A Novel Power-Efficient Wireless Multi-channel Recording System for the Telemonitoring of Electroencephalography (EEG)

This research introduces the development of a novel EEG recording system that is modular, batteryless, and wireless (untethered) with the supporting theoretical foundation in wireless communications and related design elements and circuitry. Its modular construct overcomes the EEG scaling problem and makes it easier for reconfiguring the hardware design in terms of the number and placement of electrodes and type of standard EEG system contemplated for use. In this development, portability, lightweight, and applicability to other clinical applications that rely on EEG data are sought. Due to printer tolerance, the 3D printed cap consists of 61 electrode placements. This recording capacity can however extend from 21 (as in the international 10-20 systems) up to 61 EEG channels at sample rates ranging from 250 to 1000 Hz and the transfer of the raw EEG signal using a standard allocated frequency as a data carrier. The main objectives of this dissertation are to (1) eliminate the need for heavy mounted batteries, (2) overcome the requirement for bulky power systems, and (3) avoid the use of data cables to untether the EEG system from the subject for a more practical and less restrictive setting. Unpredictability and temporal variations of the EEG input make developing a battery-free and cable-free EEG reading device challenging. Professional high-quality and high-resolution analog front ends are required to capture non-stationary EEG signals at microvolt levels. The primary components of the proposed setup are the wireless power transmission unit, which consists of a power amplifier, highly efficient resonant-inductive link, rectification, regulation, and power management units, as well as the analog front end, which consists of an analog to digital converter, pre-amplification unit, filtering unit, host microprocessor, and the wireless communication unit. These must all be compatible with the rest of the system and must use the least amount of power possible while minimizing the presence of noise and the attenuation of the recorded signal A highly efficient resonant-inductive coupling link is developed to decrease power transmission dissipation. Magnetized materials were utilized to steer electromagnetic flux and decrease route and medium loss while transmitting the required energy with low dissipation. Signal pre-amplification is handled by the front-end active electrodes. Standard bio-amplifier design approaches are combined to accomplish this purpose, and a thorough investigation of the optimum ADC, microcontroller, and transceiver units has been carried out. We can minimize overall system weight and power consumption by employing battery-less and cable-free EEG readout system designs, consequently giving patients more comfort and freedom of movement. Similarly, the solutions are designed to match the performance of medical-grade equipment. The captured electrical impulses using the proposed setup can be stored for various uses, including classification, prediction, 3D source localization, and for monitoring and diagnosing different brain disorders. All the proposed designs and supporting mathematical derivations were validated through empirical and software-simulated experiments. Many of the proposed designs, including the 3D head cap, the wireless power transmission unit, and the pre-amplification unit, are already fabricated, and the schematic circuits and simulation results were based on Spice, Altium, and high-frequency structure simulator (HFSS) software. The fully integrated head cap to be fabricated would require embedding the active electrodes into the 3D headset and applying current technological advances to miniaturize some of the design elements developed in this dissertation

Design of ASIC Based Electrical Impedance Tomography Microendoscopic System for Prostate Cancer Surgical Marginal Assessment

Prostate cancer is the second most common cancer in the United States. It is typically treated by surgically excising the cancerous section of the prostate. Because there is not always a visible distinction between the healthy and cancerous sections, surgery often leaves some cancerous tissue behind. This is referred to as a positive surgical margin and it requires adjuvant treatment with adverse side effects. Electrical impedance tomography (EIT) is a low-cost low-form-factor method that can be used to assess surgical marginal intraoperatively to ensure that no cancerous tissue is left behind. EIT-based surgical margin assessment works on the principle that the electrical properties of cancerous tissue are different from those of healthy tissue. These differences are small at lower frequencies but become more pronounced at frequencies of 1 MHz and higher. Unfortunately, previous EIT solutions for surgical marginal assessment have been limited to operating frequencies of less than 1 MHz. This thesis presents a custom application-specific integrated circuit (ASIC) analog front end for performing EIT with a signal-to-noise ratio of 75 dB up to an operating frequency of 10 MHz. The custom ASIC was integrated into a 16-electrode EIT system for surgical marginal assessment. The entire system was tested on a saline phantom with a 2 mm bead that represented a cancerous lesion. The EIT system produced single-frequency and multi-frequency images showing the presence of the inclusion

Invited: Can Wi-R enable perpetual IoB nodes?

While the number of wearables is steadily growing, the wearables/person wearing them faces a limitation due to the need for charging all of them every day. To unlock the true power of IoB, we need to make these IoB nodes perpetual. However, that is not possible with today's technology. In this paper, we will debate, whether with the advent of Wi-R protocol that uses the body to communicate at 100X lower energy that BTLE/Wi-Fi, is it going to be possible to enable the long-standing desire of perpetual sensing/actuation nodes for the Internet of Bodies.Comment: 5 pages, 5 figure

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

ULTRA LOW POWER CIRCUITS FOR WEARABLE BIOMEDICAL SENSORS

Ph.DDOCTOR OF PHILOSOPH

Interface Circuits for Microsensor Integrated Systems

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [Recent advances in sensing technologies, especially those for Microsensor Integrated Systems, have led to several new commercial applications. Among these, low voltage and low power circuit architectures have gained growing attention, being suitable for portable long battery life devices. The aim is to improve the performances of actual interface circuits and systems, both in terms of voltage mode and current mode, in order to overcome the potential problems due to technology scaling and different technology integrations. Related problems, especially those concerning parasitics, lead to a severe interface design attention, especially concerning the analog front-end and novel and smart architecture must be explored and tested, both at simulation and prototype level. Moreover, the growing demand for autonomous systems gets even harder the interface design due to the need of energy-aware cost-effective circuit interfaces integrating, where possible, energy harvesting solutions. The objective of this Special Issue is to explore the potential solutions to overcome actual limitations in sensor interface circuits and systems, especially those for low voltage and low power Microsensor Integrated Systems. The present Special Issue aims to present and highlight the advances and the latest novel and emergent results on this topic, showing best practices, implementations and applications. The Guest Editors invite to submit original research contributions dealing with sensor interfacing related to this specific topic. Additionally, application oriented and review papers are encouraged.

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