Implantable CMOS Biomedical Devices

The results of recent research on our implantable CMOS biomedical devices are reviewed. Topics include retinal prosthesis devices and deep-brain implantation devices for small animals. Fundamental device structures and characteristics as well as in vivo experiments are presented

Advances in Scalable Implantable Systems for Neurostimulation Using Networked ASICs

Neurostimulation is a known method for restoring lost functions to neurologically impaired patients. This paper describes recent advances in scalable implantable stimulation systems using networked application specific integrated circuits (ASICs). It discusses how they can meet the ever-growing demand for high-density neural interfacing and long-term reliability. A detailed design example of an implantable (inductively linked) scalable stimulation system for restoring lower limb functions in paraplegics after spinal cord injury is presented. It comprises a central hub implanted at the costal margin and multiple Active Books which provide the interface for stimulating nerve roots in the cauda equina. A 16-channel stimulation system using four Active Books is demonstrated. Each Active Book has an embedded ASIC, which is responsible for initiating stimulus current to the electrodes. It also ensures device safety by monitoring temperature, humidity, and peak electrode voltage during stimulation. The implant hub was implemented using a microcontroller-based circuit. The ASIC in the Active Book was fabricated using XFAB’s 0.6-µm high-voltage CMOS process. The stimulation system does not require an accurate reference clock in the implant. Measured results are provided

Self-folding 3D micro antennas for implantable medical devices

Tese de Doutoramento em Engenharia Biomédica.Recent advances in device miniaturization have been enabling smart and small implantable medical devices. These are often powered by bulky batteries whose dimensions represent one of the major bottlenecks on further device miniaturization. However, alternative powering methods, such as electromagnetic waves, do not rely on stored energy and are capable of providing high energy densities per unit of area, thus increasing the potential for device miniaturization. Hence, we envision an implanted medical device with an integrated miniaturized antenna, capable of receiving a radiofrequency signal from an exterior source, and converting it to a DC signal, thus enabling remote powering. This thesis addresses the analysis, design, fabrication and characterization of novel 3D micro antennas that can be integrated on 500 × 500 × 500 μm3 cubic devices, and used for wireless power transfer purposes. The analysis is built upon the theory of electrically small antennas in lossy media, and the antenna design takes into consideration miniaturization techniques which are compatible with the antenna fabrication process. For the antenna fabrication, a methodology that combines conventional planar photolithography techniques and self-folding was used. While photolithography allows the easy patterning of virtually every desired planar antenna configuration with reproducible feature precision, and the flexibility to easily and precisely change the antenna geometry and size, self-folding allows assembly of the fabricated planar patterns into a 3D structure in a highly parallel and scalable manner. After fabrication, we characterized the fabricated antennas by measuring their S-parameters and radiation patterns, demonstrating their efficacy at 2 GHz when immersed in dispersive media such as water. This step required the development and test of multiple characterization setups based on connectors, RF probes and transmission lines and the use of an anechoic chamber. Moreover, we successfully show that the antennas can wireless transfer energy to power an LED, highlighting a proof of concept for practical applications. Our findings suggest that self-folding micro antennas could provide a viable solution for powering tiny micro devices.Os recentes avanços das tecnologias de miniaturização têm permitido o desenvolvimento de dispositivos médicos implantáveis inteligentes e mais pequenos. Estes são muitas vezes alimentados por baterias volumosas cujas dimensões limitam o nível de miniaturização alcançável por um micro dispositivo. No entanto, existem formas alternativas de alimentar estes dispositivos que não dependem de energia armazenada, tais como ondas eletromagnéticas, que são capazes de providenciar uma elevada densidade de energia por unidade de área, aumentando assim o potencial de miniaturização dos dispositivos. Desta forma, visionamos um dispositivo médico implantado, com uma antena miniaturizada e integrada, capaz de receber um sinal de rádio frequência a partir de uma fonte externa, e convertê-lo num sinal DC, permitindo assim a alimentação remota do aparelho. Esta tese apresenta a análise, desenho, fabrico e caracterização de micro antenas 3D, passíveis de serem integradas em micro dispositivos cúbicos (500 × 500 × 500 μm3), e utilizadas para fins de transferência de energia sem fios. A análise assenta na teoria das antenas eletricamente pequenas em meios com perdas, e o design da antena considera técnicas de miniaturização de antenas. Para o fabrico da antena foi utilizada uma metodologia que combina técnicas de fotolitografia planar e auto-dodragem (self-folding). Enquanto a fotolitografia permite a padronização de virtualmente todos os tipos de configurações planares de forma precisa, reprodutível, e com a flexibilidade para se mudar rapidamente a geometria e o tamanho da antena, o self-folding permite a assemblagem dos painéis planares fabricados numa estrutura 3D. Depois do fabrico, as antenas foram caracterizadas medindo os seus parâmetros S e diagramas de radiação, demonstrando a sua eficácia a 2 GHz quando imersas num meio dispersivo, tal como água. Esta etapa exigiu o desenvolvimento e teste de várias setups de caracterização com base em conectores, sondas de RF e linhas de transmissão, e ainda o uso de uma câmara anecóica. Além disso, mostramos com sucesso que as micro antenas podem receber e transferir o energia para um LED acendendo-o, destacando assim esta prova de conceito para aplicações práticas. Os nossos resultados sugerem que estas micro antenas auto-dobráveis podem fornecer uma solução viável para alimentar micro dispositivos implantáveis muito pequenos.Fundação para a Ciência e a Tecnologia (FCT) bolsa SFRH/BD/63737/2009

Glucose-powered neuroelectronics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.by Benjamin Isaac Rapoport.Ph.D

An investigation of extraocular and intraocular wireless communication techniques on a retinal prosthesis system

Retinitis Pigmentosa (RP) and Age-related Macular Degeneration (AMD) are two genetic ocular diseases that cause gradual visual impairments which will eventually lead to blindness as a result of damage in the retina. In the cases of people suffering from RP and AMD, it has been found out that 95% of the photoreceptors are damaged, while interestingly majority of the bipolar and ganglion cells that are responsible for the nerve stimulation remain intact. This is where a retinal prosthesis system comes into the picture. Retinal prosthesis is a prosthetic device that is aimed to assume the functionality of the damaged photoreceptors and produce stimulations to the bipolar and ganglion cells for a visual perception. Typically, a retinal prosthesis system comprises of two major components: an image capturing unit and an array of microelectrode. While a lot of studies have been conducted on each major component, the development of the wireless link between the two components has been mostly overlooked. It is clear that the two components are not physically connected and a data exchange is required between the two. This thesis aims to bridge the knowledge gap in this area by addressing the following research questions: “What is the most suitable frequency band for a wireless link in a retinal prosthesis system?” and “What kind of antenna would generate the most optimal performance under the constraints introduced by a retinal prosthesis system?

Rehabilitación visual con implantes o prótesis de visión artificial

En el presente trabajo se realiza una revisión bibliográfica enfocada a entender los implantes o prótesis de visión artificial que existen en el mercado como alternativas para el tratamiento de las personas con ceguera secundaria a un daño en los fotorreceptores o células del epitelio pigmentario de la retina. Se explica la función de los fotorreceptores en el sistema visual humano así como el procesamiento de la información visual normal como base para entender lo que una prótesis que suple los fotorreceptores debe ser capaz de realizar. Se describe brevemente la historia de la estimulación neuronal en las distintas porciones del sistema visual. Se explica la composición general de las prótesis retinianas, se hace mención de las diversas prótesis retinianas que se han desarrollado como prototipos y a nivel investigación y se habla mas a detalle de las únicas dos prótesis retinianas que cuentan con aprobación para su uso en humanos. Finalmente se mencionan los requisitos mínimos para poder hablar de una visión funcional útil y los resultados visuales que se obtienen con las prótesis, así como los candidatos susceptibles a ser implantados y gozar de los beneficios de las prótesis retinianas.Máster en Rehabilitación Visua

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Development and modelling of a versatile active micro-electrode array for high density in-vivo and in-vitro neural signal investigation

The electrophysiological observation of neurological cells has allowed much knowledge to be gathered regarding how living organisms are believed to acquire and process sensation. Although much has been learned about neurons in isolation, there is much more to be discovered in how these neurons communicate within large networks. The challenges of measuring neurological networks at the scale, density and chronic level of non invasiveness required to observe neurological processing and decision making are manifold, however methods have been suggested that have allowed small scale networks to be observed using arrays of micro-fabricated electrodes. These arrays transduce ionic perturbations local to the cell membrane in the extracellular fluid into small electrical signals within the metal that may be measured. A device was designed for optimal electrical matching to the electrode interface and maximal signal preservation of the received extracellular neural signals. Design parameters were developed from electrophysiological computer simulations and experimentally obtained empirical models of the electrode-electrolyte interface. From this information, a novel interface based signal filtering method was developed that enabled high density amplifier interface circuitry to be realised. A novel prototype monolithic active electrode was developed using CMOS microfabrication technology. The device uses the top metallization of a selected process to form the electrode substrate and compact amplification circuitry fabricated directly beneath the electrode to amplify and separate the neural signal from the baseline offsets and noise of the electrode interface. The signal is then buffered for high speed sampling and switched signal routing. Prototype 16 and 256 active electrode array with custom support circuitry is presented at the layout stage for a 20 μm diameter 100 μm pitch electrode array. Each device consumes 26.4 μW of power and contributes 4.509 μV (rms) of noise to the received signal over a controlled bandwidth of 10 Hz - 5 kHz. The research has provided a fundamental insight into the challenges of high density neural network observation, both in the passive and the active manner. The thesis concludes that power consumption is the fundamental limiting factor of high density integrated MEA circuitry; low power dissipation being crucial for the existence of the surface adhered cells under measurement. With transistor sizing, noise and signal slewing each being inversely proportional to the dc supply current and the large power requirements of desirable ancillary circuitry such as analogue-to-digital converters, a situation of compromise is approached that must be carefully considered for specific application design

Development of bio-batteries based on electrospun membranes

Fundação para a Ciência e a Tecnologia (FCT-MCTES) under the grant SFRH/BD/69306/201

Communication and energy delivery architectures for personal medical devices

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-232).Advances in sensor technologies and integrated electronics are revolutionizing how humans access and receive healthcare. However, many envisioned wearable or implantable systems are not deployable in practice due to high energy consumption and anatomically-limited size constraints, necessitating large form-factors for external devices, or eventual surgical re-implantation procedures for in-vivo applications. Since communication and energy-management sub-systems often dominate the power budgets of personal biomedical devices, this thesis explores alternative usecases, system architectures, and circuit solutions to reduce their energy burden. For wearable applications, a system-on-chip is designed that both communicates and delivers power over an eTextiles network. The transmitter and receiver front-ends are at least an order of magnitude more efficient than conventional body-area networks. For implantable applications, two separate systems are proposed that avoid reimplantation requirements. The first system extracts energy from the endocochlear potential, an electrochemical gradient found naturally within the inner-ear of mammals, in order to power a wireless sensor. Since extractable energy levels are limited, novel sensing, communication, and energy management solutions are proposed that leverage duty-cycling to achieve enabling power consumptions that are at least an order of magnitude lower than previous work. Clinical measurements show the first system demonstrated to sustain itself with a mammalian-generated electrochemical potential operating as the only source of energy into the system. The second system leverages the essentially unlimited number of re-charge cycles offered by ultracapacitors. To ease patient usability, a rapid wireless capacitor charging architecture is proposed that employs a multi-tapped secondary inductive coil to provide charging times that are significantly faster than conventional approaches.by Patrick Philip Mercier.Ph.D