Low power wireless transceiver for implanted medical devices and neural prostheses

© 2014 Dr. Farhad GoodarzyRising healthcare costs as a fraction of gross domestic product (GDP) are a concern in almost all developing and developed countries, and drive technical innovation in healthcare. Technologies enabling patient monitoring outside of the hospital and during daily activities or delivering continuous therapeutic electrical-stimulation have the potential to drastically improve patient compliance, and potentially enable earlier detection of a number of conditions, leading to drastically lower costs. For this reason, miniaturized medical devices have become an increased focus of the electronics community in recent years. Examples include implantable medical devices (IMD), neural prostheses (NP), embedded neural systems and body area network (BAN) systems. These devices will measure various bio-potentials and relay them in a wireless fashion to some form of base station or directly stimulate the central or peripheral nervous system to help alleviate the symptoms of a disease. Many different biomarkers are of interest brain activity, blood pressure, blood glucose level, etc.) and as a result, several different sensing platforms have been proposed in the literature. Power consumption and size are amongst the significant characteristics of such wireless systems and hence are required to be kept to the minimum possible. In this thesis a low power wireless transceiver for implantable medical devices (IMD), neural prostheses (NP) and embedded neural systems is presented. To design a low power wireless system and also keep the size of the device small, specifically considering the medical implant communication service (MICS) frequency band of operation, first the low power techniques in Analogue and digital design are studied. Then the wireless biomedical related systems are surveyed and their specifications are extracted from the literature. In order to achieve low power consumption and achieve a theoretical basis for the design, a modified pulse position modulation (PPM) technique, called saturated amplified signal (SAS), has been introduced which can reduce the overall and per bit transferred power consumption of the transceiver whilst reducing the complexity of the receiver and transmitter architectures and hence potentially shrinking the size of the implemented circuitry. Modified modulation noise performance and total power consumption of the receiver, based on the modulated signal and the implemented circuit, are presented. This provides a theoretical basis to compare and produce optimal low power wireless transmitters and receivers for biomedical applications. Then various wireless transmitter architectures are studied and analysed and also the proposed transmitter architecture based on SAS modulation is presented. The transmitter achieves 40pJ/b energy efficiency and delivers 500kb/s data using MICS frequency band (402-405MHz). It consumes a measured peak power of 200μW from a 1.2V supply while occupying an active area of 0.0016mm2 in a 130nm technology. In order to achieve a complete system, a receiver is also designed and fabricated in the same technology based on the SAS modulation the exploit the superior performance of a SAS modulated signal. Also other receiver architectures are studied in detail and compared for their performance and cost of implementation. The receiver delivers a data rate of 500 Kb/s, whilst consuming 40 pJ/b transferred power consumption, placing this work amongst the best designs in terms of both data rate and power consumption. The receiver achieves a sensitivity of -80dBm and a peak power consumption of 150 μW. The design is capable of being fully integrated on single chip solutions for surgically implanted bionic systems, wearable devices and neural embedded systems. Finally the conclusion and future work are presented

Implementation of a closed-loop BCI system for real-time speech synthesis under clinical constraints

Closed-loop brain-computer interface (BCI) systems that provide real-time feedback to their users are essential for the synthesis of attempted or imagined speech from intracranial recordings. Here, we describe the implementation of our BCI speech synthesis system, which can be trained with a limited amount of overt speech to produce a continuous stream of audio outputs during subsequent speech imagery tasks. We evaluate (1) the effect of parameter choices on the execution time of individual operations in the BCI loop and (2) the accuracy of predicted outputs. To confirm the feasibility of our approach, we conduct simulations in a pseudo-prospective fashion using recorded datasets from five patients undergoing intracranial epilepsy monitoring. We propose that our system can be used to synthesize different types of speech under specific clinical constraints.N