Ultra-low power mixed-signal frontend for wearable EEGs

Electronics circuits are ubiquitous in daily life, aided by advancements in the chip design industry, leading to miniaturised solutions for typical day to day problems. One of the critical healthcare areas helped by this advancement in technology is electroencephalography (EEG). EEG is a non-invasive method of tracking a person's brain waves, and a crucial tool in several healthcare contexts, including epilepsy and sleep disorders. Current ambulatory EEG systems still suffer from limitations that affect their usability. Furthermore, many patients admitted to emergency departments (ED) for a neurological disorder like altered mental status or seizures, would remain undiagnosed hours to days after admission, which leads to an elevated rate of death compared to other conditions. Conducting a thorough EEG monitoring in early-stage could prevent further damage to the brain and avoid high mortality. But lack of portability and ease of access results in a long wait time for the prescribed patients. All real signals are analogue in nature, including brainwaves sensed by EEG systems. For converting the EEG signal into digital for further processing, a truly wearable EEG has to have an analogue mixed-signal front-end (AFE). This research aims to define the specifications for building a custom AFE for the EEG recording and use that to review the suitability of the architectures available in the literature. Another critical task is to provide new architectures that can meet the developed specifications for EEG monitoring and can be used in epilepsy diagnosis, sleep monitoring, drowsiness detection and depression study. The thesis starts with a preview on EEG technology and available methods of brainwaves recording. It further expands to design requirements for the AFE, with a discussion about critical issues that need resolving. Three new continuous-time capacitive feedback chopped amplifier designs are proposed. A novel calibration loop for setting the accurate value for a pseudo-resistor, which is a crucial block in the proposed topology, is also discussed. This pseudoresistor calibration loop achieved the resistor variation of under 8.25%. The thesis also presents a new design of a curvature corrected bandgap, as well as a novel DDA based fourth-order Sallen-Key filter. A modified sensor frontend architecture is then proposed, along with a detailed analysis of its implementation. Measurement results of the AFE are finally presented. The AFE consumed a total power of 3.2A (including ADC, amplifier, filter, and current generation circuitry) with the overall integrated input-referred noise of 0.87V-rms in the frequency band of 0.5-50Hz. Measurement results confirmed that only the proposed AFE achieved all defined specifications for the wearable EEG system with the smallest power consumption than state-of-art architectures that meet few but not all specifications. The AFE also achieved a CMRR of 131.62dB, which is higher than any studied architectures.Open Acces

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

LOW POWER AND HIGH SIGNAL TO NOISE RATIO BIO-MEDICAL AFE DESIGN TECHNIQUES

The research work described in this thesis was focused on finding novel techniques to implement a low-power and noise Bio-Medical Analog Front End (BMEF) circuit technique to enable high-quality Electrocardiography (ECG) sensing. Usually, an ECG signal and several bio-medical signals are sensed from the human body through a pair of electrodes. The electrical characteristics of the very small amplitude (1u-10mV) signals are corrupted by random noise and have a significant dc offset. 50/60Hz power supply coupling noise is one of the biggest cross-talk signals compared to the thermally generated random noise. These signals are even AFE composed of an Instrumentation Amplifier (IA), which will have a better Common Mode rejection ratio (CMRR). The main function of the AFE is to convert the weak electrical Signal into large signals whose amplitude is large enough for an Analog Digital Converter (ADC) to detect without having any errors. A Variable Gain Amplifier (VGA) is sometimes required to adjust signal amplitude to maintain the dynamic range of the ADC. Also, the Bio-medical transceiver needs an accurate and temperature-independent reference voltage and current for the ADC, commonly known as Bandgap Reference Circuit (BGR). These circuits need to consume as low power as possible to enable these circuits to be powered from the battery. The work started with analysing the existing circuit techniques for the circuits mentioned above and finding the key important improvements required to reach the target specifications. Previously proposed IA is generated based on voltage mode signal processing. To improve the CMRR (119dB), we proposed a current mode-based IA with an embedded DC cancellation technique. State-of-the-art VGA circuits were built based on the degeneration principle of the differential pair, which will enable the variable gain purpose, but none of these techniques discussed linearity improvement, which is very important in modern CMOS technologies. This work enhances the total Harmonic distortion (THD) by 21dB in the worst case by exploiting the feedback techniques around the differential pair. Also, this work proposes a low power curvature compensated bandgap with 2ppm/0C temperature sensitivity while consuming 12.5uW power from a 1.2V dc power supply. All circuits were built in 45nm TSMC-CMOS technology and simulated with all the performance metrics with Cadence (spectre) simulator. The circuit layout was carried out to study post-layout parasitic effect sensitivity

ULTRA LOW POWER CIRCUITS FOR WEARABLE BIOMEDICAL SENSORS

Ph.DDOCTOR OF PHILOSOPH

A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

New Techniques for Multi-Channel Biosignal Acquisition and Low-Power, Low-Resistance-Measurement Systems

Dense electrical recording of biosignals has been developed to provide spatial resolution and precise temporal information for health monitoring, diagnostics, and clinical research. However, more electrodes require more wires, and wiring density quickly becomes a limiting factor. To break this bottleneck, we proposed a frequency-division multiplexing (FDM) based architecture for multi-channel acquisition systems. In this final exam, I present two applications that make use of this FDM technique. The first is an FDM-based multi-channel electromyography (EMG) acquisition system, which demonstrates that the FDM system not only reduces wire count, but also mitigates the effect of low frequency motion artifacts and 50/60 Hz mains interference introduced in the wire. An FDM-based four-channel EMG recording is demonstrated, while carrying all channels over a 3-wire interface, and the system achieves an attenuation of low-frequency cable motion artifacts by 15X an! d 60Hz mains noise coupled in the cable by 62X. A second application that forms the basis of my current research effort is an FDM-based neural recording system with multiple graphene active electrodes. We demonstrated a two-channel system including graphene FET electrodes, a custom integrated circuit (IC) analog front-end (AFE), and digital demodulation. In related multi-channel sensor work, a growing need for ultra-low-power sensors has driven continuous advancement in read-out circuits for temperature, humidity, and pressure. IC-integrated Wheatstone bridges, commonly used, are efficient for large sensor resistance (5k-500kohm), but measuring small resistance (30,000x smaller nominal sensor resistance

Integrated Electronics to Control and Readout Electrochemical Biosensors for Implantable Applications

Biosensors can effectively be used to monitor multiple metabolites such as glucose, lactate, ATP and drugs in the human body. Continuous monitoring of these metabolites is essential for patients with chronic or critical conditions. Moreover, this can be used to tune the dosage of a drug for each individual patient, in order to achieve personalized therapy. Implantable medical devices (IMDs) based on biosensors are emerging as a valid alternative for blood tests in laboratories. They can provide continuous monitoring while reduce the test costs. The potentiostat plays a fundamental role in modern biosensors. A potentiostat is an electronic device that controls the electrochemical cell, using three electrodes, and runs the electrochemical measurement. In particular the IMDs require a low-power, fully-integrated, and autonomous potentiostats to control and readout the biosensors. This thesis describes two integrated circuits (ICs) to control and readout multi-target biosensors: LOPHIC and ARIC. They enable chronoamperometry and cyclic voltammetrymeasurements and consume sub-mW power. The design, implementation, characterisation, and validation with biosensors are presented for each IC. To support the calibration of the biosensors with environmental parameters, ARIC includes circuitry to measure the pHand temperature of the analyte through an Iridiumoxide pH sensor and an off-chip resistor-temperature detector (RTD). In particular, novel circuits to convert resistor value into digital are designed for RTD readout. ARIC is integrated into two IMDs aimed for health-care monitoring and personalized therapy. The control and readout of the embedded sensor arrays have been successfully achieved, thanks to ARIC, and validated for glucose and paracetamol measurements while it is remotely powered through an inductive link. To ensure the security and privacy of IMDs, a lightweight cryptographic system (LCS) is presented. This is the first ASIC implementation of a cryptosystem for IMDs, and is integrated into ARIC. The resulting system provides a unique and fundamental capability by immediately encrypting and signing the sensor data upon its creation within the body. Nano-structures such as Carbon nanotubes have been widely used to improve the sensitivity of the biosensors. However, in most of the cases, they introduce more noise into the measurements and produce a large background current. In this thesis the noise of the sensors incorporating CNTs is studied for the first time. The effect of CNTs as well as sensor geometry on the signal to noise ratio of the sensors is investigated experimentally. To remove the background current of the sensors, a differential readout scheme has been proposed. In particular, a novel differential readout IC is designed and implemented that measures inputcurrents within a wide dynamic range and produces a digital output that corresponds to the -informative- redox current of the biosensor

NASA Tech Briefs, April 2000

Topics covered include: Imaging/Video/Display Technology; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Bio-Medical; Test and Measurement; Mathematics and Information Sciences; Books and Reports

Development of adaptive transducer based on biological sensory mechanism

textAn adaptive sensor concept and prototype has been developed based on a sensing element which is analogous to and inspired by the arrangement of outer hair cells and inner hair cells between the basilar membrane and tectorial membrane which form the organ of corti in mammalian cochlea. The bio-inspired design was supported by development of a bond graph model of the electromotility (active response) of outer hair cells. Outer hair cells perform like actuators and simulation results using this model are compared with physiological data found in the literature to verify its characteristic response. Insight gained from the model is used to develop a sensor structure analogous to the organ of corti and designed to measure acceleration. A piezoelectric bimorph was selected as the transducer basis, and a bond graph model of the bimorph in an accelerometer configuration was formulated to aid control design and simulation. There is no published data regarding the type of information transmitted among the inner hair cells, outer hair cells, and brain. Consequently, a controller intended to adjust the adaptation process similar to what might exist in the cochlear system has been developed for the sensor and based on a model referenced adaptive control algorithm. Simulations verify that the algorithm can successfully control and enhance performance of the sensor. Practicability of the design is evaluated by a series of experiments on a prototype. This study focused on using a controller structure that was programmed, implemented, and tested using programmable logic based on FPGA technology. The experiments evaluated how well the adaptive sensor could meet a specified performance requirement. Implementation issues that arise, such as the need for differentiators in the adaptive controller or internal propagation of vibration within the sensor structure, hinder the tuning ability. Nevertheless, the trends indicate that the algorithm can meet the desired performance if certain limitations can be overcome. Finally, recommendations have been made for expansion of the research in such fields as an alternative structure for tuning, sensor networking, and reference sensor configuration.Mechanical Engineerin