IEEE transactions on biomedical circuits and systems最新文献

Magnetic resonance imaging (MRI) exhibits rich and clinically useful endogenous contrast mechanisms, which can differentiate soft tissues and are sensitive to flow, diffusion, magnetic susceptibility, blood oxygenation level, and more. However, MRI sensitivity is ultimately constrained by Nuclear Magnetic Resonance (NMR) physics, and its spatiotemporal resolution is limited by SNR and spatial encoding. On the other hand, miniaturized implantable sensors offer highly localized physiological information, yet communication and localization can be challenging when multiple implants are present. This paper introduces the MRDust, an active "contrast agent" that integrates active sensor implants with MRI, enabling the direct encoding of highly localized physiological data into MR images to augment the anatomical images. MRDust employs a micrometer-scale on-chip coil to actively modulate the local magnetic field, enabling MR signal amplitude and phase modulation for digital data transmission. Since MRI inherently captures the anatomical tissue structure, this method has the potential to enable simultaneous data communication, localization, and image registration with multiple implants. This paper presents the underlying physical principles, design tradeoffs, and design methodology for this approach. To validate the concept, a 900 × 990 µm² chip was designed using TSMC 28 nm technology, with an on-chip coil measuring 630 µm in diameter. The chip was tested with custom hardware in an MR750W GE3T MRI scanner. Successful voxel amplitude modulation is demonstrated with Spin-Echo Echo-Planar-Imaging (SE-EPI) sequence, achieving a contrast-to-noise ratio (CNR) of 25.58 with a power consumption of 130 µW.

This paper introduces a fast, high-accuracy methodology for conducting Electrochemical Impedance Spectroscopy (EIS) based on Fast Fourier Transform (FFT), to meet the requirements of portable, real-time biomedical impedance-based detections with Ultra-Microband (UMB) sensor. Instead of using white noise-like wideband signals as in conventional FFT-based EIS, the proposed method uses a square wave as the excitation signal, which achieves a fast, accurate EIS measurement, but no longer requires complex circuits like high-resolution DACs or frequency mixers for the signal generation. This work starts with the theoretical justification for treating the sensor as a Linear Time-Invariant (LTI), then the practical linear region for operating the sensor as an LTI system is experimentally verified and determined, which enables the capacity of employing the harmonics of a square wave for EIS measurements. A dynamic model of the charge-transfer resistance together with an approximated of the Constant Phase Element (CPE) are implemented with Verilog-A for simulations, and a circuit consisting of a control amplifier and a Trans-Impedance Amplifier (TIA) is designed and fabricated with 65 nm CMOS for validating its on-chip feasibility. This work shortens the EIS measurement time by 91.7% in a frequency sweep range from 0.5 Hz to 500 Hz, with only 2.73% average Mean Absolute Percentage Error (MAPE), compared to a commercial electrochemical instrument AutoLab, with five pre-modified electrodes across four different concentrations of Ferrocene Carboxylic Acid (FcCOOH), demonstrating this method is suitable for portable, real-time label-free EIS biomedical detections and applications.

Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures require 1) multi-class (Mul) and 2) patient-independent (Pi) classification. Recent implementable chips for seizure classification rarely satisfy the two requirements due to restricted resources in small chips; therefore, high efficiency is imperative along with accuracy. This paper introduces an efficient MulPi chip, fabricated for the first time to simultaneously fulfill multi-class and patient independence, based on a co-design approach. We develop a 5-layer convolutional neural network (CNN), MulPiCNN, with advanced training techniques for lightness and accuracy. At the hardware level, our SRAM-based chip leverages computing-in-memory (CIM) for efficiency. The fabricated MulPi chip is distinguished from prior CIMs in two folds, namely ISRW-CIM: a) input-stationary (IS) CIM for resource-saving, and b) row-wise (RW) computing to address a challenge of SRAM CIM, empowered by our novel 2T-Hadamard product unit (HPU). MulPi outperforms state-of-the-art chips with 98.5% sensitivity and 99.2% specificity, classifying in 0.12s and 0.348mm${}^{2}$.