IEEE transactions on biomedical circuits and systems最新文献_第2页

SPIRE: A 28nm Memory-Efficient Multi-Reservoir LSM Accelerator for Adaptive and Flexible Time-series Classification. SPIRE：一种用于自适应和灵活时间序列分类的28nm内存高效多库LSM加速器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-02-25 DOI: 10.1109/TBCAS.2026.3668521

Dario Fernandez-Khatiboun, Simon Richter, Yasser Rezaeiyan, Maryam Sadeghi, Corentin Piozin, Farshad Moradi

{"title":"SPIRE: A 28nm Memory-Efficient Multi-Reservoir LSM Accelerator for Adaptive and Flexible Time-series Classification.","authors":"Dario Fernandez-Khatiboun, Simon Richter, Yasser Rezaeiyan, Maryam Sadeghi, Corentin Piozin, Farshad Moradi","doi":"10.1109/TBCAS.2026.3668521","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3668521","url":null,"abstract":"Spiking Neural Networks are widely studied for their brain-inspired ability to process sequential information, yet their memory limitations often hinder the extraction of long-term dependencies. Reservoir computing, and in particular liquid state machines (LSM), has gained attention within this context for its ability to separate the recurrence and classification components into a recurrent reservoir liquid followed by a feedforward layer. However, existing LSM hardware suffers from significant design trade-offs, including large memory demands or performance degradation due to restrictive connectivity and weight precision. Inspired by these findings, we introduce SPIRE, a compact 1.13mm2 core area, fully digital multi-reservoir LSM with online learning adaptation. Implemented in TSMC 28nm CMOS technology, SPIRE is a memory-efficient multi-reservoir LSM tailored for time-series classification and edge deployment. By organizing up to eight reservoir ensembles into four parallelized cores, SPIRE enhances synaptic density and computational efficiency. Furthermore, SPIRE leverages on-the-fly generation of reservoir weights, reducing even further the memory footprint while supporting both sequential and parallelized dual operation modes. Benchmark results demonstrate that these design choices improve SPIRE's synaptic density by up to 18.46× over prior works. SPIRE achieves 3.56 GSOPs/mm2 with just 4.91 pJ/SOP in sequential inference and up to 76.05 GSOPs/mm2 with 0.1 pJ/SOP in parallel configurations running at 55 MHz and 0.55 V.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147292142","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

Real-Time On-Chip Time-of-Flight Detection for US Time Reversal Localization of Implants. 芯片上的实时飞行时间检测用于植入物的US时间反转定位。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-02-17 DOI: 10.1109/TBCAS.2026.3665787

Anirudh Kumar Parag, Bogdan C Raducanu, Stefano Stanzione, Patrick Hendrickx, Oguz Kaan Erden, Chris Van Hoof, Nick Van Helleputte, Georges Gielen

{"title":"Real-Time On-Chip Time-of-Flight Detection for US Time Reversal Localization of Implants.","authors":"Anirudh Kumar Parag, Bogdan C Raducanu, Stefano Stanzione, Patrick Hendrickx, Oguz Kaan Erden, Chris Van Hoof, Nick Van Helleputte, Georges Gielen","doi":"10.1109/TBCAS.2026.3665787","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3665787","url":null,"abstract":"Time-reversal (TR) localization using ultrasound (US) has emerged as a promising method to localize mm-sized deep-tissue implantable medical devices (IMDs) without requiring precise prior positional knowledge. While in-vitro studies using commercial off-the-shelf (COTS) systems have validated its efficacy, practical deployment demands significant system miniaturization to enable low-power, wearable implementations. To this end, this paper presents a proof-of-concept ASIC architecture comprising 45 channels, each featuring an integrated receive and transmit chain. A key component is a novel peak-instance-detection (PID) circuit that detects the time of flight (ToF) of incoming US signals in real time via dynamic thresholding and that simultaneously digitizes the result, eliminating the need to store full waveforms. This enables TR with minimal power and memory overhead. The ASIC is implemented in the TSMC 130-nm BCD+ technology. Electrical measurements confirm accurate ToF detection from recorded US backscatter signals. The PID reduces the per-channel receive power to 100 µW - achieving a 2000X reduction over COTS alternatives. Memory usage is also minimized, requiring only 540 bits to store ToFs across all channels - 746X lower than COTS approaches. Furthermore, the on-chip ToF digitization simplifies the TR computation, reducing it to a digital subtraction operation, in contrast to the complex convolutions typically employed. Finally, in-vitro experiments in heterogeneous scattering media using a 3D-printed human rib phantom demonstrate a 22X power efficiency improvement over unfocused US transmission, while matching the localization performance of MATLAB-based COTS implementations. These results highlight the feasibility of the proposed architecture as a miniaturized platform for TR-based IMD localization.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215281","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

A Multi-band Low-supply-voltage 16-QAM Transmitter Based on a Digital Fractional-N PLL with TDC Calibration for Capsule Endoscopy. 基于数字分数n锁相环TDC校准的胶囊内窥镜多频带低供电电压16-QAM发射机。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-02-16 DOI: 10.1109/TBCAS.2026.3665262

Daehyeon Kwon, Donghyun Youn, Sohmyung Ha, Minkyu Je

{"title":"A Multi-band Low-supply-voltage 16-QAM Transmitter Based on a Digital Fractional-N PLL with TDC Calibration for Capsule Endoscopy.","authors":"Daehyeon Kwon, Donghyun Youn, Sohmyung Ha, Minkyu Je","doi":"10.1109/TBCAS.2026.3665262","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3665262","url":null,"abstract":"This paper presents a highly digital transmitter capable of multi-band 16-QAM modulation for capsule endoscopy, which requires a high data rate with high energy efficiency and relatively high output power to meet receiver sensitivity specifications. A counter-assisted digital phase-locked loop (CDPLL) is utilized for generating clocks across multiple bands with low noise, resulting in good error vector magnitude (EVM) performance. The EVM generated by the CDPLL is better than 1% by using the proposed time-to-digital converter (TDC) calibration. A digital power amplifier with high power efficiency helps reduce the power consumption of the entire endoscopy capsule system, powered by a battery with limited capacity. Fabricated in a 180-nm CMOS process, the transmitter achieves a maximum data rate of 40 Mbps using 16-QAM modulation. At a 1.1-V supply, it achieves a 0.27/0.26-nJ/bit efficiency with a data rate of 40 MHz, and the measured EVM is 3.24/3.7% while delivering a -5-dBm output power within a 10-MHz narrow bandwidth at 435/415 MHz for the wireless medical capsule endoscopy band in Europe and the biomedical band in China, respectively. The corresponding figure of merit is 0.86/0.82 nJ/bit/mW in each band.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208270","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

Memristive Reservoir Computing Circuit for Real-Time Prediction of Epilepsy. 用于癫痫实时预测的记忆库计算电路。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-02-09 DOI: 10.1109/TBCAS.2026.3662427

Lun Lu, Ao Xu, Youpeng Wu, Mingxin Deng, Yi Sun, Zhiwei Li, Yinan Wang, Qingjiang Li

{"title":"Memristive Reservoir Computing Circuit for Real-Time Prediction of Epilepsy.","authors":"Lun Lu, Ao Xu, Youpeng Wu, Mingxin Deng, Yi Sun, Zhiwei Li, Yinan Wang, Qingjiang Li","doi":"10.1109/TBCAS.2026.3662427","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3662427","url":null,"abstract":"The prediction of epileptic seizures can significantly improve patients' quality of life by enabling timely preventive interventions. However, realizing automated real-time prediction on edge hardware remains challenging due to high computational complexity, inefficient temporal signal processing, and the von Neumann bottleneck. In this work, we propose a memristor-based multi-stage reservoir computing architecture that jointly addresses algorithmic and hardware limitations. Volatile memristors are employed in reservoir modules to perform nonlinear temporal feature extraction, avoiding error accumulation issues commonly observed in recurrent neural networks. Non-volatile memristor crossbar arrays are further integrated to implement in-memory analog multiply-accumulate operations, significantly reducing data movement and improving hardware efficiency. Owing to the proposed multi-stage structure, high prediction accuracy is achieved with only 1,700 trainable parameters. Moreover, comprehensive hardware-aware evaluations are conducted, including input noise injection, device-to-device and cycle-to cycle variations to assess robustness against memristor non-idealities. Results demonstrate that the proposed system achieves over 97% accuracy in simulation and exceeds 95% accuracy in hardware experiments, while maintaining stable performance under substantial noise, making it a promising low-power solution for real-time seizure prediction on edge platforms.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146151560","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

A 40.68-MHz Fully-Integrated Voltage/Current-Mode Dual-Output PMU for Wireless Neural Implants. 用于无线神经植入物的40.68 mhz全集成电压/电流模式双输出PMU。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-02-01 DOI: 10.1109/TBCAS.2025.3591228

Yi-Han Ou-Yang, Ronald Wijermars, Pyungwoo Yeon, Tianqi Lu, Amin Arbabian, Wouter A Serdijn, Sijun Du, Dante G Muratore

{"title":"A 40.68-MHz Fully-Integrated Voltage/Current-Mode Dual-Output PMU for Wireless Neural Implants.","authors":"Yi-Han Ou-Yang, Ronald Wijermars, Pyungwoo Yeon, Tianqi Lu, Amin Arbabian, Wouter A Serdijn, Sijun Du, Dante G Muratore","doi":"10.1109/TBCAS.2025.3591228","DOIUrl":"10.1109/TBCAS.2025.3591228","url":null,"abstract":"This paper presents a fully-integrated single-input dual-output power management unit operating both in voltage/current modes for powering mm-scale wireless neural implants. The chip operates in voltage mode most of the time, using an active full-wave rectifier to regulate a low-voltage, high-load output with high power efficiency and low output ripple ($<$32 $mathrm{mV}_{mathrm{pp}}$). It switches to current mode rectification when generating a high-voltage, low-load output. This dual-mode operation allows for flexible power distribution and configurable voltage ratios between the two outputs. The selected 40.68 MHz operating frequency reduces the required capacitances for input impedance matching and output filtering, enabling on-chip integration; the only external component is the receiver coil. A novel resonance breakup switch compatible with full-wave rectification ensures a smooth cold start-up of the chip without any external voltage supply. The chip was fabricated using 40-nm CMOS technology with an active area of 1.18 $mathrm{mm^{2}}$ and was tested in a wireless power link. Measurement results demonstrate that the chip can simultaneously regulate two outputs, $V_{LV}$ = 1 V and $V_{HV}$ = 2 V, with a tested maximum output power of 10 mW and 32.6 µW on $V_{LV}$ and $V_{HV}$, respectively. At the optimal output power condition ($P_{LV}$ = 4.4$sim$6.7 mW), the system achieves a peak power conversion efficiency of 85.87% and a peak end-to-end efficiency of 17.32% when regulating $V_{LV}$. The end-to-end efficiency drops by only 2.38% when regulating both outputs with $R_{LV}$ = 225 $Omega$ and $R_{HV}$ = 400 k$Omega$.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":"41-56"},"PeriodicalIF":4.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692833","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 Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-01-28 DOI: 10.1109/TBCAS.2025.3639842

引用次数: 0

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

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-01-28 DOI: 10.1109/TBCAS.2026.3654281

引用次数: 0

Incoming Editorial 传入的编辑

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-01-28 DOI: 10.1109/TBCAS.2026.3654331

Pedram Mohseni

引用次数: 0

A 1.69µJ Highly Robust Cardiac Arrhythmia Monitoring Processor with Triple-Adaptive QRS Detector and Medically Driven Feature-Fusion Hybrid Neural Networks. 具有三自适应QRS检测器和医学驱动特征融合混合神经网络的1.69µJ高鲁棒心律失常监测处理器。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-01-22 DOI: 10.1109/TBCAS.2026.3653683

Weihao Wang, Xuecong Lu, Guangshun Wei, Gexuan Wu, Guanglin Deng, Wenliang Chen, Yunfeng Huang, Kong-Pang Pun, Bing Li

{"title":"A 1.69µJ Highly Robust Cardiac Arrhythmia Monitoring Processor with Triple-Adaptive QRS Detector and Medically Driven Feature-Fusion Hybrid Neural Networks.","authors":"Weihao Wang, Xuecong Lu, Guangshun Wei, Gexuan Wu, Guanglin Deng, Wenliang Chen, Yunfeng Huang, Kong-Pang Pun, Bing Li","doi":"10.1109/TBCAS.2026.3653683","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3653683","url":null,"abstract":"The detection of arrhythmias is crucial in monitoring cardiac health. However, electrocardiogram (ECG) signals obtained from wearable devices are often compromised by noise, including electrode motion artifacts, baseline wander, and muscle artifacts. This paper addresses these challenges by proposing a highly robust cardiac health monitoring processor featuring a cascaded triple-adaptive QRS detector and medically driven feature-fusion hybrid neural networks (HNN) for arrhythmia classification. The QRS detector uses a self-adaptive triplethreshold mechanism that dynamically correlates duration, RR interval, and error correction thresholds, allowing it to accurately identify QRS complex features in noisy signals, facilitated by event-driven sampling. The HNN arrhythmia classifier combines long short-term memory (LSTM) and artificial neural network (ANN) architectures with three medically driven pathological feature fusion, achieving improved computational efficiency. The prototype is fabricated using the 65-nm CMOS process. The results reveal three findings. First, the total and dynamic power are 2.53 µW and 0.072 µW, respectively, and the all-digital implementation achieves the 0.99mm2 area. Second, the average R-peak detection sensitivity/precision rates exceed 97.38%/97.08% on the MIT-BIH Noise Stress Test Database, and inter-patient classification accuracy exceeds 90.1% on the MIT-BIH Arrhythmia Database under a 6 dB signal-to-noise ratio (SNR). Third, the system achieves low computational complexity with only 2063 parameters and 5.5 KB of SRAM.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032294","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

In Vivo Wireless Powering of a Long-Acting Nanofluidic Drug Delivery Implant. 长效纳米流体给药植入物的体内无线供电。

IF 4.9

IEEE transactions on biomedical circuits and systems Pub Date : 2026-01-22 DOI: 10.1109/TBCAS.2026.3656748

Fabiana Del Bono, Nicola Di Trani, Ashley Joubert, Camden Caffey, Andrea Dentis, Danilo Demarchi, Alessandro Grattoni, Paolo Motto Ros

{"title":"In Vivo Wireless Powering of a Long-Acting Nanofluidic Drug Delivery Implant.","authors":"Fabiana Del Bono, Nicola Di Trani, Ashley Joubert, Camden Caffey, Andrea Dentis, Danilo Demarchi, Alessandro Grattoni, Paolo Motto Ros","doi":"10.1109/TBCAS.2026.3656748","DOIUrl":"https://doi.org/10.1109/TBCAS.2026.3656748","url":null,"abstract":"Wireless power transfer (WPT) is a key enabler for long-term operation of implantable medical devices, eliminating the need for percutaneous drivelines and frequent surgical device replacements. This paper presents the design and validation of a fully wireless, rechargeable implantable drug delivery system (nDS) with an integrated power management and control system, specifically developed for use in freely moving animal models. The proposed system consists of a subcutaneous implant with an inductive power receiver and an external, backpack-mounted power transmitter that dynamically adjusts energy delivery in response to real-time implant feedback. A closed-loop power control strategy, implemented via Bluetooth Low Energy (BLE) communication, ensures adaptive power transfer to maintain system efficiency despite coil misalignment and animal movement. Building on a previously characterized inductive link, the present work extends the validation from benchtop characterization to in vivo operation in freely moving rats, demonstrating safe and repeatable wireless battery recharging of an implantable nanofluidic drug delivery system. Across four in vivo recharging sessions, the median average power transfer efficiency during constantcurrent phase was 22.9% with a median average power delivered to the load of 104.7 mW. The charging sessions lasted from 90 (first) to 30 (last) minutes, performed once per week over 4 weeks. The proposed closed-loop WPT implementation enabled reliable battery recharging within clinically relevant time scales while maintaining operation in compliance with thermal safety constraints, thereby supporting chronic, fully untethered drug delivery studies in small animals.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032348","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