IEEE transactions on biomedical circuits and systems最新文献

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.

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.

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.