A multi-channel stimulator with an active electrode array implant for vagal-cardiac neuromodulation studies.

BACKGROUND: Implantable vagus nerve stimulation is a promising approach for restoring autonomic cardiovascular functions after heart transplantation. For successful treatment a system should have multiple electrodes to deliver precise stimulation and complex neuromodulation patterns. METHODS: This paper presents an implantable multi-channel stimulation system for vagal-cardiac neuromodulation studies in swine species. The system comprises an active electrode array implant percutaneously connected to an external wearable controller. The active electrode array implant has an integrated stimulator ASIC mounted on a ceramic substrate connected to an intraneural electrode array via micro-rivet bonding. The implant is silicone encapsulated for biocompatibility and implanted lifetime. The stimulation parameters are remotely transmitted via a Bluetooth telemetry link. RESULTS: The size of the encapsulated active electrode array implant is 8 mm × 10 mm × 3 mm. The stimulator ASIC has 10-bit current amplitude resolution and 16 independent output channels, each capable of delivering up to 550 µA stimulus current and a maximum voltage of 20 V. The active electrode array implant was subjected to in vitro accelerated lifetime testing at 70 °C for 7 days with no degradation in performance. After over 2 h continuous stimulation, the surface temperature change of the implant was less than 0.5 °C. In addition, in vivo testing on the sciatic nerve of a male Göttingen minipig demonstrated that the implant could effectively elicit an EMG response that grew progressively stronger on increasing the amplitude of the stimulation. CONCLUSIONS: The multi-channel stimulator is suitable for long term implantation. It shows potential as a useful tool in vagal-cardiac neuromodulation studies in animal models for restoring autonomic cardiovascular functions after heart transplantation.

An optogenetic cell therapy to restore control of target muscles in an aggressive mouse model of amyotrophic lateral sclerosis.

Breakdown of neuromuscular junctions (NMJs) is an early pathological hallmark of amyotrophic lateral sclerosis (ALS) that blocks neuromuscular transmission, leading to muscle weakness, paralysis and, ultimately, premature death. Currently, no therapies exist that can prevent progressive motor neuron degeneration, muscle denervation, or paralysis in ALS. Here, we report important advances in the development of an optogenetic, neural replacement strategy that can effectively restore innervation of severely affected skeletal muscles in the aggressive SOD1G93A mouse model of ALS, thus providing an interface to selectively control the function of targeted muscles using optical stimulation. We also identify a specific approach to confer complete survival of allogeneic replacement motor neurons. Furthermore, we demonstrate that an optical stimulation training paradigm can prevent atrophy of reinnervated muscle fibers and results in a tenfold increase in optically evoked contractile force. Together, these advances pave the way for an assistive therapy that could benefit all ALS patients.

High Frame Rate Electrical Impedance Tomography System for Monitoring of Regional Lung Ventilation.

This paper describes the development of a compact high frame rate passive electrical impedance tomography system. The injected current amplitude and frequency can be adjusted to fit any EIT application. Measured results show that the system is capable of high frame rate of 89 fps and has power consumption of 1.7 W. It has automatic gain control that reduces noise and improves the quality of the measured EIT image. A comparison is made with other EIT systems to show the potential of the developed system. Clinical Relevance- The developed EIT system has application in the clinical assessment of neonatal and SARS-Co V-2 patients. In these applications there is an urgent need for a low cost bedside non-invasive imaging system to continuously monitor dynamic changes in regional lung ventilation.

Multimodal Diagnosis for Pulmonary Embolism from EHR Data and CT Images.

Pulmonary Embolism (PE) is a severe medical condition that can pose a significant risk to life. Traditional deep learning methods for PE diagnosis are based on Computed Tomography (CT) images and do not consider the patient's clinical context. To make full use of patient's clinical information, this article presents a multimodal fusion model ingesting Electronic Health Record (EHR) data and CT images for PE diagnosis. The proposed model is based on multilayer perception and convolutional neural networks. To remove the invalid information in the EHR data, the multidimensional scaling algorithm is performed for feature dimension reduction. The EHR data and CT images of 600 patients are used for experiments. The experiment results show that the proposed models outperform existing methods and the multimodal fusion model shows better performance than the single-input model.

Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting.

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels σN between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.

A Fully Implantable Opto-Electro Closed-Loop Neural Interface for Motor Neuron Disease Studies.

This paper presents a fully implantable closed-loop device for use in freely moving rodents to investigate new treatments for motor neuron disease. The 0.18 µm CMOS integrated circuit comprises 4 stimulators, each featuring 16 channels for optical and electrical stimulation using arbitrary current waveforms at frequencies from 1.5 Hz to 50 kHz, and a bandwidth programmable front-end for neural recording. The implant uses a Qi wireless inductive link which can deliver >100 mW power at a maximum distance of 2 cm for a freely moving rodent. A backup rechargeable battery can support 10 mA continuous stimulation currents for 2.5 hours in the absence of an inductive power link. The implant is controlled by a graphic user interface with broad programmable parameters via a Bluetooth low energy bidirectional data telemetry link. The encapsulated implant is 40 mm × 20 mm × 10 mm. Measured results are presented showing the electrical performance of the electronics and the packaging method.

Insertion Guidance Based on Impedance Measurements of a Cochlear Electrode Array.

The cochlear implantable neuromodulator provides substantial auditory perception to those with severe or profound impaired hearing. Correct electrode array positioning in the cochlea is one of the important factors for quality hearing, and misplacement may lead to additional injury to the cochlea. Visual inspection of the progress of electrode insertion is limited and mainly relies on the surgeon's tactile skills, and there is a need to detect in real-time the electrode array position in the cochlea during insertion. The available clinical measurement presently provides very limited information. Impedance measurement may be used to assist with the insertion of the electrode array. Using computational modeling of the cochlea, and its local tissue layers merging with the associated neuromodulator electrode array parameters, the impedance variations at different insertion depths and the proximities to the cochlea walls have been analyzed. In this study, an anatomical computational model of the temporal region of a patient is used to derive the relationship between impedance variations and the electrode proximity to the cochlea wall and electrode insertion depth. The aim was to examine whether the use of electrode impedance variations can be an effective marker of electrode proximity and electrode insertion depth. The proposed anatomical model simulates the quasi-static electrode impedance variations at different selected points but at considerable computation cost. A much less computationally intensive geometric model (~1/30) provided comparative impedance measurements with differences of <2%. Both use finite element analysis over the entire cross-section area of the scala tympani. It is shown that the magnitude of the impedance varies with both electrode insertion depth and electrode proximity to the adjacent anatomical layers (e.g., cochlea wall). In particular, there is a 1,400% increase when the electrode array is moved very close to the cochlea wall. This may help the surgeon to find the optimal electrode position within the scala tympani by observation of such impedance characteristics. The misplacement of the electrode array within the scala tympani may be eliminated by using the impedance variation metric during electrode array insertion if the results are validated with an experimental study.

Cross-sectional chest circumference and shape development in infants.

OBJECTIVE: This study investigates the development of the thoracic cross-section at the nipple line level during the early stages of life. Unlike the descriptive awareness regarding chest development course, there exist no quantitative references concerning shape, circumference and possible dependencies to age, gender or body weight. The proposed mathematical relations are expected to help create guidelines for more realistic modelling and potential detection of abnormalities. One potential application is lung electrical impedance tomography (EIT) monitoring where accurate chest models are crucial in both extracting reliable parameters for regional ventilation function and design of EIT belts. Despite their importance, such reference data is not readily available for the younger age range due to insufficient data amid the regulations of neonatal imaging. RESULTS: Chest circumference shows the highest correlation to body weight following the relation [Formula: see text] where x is the body weight in grams and f(x) is the chest circumference in cm at the nipple line level. No statistically significant difference in chest circumference between genders was detected. However, the shape indicated signs of both age and gender dependencies with on average boys developing a more rectangular shape than girls from the age of 1 years and 9 months.

Locating Functionalized Gold Nanoparticles Using Electrical Impedance Tomography.

OBJECTIVE: An imaging device to locate functionalised nanoparticles, whereby therapeutic agents are transported from the site of administration specifically to diseased tissues, remains a challenge for pharmaceutical research. Here, we show a new method based on electrical impedance tomography (EIT) to provide images of the location of gold nanoparticles (GNPs) and the excitation of GNPs with radio frequencies (RF) to change impedance permitting an estimation of their location in cell models Methods: We have created an imaging system using quantum cluster GNPs as contrast agent, activated with RF fields to heat the functionalized GNPs, which causes a change in impedance in the surrounding region. This change is then identified with EIT. RESULTS: Images of impedance changes of around 80 ± 4% are obtained for a sample of citrate stabilized GNPs in a solution of phosphate-buffered saline. A second quantification was carried out using colorectal cancer cells incubated with culture media, and the internalization of GNPs into the colorectal cancer cells was undertaken to compare them with the EIT images. When the cells were incubated with functionalised GNPs, the change was more apparent, approximately 40 ± 2%. This change was reflected in the EIT image as the cell area was more clearly identifiable from the rest of the area. SIGNIFICANCE: EIT can be used as a new method to locate functionalized GNPs in human cells and help in the development of GNP-based drugs in humans to improve their efficacy in the future.

Generation of Anatomically Inspired Human Airway Tree Using Electrical Impedance Tomography: A Method to Estimate Regional Lung Filling Characteristics.

The purpose of lung recruitment is to improve and optimize the air exchange flow in the lungs by adjusting the respiratory settings during mechanical ventilation. Electrical impedance tomography (EIT) is a monitoring tool that permits measurement of regional pulmonary filling characteristics or filling index (FI) during ventilation. The conventional EIT system has limitations which compromise the accuracy of the FI. This paper proposes a novel and automated methodology for accurate FI estimation based on EIT images of recruitable regional collapse and hyperdistension during incremental positive end-expiratory pressure. It identifies details of the airway tree (AT) to generate a correction factor to the FIs providing an accurate measurement. Multi-scale image enhancement followed by identification of the AT skeleton with a robust and self-exploratory tracing algorithm is used to automatically estimate the FI. AT tracing was validated using phantom data on a ground-truth lung. Based on generated phantom EIT images, including an established reference, the proposed method results in more accurate FI estimation of 65% in all quadrants compared with the current state-of-the-art. Measured regional filling characteristics were also examined by comparing regional and global impedance variations in clinically recorded data from ten different subjects. Clinical tests on filling characteristics based on extraction of the AT from the resolution enhanced EIT images indicated a more accurate result compared with the standard EIT images.

Integrated Circuits for Medical Ultrasound Applications: Imaging and Beyond.

Medical ultrasound has become a crucial part of modern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the development of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. However, medical ultrasound still faces many challenges including power-efficient driving of transducers, low-noise recording of ultrasound echoes, effective beamforming in a non-linear, high-attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low-noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. This paper concludes with a discussion and recommendations for future research directions.

Development of an in-line magnetometer for flow chemistry and its demonstration for magnetic nanoparticle synthesis.

Despite the wide usage of magnetic nanoparticles, it remains challenging to synthesise particles with properties that exploit each application's full potential. Time consuming experimental procedures and particle analysis hinder process development, which is commonly constrained to a handful of experiments without considering particle formation kinetics, reproducibility and scalability. Flow reactors are known for their potential of large-scale production and high-throughput screening of process parameters. These advantages, however, have not been utilised for magnetic nanoparticle synthesis where particle characterisation is performed, with a few exceptions, post-synthesis. To overcome this bottleneck, we developed a highly sensitive magnetometer for flow reactors to characterise magnetic nanoparticles in solution in-line and in real-time using alternating current susceptometry. This flow magnetometer enriches the flow-chemistry toolbox by facilitating continuous quality control and high-throughput screening of magnetic nanoparticle syntheses. The sensitivity required to monitor magnetic nanoparticle syntheses at the typically low concentrations (<100 mM of Fe) was achieved by comparing the signals induced in the sample and reference cell, each of which contained near-identical pairs of induction and pick-up coils. The reference cell was filled only with air, whereas the sample cell was a flow cell allowing sample solution to pass through. Balancing the flow and reference cell impedance with a newly developed electronic circuit was pivotal for the magnetometer's sensitivity. To showcase its potential, the flow magnetometer was used to monitor two iron oxide nanoparticle syntheses with well-known particle formation kinetics, i.e., co-precipitation syntheses with sodium carbonate and sodium hydroxide as base, which have been previously studied via synchrotron X-ray diffraction. The flow magnetometer facilitated batch (on-line) and flow (in-line) synthesis monitoring, providing new insights into the particle formation kinetics as well as, effect of temperature and pH. The compact lab-scale flow device presented here, opens up new possibilities for magnetic nanoparticle synthesis and manufacturing, including 1) early stage reaction characterisation 2) process monitoring and control and 3) high-throughput screening in combination with flow reactors.

A Versatile Hermetically Sealed Microelectronic Implant for Peripheral Nerve Stimulation Applications.

This article presents a versatile neurostimulation platform featuring a fully implantable multi-channel neural stimulator for chronic experimental studies with freely moving large animal models involving peripheral nerves. The implant is hermetically sealed in a ceramic enclosure and encapsulated in medical grade silicone rubber, and then underwent active tests at accelerated aging conditions at 100°C for 15 consecutive days. The stimulator microelectronics are implemented in a 0.6-µm CMOS technology, with a crosstalk reduction scheme to minimize cross-channel interference, and high-speed power and data telemetry for battery-less operation. A wearable transmitter equipped with a Bluetooth Low Energy radio link, and a custom graphical user interface provide real-time, remotely controlled stimulation. Three parallel stimulators provide independent stimulation on three channels, where each stimulator supports six stimulating sites and two return sites through multiplexing, hence the implant can facilitate stimulation at up to 36 different electrode pairs. The design of the electronics, method of hermetic packaging and electrical performance as well as in vitro testing with electrodes in saline are presented.

An Imaged Based Method for Universal Performance Evaluation of Electrical Impedance Tomography Systems.

This paper describes a simple and reproducible method for universal evaluation of the performance of electrical impedance tomography (EIT) systems using reconstructed images. To address the issues where common electrical parameters are not directly related to the quality of EIT images, based on objective full reference (FR) image quality assessment, the method provides a visually distinguishable hot colormap and two new FR metrics, the global and the more specific 'region of interest'. A passive 16 electrode EIT system using an application specific integrated circuit front-end was used to evaluate the proposed method. The measured results show, both visually and in terms of the proposed FR metrics, the impact on recorded EIT images with different design parameters and non-idealities. The paper also compares the image results of a passive electrode system with a matched 'single variable' active electrode system and demonstrates the merit of an active electrode system for noise interference. A figure of merit based on the FR metrics is proposed.

Thoracic shape changes in newborns due to their position.

The highly compliant nature of the neonatal chest wall is known to clinicians. However, its morphological changes have never been characterized and are especially important for a customised monitoring of respiratory diseases. Here, we show that a device applied on newborns can trace their chest boundary without the use of radiation. Such technology, which is easy to sanitise between patients, works like a smart measurement tape drawing also a digital cross section of the chest. We also show that in neonates the supine position generates a significantly different cross section compared to the lateral ones. Lastly, an unprecedented comparison between a premature neonate and a child is reported.

Model Selection Based Algorithm in Neonatal Chest EIT.

This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients. The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated. The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions.

A Multi-Channel Stimulator With High-Resolution Time-to-Current Conversion for Vagal-Cardiac Neuromodulation.

This paper presents a low power integrated multi-channel stimulator for a cardiac neuroprosthesis designed to restore the parasympathetic control after heart transplantation. The proposed stimulator is based on time-to-current conversion. It replaces the conventional current mode digital-to-analog converter (DAC) that uses tens of microamps for biasing, with a novel capacitor time-based DAC (CT-DAC) offering about 10-bit current amplitude resolution with a bias current of only 250 nA. A stimulator chip was designed in a 0.18 µm CMOS high-voltage (HV) technology. It consists of 16 independent channels, each capable of delivering up to 550 µA stimulus current with a HV output stage that can be operated up to 20 V. The stimulator chip performance was evaluated using both RC equivalent load and a microelectrode array in saline solution. It is power efficient, provides high-resolution current amplitude stimulation, and has good charge balance. The design is suitable for multi-channel neural stimulation applications.

Time Stamp - A Novel Time-to-Digital Demodulation Method for Bioimpedance Implant Applications.

Bioimpedance analysis is a noninvasive and inexpensive technology used to investigate the electrical properties of biological tissues. The analysis requires demodulation to extract the real and imaginary parts of the impedance. Conventional systems use complex architectures such as I-Q demodulation. In this paper, a very simple alternative time-to-digital demodulation method or 'time stamp' is proposed. It employs only three comparators to identify or stamp in the time domain, the crossing points of the excitation signal, and the measured signal. In a CMOS proof of concept design, the accuracy of impedance magnitude and phase is 97.06% and 98.81% respectively over a bandwidth of 10 kHz to 500 kHz. The effect of fractional-N synthesis is analysed for the counter-based zero crossing phase detector obtaining a finer phase resolution (0.51˚ at 500 kHz) using a counter clock frequency ( fclk = 12.5 MHz). Because of its circuit simplicity and ease of transmitting the time stamps, the method is very suited to implantable devices requiring low area and power consumption.

Auditory filter-bank compression improves estimation of signal-to-noise ratio for speech in noise.

Signal-to-noise ratio (SNR) estimation is necessary for many speech processing applications often challenged by nonstationary noise. The authors have previously demonstrated that the variance of spectral entropy (VSE) is a reliable estimate of SNR in nonstationary noise. Based on pre-estimated VSE-SNR relationship functions, the SNR of unseen acoustic environments can be estimated from the measured VSE. This study predicts that introducing a compressive function based on cochlear processing will increase the stability of the pre-estimated VSE-SNR relationship functions. This study demonstrates that calculating the VSE based on a nonlinear filter-bank, simulating cochlear compression, reduces the VSE-based SNR estimation errors. VSE-SNR relationship functions were estimated using speech tokens presented in babble noise comprised of different numbers of speakers. Results showed that the coefficient of determination (R2) of the estimated VSE-SNR relationship functions have absolute percentage improvements of over 26% when using a filter-bank with a compressive function, compared to when using a linear filter-bank without compression. In 2-talker babble noise, the estimation accuracy is more than 3 dB better than other published methods.

A Framework for Adapting Deep Brain Stimulation Using Parkinsonian State Estimates.

The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s.