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

This paper presents a multi-antenna external reader system that enables orientation insensitive communication with implantable medical devices (IMDs) for wireless biomedical applications. The proposed system consists of a circular array with six loop antennas. The antenna placement and orientations are determined by analyzing the near-field magnetic field variations of the loop antenna. The proposed system is first simulated using HFSS electromagnetic simulation software. Our simulations show that the received power at the proposed external reader with six antennas only varies about 5 dB for any given orientation of the implanted antenna, which is highly significant compared to the 20-35 dB variation with a single external antenna. Here, we select the antenna which provides the largest coupling between the IMD to receive/transmit signals. A prototype of the proposed multi-antenna external reader is then implemented using custom-designed PCBs that interconnect loop antennas, transceiver ICs, and commercially-available circuit components. A custom PCB with a miniaturized loop antenna is used to emulate an implantable device. Based on measurement results, the received power in the external reader only varies about 3 dB when the miniaturized antenna rotates with respect to the x-axis. These measurements show good agreement with the simulated reader.

Multi-access networking with miniaturized wireless implantable devices can enable and advance closed-loop medical applications to deliver precise diagnosis and treatment. Using ultrasound (US) for wireless implant systems is an advantageous approach as US can beamform with high spatial resolution to efficiently power and address multiple implants in the network. To demonstrate these capabilities, we use wirelessly powered mm-sized implants with bidirectional communication links; uplink data communication measurements are performed using time, spatial, and frequency-division multiplexing schemes in tissue phantom. A 32-channel linear transmitter array and an external receiver are used as a base station to network with two implants that are placed 6.5 cm deep and spaced less than 1 cm apart. Successful wireless powering and uplink data communication around 100 kbps with a measured bit error rate below 10^-4 are demonstrated for all three networking schemes, validating the multi-access networking feasibility of US wireless implant systems.

Adaptive deep brain stimulation (aDBS) is an emerging method to alleviate the side effects and improve the efficacy of conventional open-loop stimulation for movement disorders. However, current adaptive DBS techniques are primarily based on single-feature thresholding, precluding an optimized delivery of stimulation for precise control of motor symptoms. Here, we propose to use a machine learning approach for resting-state tremor detection from local field potentials (LFPs) recorded from subthalamic nucleus (STN) in 12 Parkinson's patients. We compare the performance of state-of-the-art classifiers and LFP-based biomarkers for tremor detection, showing that the high-frequency oscillations and Hjorth parameters achieve a high discriminative performance. In addition, using Kalman filtering in the feature space, we show that the tremor detection performance significantly improves (F_(1,15)=32.16, p<0.0001). The proposed method holds great promise for efficient on-demand delivery of stimulation in Parkinson's disease.

In recent electrophysiological studies, CMOS-based high-density microelectrode arrays (HD-MEA) have been widely used for studies of both in-vitro and in-vivo neuronal signals and network behavior. Yet, an open issue in MEA design concerns the tradeoff between signal-to-noise ratio (SNR) and number of readout channels. Here we present a new HD-MEA design in 0.18 μm CMOS technology, consisting of 19,584 electrodes at a pitch of 18.0 μm. By combing two readout structures, namely active-pixel-sensor (APS) and switch-matrix (SM) on a single chip, the dual-mode HD-MEA is capable of recording simultaneously from the entire array and achieving high signal-to-noise-ratio recordings on a subset of electrodes. The APS readout circuits feature a noise level of 10.9 μV_rms for the action potential band (300 Hz - 5 kHz), while the noise level for the switch-matrix readout is 3.1 μV_rms.

The role of peripheral nerves in regulating major organ function in health and disease is not well understood. Elucidating the relationships between biomarkers and neural activity during conditions free form anesthesia is essential to advancing future investigations of autonomic organ control and improving precision for neuromodulation treatment approaches. Here we present a simple, customizable, off-the-shelf component sensor platform to meet research needs for studying different organs under conscious, free movement. The platform consists of a small, rechargeable coin-cell battery, an energy-harvesting IC, a low-power microcontroller, a low-power pressure transducer, customizable number of electrodes with a common anode, inductive recharge input, and OOK inductive transmission. A case study demonstrating a bladder implant for long-term monitoring is presented, utilizing a novel, non-hermetic encapsulation approach. The customized platform uses two sleep modes to minimize battery loading, exhibiting a maximum time-averaged current draw of 125 micro-amps during sensing and transmission, with a quiescent current draw of 95 nano-amps into the microcontroller.

Although the mechanisms of recording bioelectrical signals from different types of electrogenic cells (neurons, cardiac cells etc.) by means of planar metal electrodes have been extensively studied, the recording characteristics and conditions for very small electrode sizes are not yet established. Here, we present a combined experimental and computational approach to elucidate, how the electrode size influences the recorded signals, and how inherent properties of the electrode, such as impedance, noise, and transmission characteristics shape the signal. We demonstrate that good quality recordings can be achieved with electrode diameters of less than 10 µm, provided that impedance reduction measures have been implemented and provided that a set of requirements for signal amplification has been met.

Human schistosomiasis is a neglected tropical disease caused by trematodes, affecting almost 250 million people worldwide. For the past 30 years, treatment has relied on the large-scale administration of praziquantel. However, concerns regarding the appearance of drug-resistance parasites require efforts in identifying novel classes of suitable drugs against schistosomiasis. The current drug screening system is manual, slow and subjective. We present here a microfluidic platform capable of detecting changes in viability of Schistosoma mansoni larvae (Newly Transformed Schistosomula, NTS). This platform could serve as a pre-screening tool for the identification of drug candidates. It is composed of a pair of coplanar electrodes integrated in a microfluidic channel for the detection and quantification of NTS motility. Comparison of viability detection by using our platform with the standard visual evaluation shows that our method is able to reliably detect viable and non-viable NTS at high sensitivity, also in case of low-motility parasites, while enabling a 10 fold decrease in sample consumption.

In this work, we investigated the use of noninvasive, targeted transcutaneous electrical nerve stimulation (TENS) of peripheral nerves to provide sensory feedback to two amputees, one with targeted sensory reinnervation (TSR) and one without TSR. A major step in developing a closed-loop prosthesis is providing the sense of touch back to the amputee user. We investigated the effect of targeted nerve stimulation amplitude, pulse width, and frequency on stimulation perception. We discovered that both subjects were able to reliably detect stimulation patterns with pulses less than 1 ms. We utilized the psychophysical results to produce a subject specific stimulation pattern using a leaky integrate and fire (LIF) neuron model from force sensors on a prosthetic hand during a grasping task. For the first time, we show that TENS is able to provide graded sensory feedback at multiple sites in both TSR and non-TSR amputees while using behavioral results to tune a neuromorphic stimulation pattern driven by a force sensor output from a prosthetic hand.