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Majerus, Steve JWireless, Implantable Microsystem for Chronic Bladder Pressure Monitoring
Doctor of Philosophy, Case Western Reserve University, 2014, EECS - Electrical Engineering
This work describes the design and testing of a wireless implantable bladder pressure sensor suitable for chronic implantation in humans. The sensor was designed to fulfill the unmet need for a chronic bladder pressure sensing device in urological fields such as urodynamics for diagnosis and neuromodulation for bladder control. Neuromodulation would particularly benefit from a wireless bladder pressure sensor providing real-time pressure feedback to an implanted stimulator, resulting in greater bladder capacity while using less power. The pressure sensing system consists of an implantable microsystem, an external RF receiver, and a wireless battery charger. The implant is small enough to be cystoscopically implanted within the bladder wall, where it is securely held and shielded from the urine stream, protecting both the device and the patient. The implantable microsystem consists of a custom application-specific integrated circuit (ASIC), pressure transducer, rechargeable battery, and wireless telemetry and recharging antennas. Because the battery capacity is extremely limited, the ASIC was designed using an ultra-low-power methodology in which power is dynamically allocated to instrumentation and telemetry circuits by a power management unit. A low-power regulator and clock oscillator set the minimum current draw at 7.5 µA and instrumentation circuitry is operated at low duty cycles to transmit 100-Hz pressure samples while consuming 74 µA. An adaptive transmission activity detector determines the minimum telemetry rate to limit broadcast of unimportant samples. Measured results indicated that the power management circuits produced an average system current of 16 µA while reducing the number of transmitted samples by more than 95% with typical bladder pressure signals. The wireless telemetry range of the system was measured to be 35 cm with a bit-error-rate of 10-3, and the battery was wirelessly recharged at distances up to 20 cm. A novel biocompatible packaging method consisting of a silicone-nylon mesh membrane and a compliant silicone gel was developed to protect the sensor from water ingress while only reducing the sensor sensitivity by 5%. Dynamic offset removal circuitry extended the system dynamic range to 2,900 cm H2O but limited the sensor AC accuracy to 3.7 cm H2O over a frequency range of 0.002 – 50 Hz. The DC accuracy of the sensor was measured to be approximately 2.6 cm H2O (0.9% full-scale). Functionality of wired prototypes was confirmed in feline and canine animal models, and wireless prototypes were implanted in a female calf large-animal model. Measured in vivo pressure recordings of bladder contractions correlated well with reference catheters (r =0.893–0.994).

Committee:

Steven Garverick (Advisor); Swarup Bhunia (Committee Co-Chair); Margot Damaser (Committee Member); Pedram Mohseni (Committee Member); Christian Zorman (Committee Member)

Subjects:

Biomedical Engineering; Electrical Engineering

Keywords:

Implantable electronics; bladder pressure sensor; low-power; integrated circuit; wireless; chronic implantation; bladder implant; pressure sensor; power management; adaptive transmission rate; wireless battery recharge

Narasimhan, SeetharamUltralow-Power and Robust Implantable Neural Interfaces: An Algorithm-Architecture-Circuit Co-Design Approach
Doctor of Philosophy, Case Western Reserve University, 2012, EECS - Computer Engineering
Implantable systems are used in various contexts for interfacing with the body and for providing real-time monitoring and control capability. In particular, implantable neural interfaces can be used to radically improve our understanding of the nervous system and to provide precise treatments for a variety of neurological problems. However, these systems require significant computing power to perform real-time in-situ analysis of neural signals to recognize behaviorally meaningful patterns which are used to trigger appropriate corrective actions. Due to the tight area and power constraints of neural implants, it is important to develop novel algorithm-architecture-circuit co-design approaches for efficient implementation of neural signal analysis. First, we develop an algorithmic framework which is suitable for ultralow-power hardware implementation while simultaneously satisfying emerging design requirements like reliability and security. The algorithm is based on building a dynamic hierarchical multi-level vocabulary of neural patterns in the wavelet domain. The vocabulary-based analysis allows recognition of neural patterns at multiple levels (spike, burst, and pattern of bursts across multiple channels) and transmission of recorded data with large compression, thus, saving power and communication bandwidth of the integrated telemetry device. Hardware implementation of the proposed algorithm aims at reducing system power through choice of appropriate architecture and circuit-level design techniques. We show that a super-threshold design operating at a much higher frequency can achieve comparable energy dissipation as a sub-threshold low-frequency design through application of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. We propose an architecture-level preferential design approach for further energy reduction at the cost of graceful degradation in output signal quality under voltage scaling and parameter variations. Considering the emerging need of secure computing in implantable systems, we analyze the various security threats in the proposed system. We exploit the vocabulary-based encoding of neural signals to realize an ultra-lightweight data obfuscation solution. Furthermore, we consider an emerging security threat namely, hardware Trojan attack, where an adversary introduces malicious modifications of a circuit during design or fabrication. We analyze the effectiveness of different Trojan attacks in implantable systems and develop side-channel analysis based Trojan detection approaches.

Committee:

Swarup Bhunia, PhD (Committee Chair); Christos Papachristou, PhD (Committee Member); Steve Garverick, PhD (Committee Member); Hillel Chiel, PhD (Committee Member)

Subjects:

Biomedical Engineering; Computer Engineering

Keywords:

Implantable Electronics; Neural Interface Systems; Vocabulary-based Approach; Preferential Design; Algorithm-Architecture-Circuit Co-design; Security; Hardware Trojan