IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference最新文献

The proper functioning of the respiratory system is evaluated by monitoring the exchange of blood oxygen and carbon dioxide. While wearable devices for monitoring both blood oxygen and carbon dioxide are emerging, wearable carbon dioxide monitors remain relatively rare. This paper introduces a novel wearable prototype that integrates the measurement of transcutaneous carbon dioxide and peripheral blood oxygen saturation on a miniaturized custom-designed printed circuit board. The device employs a fluorescent sensing film consisting of two distinct luminophore types and utilizes the time-domain dual lifetime referencing technique to enhance measurement accuracy by mitigating the effects of confounding factors. Thorough testing on human subjects validated the prototype's functionality, comparing its performance against commercial clinical devices. The prototype effectively tracked changes in transcutaneous carbon dioxide induced by hyperventilation, with a resolution as low as 1 mmHg. Additionally, blood oxygen saturation measurements were tested on human subjects to compare our prototypes' performance to that of clinical devices. The results confirm the potential of the proposed novel wearable for prolonged use with minimal maintenance and underscore its significance in advancing wearable health monitoring technologies.

Intravascular ultrasound (IVUS) provides sufficient spatial resolution and penetration depth for use during radiological interventions and for detecting lesion morphology and pathology. Increased use of IVUS in peripheral arteries requires ever-smaller catheters with broader bandwidth to achieve microscopic resolution. This work advances the development of sub-mm IVUS catheters that have greatly improved resolution using broad-band polymer transducers. The high transducer impedance prevents direct attachment to standard 50-Ω micro-coaxial cables and requires high voltage excitation (50 - 100 V_PP typically). To enable broadbandwidth IVUS in a sub-mm catheter, a custom analog front end (AFE) ASIC was developed. The AFE used an active limiter with a regenerative clamping structure to safely withstand large voltages using low-V_T FETs. AFE characterization in series and shunt configurations demonstrated a typical signal-to-noise ratio of 33.3 dB over a 105 MHz imaging bandwidth. SNR was limited by the wide fractional bandwidth; AFEs demonstrated a 2.1 nV/√Hz noise floor. Pulse recovery to >230-Vpp excitation was measured between 325 - 450 ns, allowing imaging as close as 0.5 mm. The presented design achieves a broader imaging fractional bandwidth than previous interface amplifiers in a sub-millimeter form factor. Example images obtained from a 0.8-mm, 40 MHz transducer showed sufficient resolution for the detection of individual stent struts within a simulated artery with a wall thickness of 0.35 mm.

Electrical capacitance tomography (ECT) is a non-optical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.

Neurostimulation therapies are often applied as an alternative method to pharmaceutical treatment for chronic pain relief. This paper demonstrates the design and implementation of a programmable Pulse Generator (PG) for analgesic nerve stimulation with 3 modes of operation: biphasic asymmetric, biphasic capacitor coupled, and monophasic Degradation On Command (DOC). The PG is implemented on 180nm CMOS technology and could generate up to ± 4mA current pulses in steps of 31μA (8-bit resolution) for pulse duration range of 1-256μs and stimulation frequency range of 16Hz-250kHz. During in vitro studies, capacitor-coupled biphasic stimulation provides electrode stability with only 5Ω impedance change for up to 14 million pulses. In the DOC mode, accelerated degradation of a bioresorbable electrode was observed after 24hrs of stimulation, when its impedance increased from about 100Ω to over 0.2MΩ at 500Hz. The compact, tunable and battery-powered pulse generator printed circuit board (PCB) shows promising results to perform in vivo animal studies for up to 30 hours of continuous stimulation with 26.4mW peak power consumption.

In this paper we present spatio-temporally controlled electrochemical stimulation of aqueous samples using an integrated CMOS microelectrode array with 131,072 pixels. We demonstrate programmable gold electrodeposition in arbitrary spatial patterns, controllable electrolysis to produce microscale hydrogen bubbles, and spatially targeted electrochemical pH modulation. Dense spatially-addressable electrochemical stimulation is important for a wide range of bioelectronics applications.

Brain computer interfaces (BCIs) provide clinical benefits including partial restoration of lost motor control, vision, speech, and hearing. A fundamental limitation of existing BCIs is their inability to span several areas (> cm²) of the cortex with fine (<100 μm) resolution. One challenge of scaling neural interfaces is output wiring and connector sizes as each channel must be independently routed out of the brain. Time division multiplexing (TDM) overcomes this by enabling several channels to share the same output wire at the cost of added noise. This work leverages a 130-nm CMOS process and transfer printing to design and simulate a 384-channel actively multiplexed array, which minimizes noise by adding front end filtering and amplification to every electrode site (pixel). The pixels are 50 μm × 50 μm and enable recording of all 384 channels at 30 kHz with a gain of 22.3 dB, noise of 9.57 μV rms, bandwidth of 0.1 Hz - 10 kHz, while only consuming 0.63 μW/channel. This work can be applied broadly across neural interfaces to create high channel-count arrays and ultimately improve BCIs.

Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.

Imaging of spinal cord microvasculature holds great potential in directing critical care management of spinal cord injury (SCI). Traditionally, contrast agents are preferred for imaging of the spinal cord vasculature, which is disadvantageous for long-term monitoring of injury. Here, we present FlowMorph, an algorithm that uses mathematical morphology techniques to segment non-contrast Doppler-based videos of rat spinal cord. Using the segmentation, it measures single-vessel parameters such as flow velocity, rate, and radius, with visible cardiac cycles in individual vessels showcasing the spatiotemporal resolution. The segmentation outlines vessels well with little extraneous labeling, and outlines are smooth through time. Radius measurements of perforating vessels are similar to what is seen in the literature through other methods. Verification of the algorithm through comparison to manual measurement and in vitro microphantom standards highlights points of future improvement. This method will be vital for future work studying the vascular effects of SCI and can be adopted to other species as well.

In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous activity or so-called "bursting". Bursting entails systematic changes in spike shapes and amplitudes and remains a challenge for current spike sorting schemes. We use realistic simulated bursting recordings of high-density micro-electrode arrays (HD-MEAs) and we present a fully automated algorithm based on template matching with a focus on recovering missed spikes during bursts. To compare and benchmark spike-sorting performance after applying our method, we used ground-truth information of simulated recordings. We show that our approach can be effective in improving spike sorting performance during bursting. Further validation with experimental recordings is necessary.

Neural interfaces with increasing channel counts require a scalable means of testing. While multiplexed potentiostats have long been the solution to this problem, most have been dedicated to one specific probe design or potentiostat, limited in the electrochemical techniques available, inordinately expensive, or they support multiplexing of too few channels. We present the design of an automated multiplexed potentiostat system that addresses these limitations-it is easily generalizable to any probe and potentiostat, supports any electrochemical technique available with the potentiostat, is low-cost, and can readily be expanded to hundreds of channels with support for multiple simultaneous potentiostats. This paper discusses the design philosophy and architecture of our 512-channel, 4-potentiostat system before demonstrating functionality with electrochemical impedance spectroscopy data, cyclic voltammetry curves, and an example of electrochemical surface modification, all on functional implantable microelectrode arrays currently being used for in vivo electrophysiological studies. Finally, we discuss the limitations to some sensitive or high-frequency impedance measurements due to reactive parasitics.