IEEE transactions on biomedical circuits and systems最新文献

{"title":"Design and Implementation of Integrated Dual-Mode Pulse and Continuous-Wave Electron Paramagnetic Resonance Spectrometers","authors":"Jui-Hung Sun;Difei Wu;Peter Qin;Constantine Sideris","doi":"10.1109/TBCAS.2024.3465210","DOIUrl":"10.1109/TBCAS.2024.3465210","url":null,"abstract":"Electron paramagnetic resonance (EPR) is a powerful spectroscopic technique that allows direct detection and characterization of radicals containing unpaired electron(s). The development of portable, low-power EPR sensing modalities has the potential to significantly expand the utility of EPR in a broad range of fields, ranging from basic science to practical applications such as point-of-care diagnostics. The two major methodologies of EPR are continuous-wave (CW) EPR, where the frequency or field is swept with a constant excitation, and pulse EPR, where short pulses induce a transient signal. In this work, we present the first realization of a fully integrated pulse EPR spectrometer on-chip. The spectrometer utilizes a subharmonic direct-conversion architecture that enables an on-chip oscillator to be used as a dual-mode EPR sensing cell, capable of both CW and pulse-mode operation. An on-chip reference oscillator is used to injection-lock the sensor to form pulses and also to downconvert the pulse EPR signal. A proof-of-concept spectrometer IC with two independent sensing cells is presented, which achieves a pulse sensitivity of \u0000<inline-formula><tex-math>$4.6times 10^{9}$</tex-math></inline-formula>\u0000 spins (1000 averages) and a CW sensitivity of \u0000<inline-formula><tex-math>$2.9times 10^{9}$</tex-math></inline-formula>\u0000 spins/\u0000<inline-formula><tex-math>$sqrt{text{Hz}}$</tex-math></inline-formula>\u0000 and can be powered and controlled via a computer USB interface. The sensing cells consume as little as 2.1mW (CW mode), and the system is tunable over a wide frequency range of 12.8–14.9GHz (CW/pulse). Single-pulse free induction decay (FID), two-pulse inversion recovery, two-pulse Hahn echo, three-pulse stimulated echo, and CW experiments demonstrate the viability of the spectrometer for use in portable EPR sensing.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1209-1219"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142304706","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":"Closed-Loop Implantable Neurostimulators for Individualized Treatment of Intractable Epilepsy: A Review of Recent Developments, Ongoing Challenges, and Future Opportunities","authors":"Hossein Kassiri;Abdul Muneeb;Rojin Salahi;Alireza Dabbaghian","doi":"10.1109/TBCAS.2024.3456825","DOIUrl":"10.1109/TBCAS.2024.3456825","url":null,"abstract":"Driven by its proven therapeutic efficacy in treating movement disorders and psychiatric conditions, neurostimulation has emerged as a promising intervention for intractable epilepsy. Researchers envision an advanced implantable device capable of long-term neuronal monitoring, high spatio-temporal resolution data processing, and timely responsive neurostimulation upon seizure detection. However, the stringent energy constraints of implantable devices and significant inter-patient variability in neural activity pose substantial challenges and opportunities for biomedical circuits and systems researchers. For seizure detection, various ASIC solutions employing both deterministic and data-driven algorithms have been developed. These solutions leverage a subset of numerous signal features (spanning time and frequency domains) and classifiers (such as SVMs, DNNs, SNNs) to achieve notable success in terms of detection accuracy, latency, and energy efficiency. Implementations vary widely in computational approaches (digital, mixed-signal, analog, spike-based), training strategies (online versus offline), and application targets (patient-specific versus cross-patient). In terms of treatment, recent efforts have focused on the personalization of stimulation waveforms to enhance therapeutic efficacy. This personalization faces complex challenges, including a limited understanding of how stimulation parameters influence neuronal activity, the lack of a comprehensive brain model to capture its intricate electrochemical dynamics, and recording neural signals in the presence of stimulation artifacts. This review provides a comprehensive overview of the field, detailing the foundational principles, recent advancements, and ongoing challenges in enhancing the diagnostic accuracy, treatment efficacy, and energy efficiency of implantable patient-optimized neurostimulators. We also discuss potential future directions, emphasizing the need for standardized performance metrics, advanced computational models, and adaptive stimulation protocols to realize the full potential of this transformative technology.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 6","pages":"1268-1295"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195024","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":"Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA","authors":"Matteo Antonio Scrugli;Gianluca Leone;Paola Busia;Luigi Raffo;Paolo Meloni","doi":"10.1109/TBCAS.2024.3456552","DOIUrl":"10.1109/TBCAS.2024.3456552","url":null,"abstract":"The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) signals offers exciting opportunities for the development of complex prosthetic devices and human-machine interfaces, moving from discrete gesture recognition, towards continuous movement tracking. In this study, we present two solutions for real-time sEMG processing, based on lightweight Spiking Neural Networks (SNNs) and efficiently implemented on a Lattice iCE40-UltraPlus FPGA, especially suitable for low-power applications. We first assess the performance in the discrete finger gesture recognition task, considering as a reference the NinaPro DB5 dataset, and demonstrating an accuracy of 83.17% in the classification of twelve different finger gestures. We also consider the more challenging problem of continuous finger force modeling, referencing the Hyser dataset for finger tracking during independent extension and contraction exercises. The assessment reveals a correlation of up to 0.875 with the ground-truth forces. Our systems take advantage of SNNs’ inherent efficiency and, dissipating 11.31 mW in active mode, consume 44.6 µJ for a gesture recognition classification and 1.19 µJ for a force modeling inference. Considering dynamic power-consumption management and the introduction of idle periods, average power drops to 1.84 mW and 3.69 mW for these respective tasks.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"68-81"},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing.","authors":"Maryam Sadeghi, Yasser Rezaeiyan, Dario Fernandez Khatiboun, Sherif Eissa, Federico Corradi, Charles Augustine, Farshad Moradi","doi":"10.1109/TBCAS.2024.3452635","DOIUrl":"10.1109/TBCAS.2024.3452635","url":null,"abstract":"<p><p>The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation. This work presents NEXUS, a 16-core SNN with a diamond-shaped NoC topology fabricated in 28-nm CMOS technology. It integrates 4096 leaky integrate-and-fire (LIF) neurons with 1M 4-bit synaptic weights, occupying an area of 2.16 mm2. The proposed NoC architecture is scalable to any network size, ensuring no data loss due to contending packets with a maximum routing latency of 5.1μs for 16 cores. The proposed congestion management method eliminates the need for FIFO in routers, resulting in a compact router footprint of 0.001 mm². The proposed neurosynaptic core allows for increasing the processing speed by up to 8.5× depending on input sparsity. The SNN achieves a peak throughput of 4.7 GSOP/s at 0.9 V, consuming a minimum energy per synaptic operation (SOP) of 3.3 pJ at 0.55 V. A 4-layer feed-forward network is mapped onto the chip, classifying MNIST digits with 92.3% accuracy at 8.4Kclassification/ s and consuming 2.7-μJ/classification. Additionally, an audio recognition task mapped onto the chip achieves 87.4% accuracy at 215-μJ/classification.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142116468","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":"GCOC: A Genome Classifier-On-Chip based on Similarity Search Content Addressable Memory.","authors":"Yuval Harary, Paz Snapir, Shir Siman Tov, Chen Kruphman, Eyal Rechef, Zuher Jahshan, Esteban Garzon, Leonid Yavits","doi":"10.1109/TBCAS.2024.3449788","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3449788","url":null,"abstract":"<p><p>GCOC is a genome classification system-on-chip (SoC) that classifies genomes by k-mer matching, an approach that divides a DNA query sequence into a set of short DNA fragments of size k, which are searched in a reference genome database, with the underlying assumption that sequenced DNA reads of the same organism (or its close variants) share most of such k-mers. At the core of GCOC is a similarity, or approximate search-capable Content Addressable Memory (SAS-CAM), which in addition to exact match, also supports approximate, or Hamming distance tolerant search. Classification operation is controlled by an embedded RISC-V processor. GCOC classification platform was designed and manufactured in a commercial 65nm process. We conduct a thorough analysis of GCOC classification efficiency as well as its performance, silicon area, and power consumption using silicon measurements. GCOC classifies 769.2K short DNA reads/sec. The silicon area of GCOC SoC is 3.12mm² and its power consumption is 1.27mW. We envision GCOC deployed as a field (for example at points of care) portable classifier where the classification is required to be real-time, easy to operate and energy efficient.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086424","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":"An Electrochemical CMOS Biosensor Array Using Phase-Only Modulation With 0.035% Phase Error and In-Pixel Averaging","authors":"Aditi Jain;Saeromi Chung;Eliah Aronoff Spencer;Drew A. Hall","doi":"10.1109/TBCAS.2024.3450843","DOIUrl":"10.1109/TBCAS.2024.3450843","url":null,"abstract":"This paper presents a 16 × 20 CMOS biosensor array based on electrochemical impedance spectroscopy (EIS), a highly sensitive label-free technique for rapid disease detection at the point-of-care. This high-density system implements polar-mode detection with phase-only EIS measurement over a 5 kHz - 1 MHz frequency range. The design features predominantly digital readout circuitry, ensuring scalability with technology, along with a load-compensated transimpedance amplifier, all within a 140 × 140 µm² pixel. The architecture enables in-pixel digitization and accumulation, which increases the SNR by 10 dB for each 10× increase in readout time. Implemented in a 180 nm CMOS process, the 3 × 4 mm² chip achieves state-of-the-art performance with an rms phase error of 0.035% at 100 kHz through a duty-cycle insensitive phase detector and one of the smallest per pixel areas with in-pixel quantization.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 2","pages":"416-427"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086423","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":"Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection","authors":"Qiang Zhang;Mingyue Cui;Yue Liu;Weichong Chen;Zhiyi Yu","doi":"10.1109/TBCAS.2024.3450896","DOIUrl":"10.1109/TBCAS.2024.3450896","url":null,"abstract":"Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address this problem, this article proposes a novel fusion architecture for AI processors, which enables event-triggered cross-paradigm integration and computation. Our method introduces a distributed-aggregated classification architecture (D-ACA) that facilitates the reuse of hardware resources across two-stage recognition, thereby obviating the need for standby hardware and enhancing energy efficiency. Integrating a non-encoding biomedical circuit method based on spiking neural networks (SNNs), the architecture eliminates encoded neurons at the hardware level, significantly optimizing energy consumption and hardware resource utilization. Additionally, we develop a configurable and highly flexible control method that supports various neuron modules, enabling continuous detection of epileptic seizures and activating high-precision recognition upon event detection. Finally, we implement the design on the Xilinx ZCU 102 FPGA board, where the AI processor achieves a high classification accuracy of 98.1% while consuming extremely low classification energy (3.73 <inline-formula><tex-math>$mu$</tex-math></inline-formula>J per classification).","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"28-39"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086425","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":"Parallel Resonant Magnetic Field Generator for Biomedical Applications.","authors":"Yuan Lei, Shoulong Dong, Runze Liang, Sizhe Xiang, Qinyu Huang, Junhao Ma, Hongyu Kou, Liang Yu, Chenguo Yao","doi":"10.1109/TBCAS.2024.3450881","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3450881","url":null,"abstract":"<p><p>In recent years, pulsed magnetic field (PMF) have attracted significant attention as a non-invasive electroporation method in the biomedical field. To further explore the biomedical effects generated by oscillating PMF, we designed a novel PMF generator for biomedical research. Based on resonance principles, the designed generator outputs sinusoidal oscillating PMF. To validate the feasibility and application value of the designed topology, a miniaturized platform was constructed using a selected multi-turn solenoid coil. The output performance of the generator was tested under different discharge voltage levels. The results revealed that the current multiplication factor remained consistently around 2 times, with the energy efficiency and circuit quality factor maintained at 82% and above 4.5, respectively. In addition, the generator's ability to flexibly modulate the number of pulse oscillations was demonstrated. The compatibility of the designed coil parameters and generator circuit parameters was analyzed, with tests on the effects of coil resistance and switch action time on the generator's output performance. Based on the magnetic field action platform, a simulation model of the actual scale coil was established. The spatial and temporal distribution of the magnetic field, induced electric field, and power transmission in the target area were described from multiple angles. Finally, biological experiments conducted using the constructed generator revealed the synergistic effect of sinusoidal oscillating PMF combined with drugs in tumor cell killing.</p>","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086426","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}

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{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2024.3437552","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3437552","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}