototype chip occupies an area of 0.694mm2 and consumes 12/32.6/51.6<inline-formula><tex-math>$mu$</tex-math></inline-formula>W for recordings without/with single-end/with differential rail-to-rail artifacts. The system demonstrates an average artifacts recovery time of 65.3 <inline-formula><tex-math>$mu$</tex-math></inline-formula>s under 3.6V stimulation artifacts, achieving an average artifacts recovery speed of 52.3mVpp/μs, which is 2.25× larger input range and 3× faster recovery compared to the state-of-the-art.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1160-1174"},"PeriodicalIF":4.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995414","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

IEEE Circuits and Systems Society Information IEEE电路与系统学会信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3538049

引用次数: 0

Guest Editorial: Ultralow-Power Technologies for Edge Computing in Human-Machine Interface Applications 嘉宾评论：人机界面应用中的边缘计算超低功耗技术

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3533805

Elisa Donati;Bo Zhao;Simone Benatti;Andrea Cossettini

引用次数: 0

IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2025.3538047

引用次数: 0

Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle” “利用直接隧道原理设计CMOS ISFET的极低截止频率高通前端”的勘误

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-11 DOI: 10.1109/TBCAS.2024.3411913

Jing Liang;Yuanqi Hu

引用次数: 0

Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design 基于FeRAM交叉棒阵列的手持式光声系统实时成像增强神经形态设计。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-02-04 DOI: 10.1109/TBCAS.2025.3538578

Zhengyuan Zhang;Tiancheng Cao;Siyu Liu;Haoran Jin;Wensong Wang;Xiangjun Yin;Chen Liu;Wang Ling Goh;Yuan Gao;Yuanjin Zheng

{"title":"Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design","authors":"Zhengyuan Zhang;Tiancheng Cao;Siyu Liu;Haoran Jin;Wensong Wang;Xiangjun Yin;Chen Liu;Wang Ling Goh;Yuan Gao;Yuanjin Zheng","doi":"10.1109/TBCAS.2025.3538578","DOIUrl":"10.1109/TBCAS.2025.3538578","url":null,"abstract":"The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in such systems are inferior due to physics and system volume constraints, which limited its wide deployment and application. This paper proposes a novel platform to enhance the imaging quality of handheld PAI system in real time, integrating MultiResU-Net imaging enhancement algorithm with Ferroelectric random-access memory (FeRAM) crossbar array. The FeRAM crossbar array enables in memory computing, which is highly suitable for accelerating deep neural network where extensive matrix multiplications are involved. The hardware implementation of the algorithm is optimized for low-power operation on edge devices, a specifically designed algorithmic strategy is further introduced to accurately simulate the impact of hardware variation on the computation in the array with time complexity of <italic>O(mn). The feasibility and effectiveness of this method are demonstrated through simulation data (synthesized through physical model) and <italic>in vivo data, the experimental results demonstrate more than 10 times of imaging resolution improvement. The execution of neural network inference has been significantly accelerated and can be completed within a few microseconds, fully covering the imaging speed in handheld PAI system and satisfying the real time imaging capability. The whole platform can be integrated into a compact size of 25<inline-formula><tex-math>$times$</tex-math></inline-formula>25<inline-formula><tex-math>$times$</tex-math></inline-formula>20 <bold>cm3, which is a portable system with real time and high resolution imaging capability.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1031-1044"},"PeriodicalIF":4.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545248","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 Active Microchannel Neural Interface for Implantable Electrical Stimulation and Recording 用于植入式电刺激和记录的主动微通道神经接口。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2025-01-27 DOI: 10.1109/TBCAS.2025.3533612

Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous

{"title":"An Active Microchannel Neural Interface for Implantable Electrical Stimulation and Recording","authors":"Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous","doi":"10.1109/TBCAS.2025.3533612","DOIUrl":"10.1109/TBCAS.2025.3533612","url":null,"abstract":"A mm-sized, implantable neural interface for bidirectional control of the peripheral nerves with microchannel electrodes is presented in this paper. The application-specific integrated circuit (ASIC) developed in a 0.18 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m CMOS technology is designed to achieve highly selective, concurrent control of 300-<inline-formula><tex-math>$mu$</tex-math></inline-formula>m-wide groups of small nerve sections. It has <italic>in-situ, high-voltage-compliant (45 V) electrical stimulation and low-voltage (1.8 V) neural recording in each channel. Biphasic stimulus current pulses up to 124 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A, with a 2 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A resolution are generated between 7.4 Hz and 20 kHz frequencies to stimulate and block neural activity. Action potentials are measured across a 10 kHz bandwidth with a variable gain response that ranges up to 72 dB. The neural recording front-end implements a low-power and low-noise biopotential amplifier with an input-referred noise (IRN) of 2.74 <inline-formula><tex-math>$mu$</tex-math></inline-formula>Vrms across the full measurement bandwidth. Automatic detection and reduction of stimulus artifacts is realised using a pole-shifting mechanism with a 1-ms amplifier recovery time. Versatile control of concurrently-operating channels is achieved in a two-channel, 2.31 mm2 interface ASIC using local control that allows up to seven devices to operate in parallel. <italic>In vitro validation of the active interface shows feasibility for closed-loop peripheral nerve control, while <italic>ex vivo analyses of concurrent stimulation and recording demonstrates the measured neural response to electrical stimuli.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1018-1030"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545027","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