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

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces

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

Nano-Watt Modular Integrated Circuits for Wireless Neural Interface.

In this work, a nano-watt modular neural interface circuit is proposed for ECoG neuroprosthetics. The main purposes of this work are threefold: (1) optimizing the power-performance of the neural interface circuits based on ECoG signal characteristics, (2) equipping a stimulation capability, and (3) providing a modular system solution to expand functionality. To achieve these aims, the proposed system introduces the following contributions/innovations: (1) power-noise optimization based on the ECoG signal driven analysis, (2) extreme low-power analog front-ends, (3) Manchester clock-edge modulation clock data recovery, (4) power-efficient data compression, (5) integrated stimulator with fully programmable waveform, (6) wireless signal transmission through skin, and (7) modular expandable design. Towards these challenges and contributions, three different ECoG neural interface systems, ENI-1, ENI-16, and ENI-32, have been designed, fabricated, and tested. The first ENI system(ENI-1) is a one-channel analog front-end and fabricated in a 0.25µm CMOS process with chopper stabilized pseudo open-loop preamplifier and area-efficient SAR ADC. The measured channel power, noise and area are 1.68µW at 2.5V power-supply, 1.69µVrms (NEF=2.43), and 0.0694mm^2, respectively. The fabricated IC is packaged with customized miniaturized package. In-vivo human EEG is successfully measured with the fabricated ENI-1-IC. To demonstrate a system expandability and wireless link, ENI-16 IC is fabricated in 0.25µm CMOS process and has sixteen channels with a push-pull preamplifier, asynchronous SAR ADC, and intra-skin communication(ISCOM) which is a new way of transmitting the signal through skin. The measured channel power, noise and area are 780nW, 4.26µVrms (NEF=5.2), and 2.88mm^2, respectively. With the fabricated ENI-16-IC, in-vivo epidural ECoG from monkey is successfully measured. As a closed-loop system, ENI-32 focuses on optimizing the power performance based on a bio-signal property and integrating stimulator. ENI-32 is fabricated in 0.18µm CMOS process and has thirty-two recording channels and four stimulation channels with a cyclic preamplifier, data compression, asymmetric wireless transceiver (Tx/Rx). The measured channel power, noise and area are 140nW (680nW including ISCOM), 3.26µVrms (NEF=1.6), and 5.76mm^2, respectively. The ENI-32 achieves an order of magnitude power reduction while maintaining the system performance. The proposed nano-watt ENI-32 can be the first practical wireless closed-loop solution with a practically miniaturized implantable device.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98064/1/schang_1.pd

Time Synchronization in Multimodal Wireless Cyber-Physical Systems: A Wearable Biopotential Acquisition and Collaborative Brain-Computer Interface Paradigm

Die Forschung zu Brain-Computer Interface (BCI) hat in den letzten drei Jahren riesige Fortschritte gemacht, nicht nur im Bereich der menschlich gesteuerten Roboter, der Steuerung von Prothesen, des Interpretierens von Wörtern, der Kommunikation in einer Virtual Reality Umgebung oder der Computerspiele, sondern auch in der kognitiven Neurologie. Patienten, die unter enormen motorischen Dysfunktionen leiden (letztes Stadium Amyotrophe Lateralsklerose) könnten solch ein BCI System als alternatives Medium zur Kommunikation durch die eigene Gehirnaktivität nutzen. Neuste Studien zeigen, dass die Verwendung dieses BCI Systems in einem Gruppenexperiment helfen kann die menschliche Entscheidungstreffung deutlich zu verbessern. Dies ist ein neues Feld des BCI, nämlich das Collaborative BCI. Einerseits erfordert die Durchführung solch eines Gruppenexperiments drahtlose Hochleistungs-EEG Systeme, basierend auf BCI, welches kostengünstig und tragbar sein sollte und Langzeit-Monitoring hochwertiger EEG Daten sicherstellt. Andererseits ist es erforderlich, eine Zeitsynchronisierung zwischen den einzelnen BCI Systemen einzusetzen, damit diese für ein Gruppenexperiment zum Einsatz kommen können. Diese Herausforderungen setzten die Grundlage dieser Doktorarbeit. In dieser Arbeit wurde ein neuartiges, nicht invasives, modulares, biopotentiales Messsystem entwickelt: Dieses kann Breitband (0.5 Hz–150 Hz) Biopotentiale ableiten, bestehend aus Elektromyographie (EMG), Elektrokardiografie (EKG), Elektroencephalografie (EEG), wurde insgesamt bezeichnet als ExG bzw. das Messsystem als ExG-System benannt. Die Modularität des ExG-Systems erlaubt 8 bis hin zu 256 Kanäle zu konfigurieren, je nach Anforderung, ob in einen textilen Schlauch eingekapselt zur Erfassung von EMG Signalen, in eine textilen Weste zur Erfassung von ECG Signalen oder in eine textilen Kappe zur Erfassung von EEG Signalen. Der Einbau des ExG-Systems in eine Kappe wurde ebenfalls im Rahmen der Arbeit entwickelt. Der letzte Schritt des ExG-Systems zeigt niedriges Eingangsrauschen von 7 µVvon-Spitze-zu-Spitze und benötigt 41 mW/Kanal der Datenaufnahme im aktiven Zustand. Ein WiFi-Modul wurde für eine drahtlose Datenübertragung an einen ferngesteuerten PC in das ExG-System eingebaut. Um mit dem entwickelte System BCI Anwendungen zu ermöglichen, wurde ein akustisch und visuell evozierter Potenzialstimulator (SSVEP/AEP Stimulator) entwickelt. In eben diesem wurde ein Rasperry Pi als Zentralrechner benutzt und ein Bash basiertes Player-Skript iii einprogrammiert, das Mediadaten (Video, Audio, Ton) aus der Angabe einer Lookup Tabelle (LUT) in ihr Linux Betriebssystem spielt. Im Rahmen der Arbeit wurde eine Zeitsynchronisierung an einigen dieser ExG-Systeme mit Hilfe von einer eingebetteten Hardware/Softwarelösung durchgeführt. Die Hardwareteile bestehen aus einigen Leiterplatten, nämlich Sync Modulen mit einem Quarzoszillator, einem Mikrocontroller und einem Funkmodul (Hierbei Bluetooth 4.0). Eines von diesen ist das Sync-Addon, das mit jedem Messsystem (z.B. ExG-System) das zu synchronisieren ist, angeschlossen wird. Das andere bezeichnet man als Sync-Center, das an die Datenverarbeitungsrechner angehängt wird. Das Softwareteil übernimmt den Zeitsynchronisierungsmechanismus mit Hilfe eines funkbasierten Protokolls. Im Rahmen der Arbeit wurde ein neues energieeffizientes pairwise broadcast Zeitsynchronisationsprotokoll (PBS), welches nur theoretisch vorgestellt wurde, experimentell verifiziert. Außerdem wurde es mit anderen bestehenden Zeitsynchronisationsprotokollen auf dem aktuellen Stand der Technik evaluiert, basierend auf den Ergebnissen der gleichen Hardwareebene. In der letzten Iteration der Sync-Module wurde ein durchschnittlicher Synchronisationsfehler von 2 ms, den Konfidenzintervall von 95% berücksichtigend, erlangt. Da für Collaborative BCI, P300, ein Ereignis bezogenes Potenzial mit dem Auslöseimpuls, der 300−500 ms nach dem Vorgang eintritt, eingestellt wurde, ist die erreichte Synchronisationsgenauigkeit genügend, um solch ein Experiment durchzuführen.Brain-computer interface (BCI) has experienced the last three decades tremendous technological advances not only in the field of human controller robotics, or in controlling prosthesis, or in spelling words, or in interacting with a virtual reality environment, or in gaming but also in cognitive neuroscience. Patients suffering from severe motoric dysfunction (e.g. late stage of Amyotrophic Lateral Sclerosis) may utilise such a BCI system as an alternative medium of communication by mental activity. Recently studies have shown that usage of such BCI in a group experiment can help to improve human decision making. This is a new field of BCI, namely collaborative BCI. On one hand, performing such group experiments require wireless, high density EEG system based BCI which should be low-cost, wearable and provide long time monitoring of good quality EEG data. On the other hand time synchronization is required to be established among a group of BCI systems if they are employed for such a group experiments. These drawbacks set the foundation of this thesis work. In this work a novel non-invasive modular biopotential measurement system which can acquire wideband (0.15 Hz–200 Hz) biopotential signals consisting Electromyography (EMG), Electrocardiography (ECG), Electroencephalography (EEG) together called ExG, following ExG-system was designed. The modularity of the ExG-system allows it to be configured from 8 up to 256 channels according to the requirement if it’s to be encapsulated in a textile sleeve for recording of EMG signals, or in a textile vest for recording of ECG signals, or in a textile cap for recording of EEG signals. The assembly of the ExG-system in cap was also developed during the scope of the work. The final iteration of the ExG-system exhibits low input noise of 7 µVpeak-to-peak and require 41 mW/channel of data recording in active state. A WiFi module was embedded into the ExG-system for wireless data transmission to a remote PC. To enable the developed system for BCI applications a steady-state visually/auditory evoked potential stimulator (SSVEP/AEP stimulator) incorporating a Raspberry Pi as a main computer and a bash based player script which plays media data (video, pictures, sound) as defined in a lookup table in the Linux operating system of it. Within the scope of the work time synchronization among a group of such ExG-systems was further realized with the help of an embedded hardware/software solution. The hardware part consists of two different PCB sync modules that are incorporated with a crystal oscillator a microcontroller, a radio module (in this case Bluetooth 4.0). One of them is called the v sync-addon which is to be attached to each of the measurement systems (e.g. ExG-system) that are to be synchronized and the sync-center which is to be attached to the remote PC. On the software part, a wireless time synchronization protocol exchanging timing information among the sync-center and sync-addons must establish tight time synchronization between the ExG-system. Within the framework of this work, a novel time synchronization protocol energy efficient pairwise broadcast synchronization protocol (PBS) that was only theoretically proposed before but not evaluated on real hardware was experimentally evaluated with the developed sync modules. Moreover a benchmarking with other state-of-the-art existing time synchronization protocols based on the results from same hardware platform were drawn. In the final iteration of sync modules an average synchronization error of 2 ms was achieved considering the 95% of confidence interval. Since for collaborative BCI, P300, an event related potential was triggered with the stimuli that occur 300−500 ms after the event, the achieved synchronization accuracy is sufficient to conduct such experiments

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

Conformable transistors for bioelectronics

The diversity of network disruptions that occur in patients with neuropsychiatric disorders creates a strong demand for personalized medicine. Such approaches often take the form of implantable bioelectronic devices that are capable of monitoring pathophysiological activity for identifying biomarkers to allow for local and responsive delivery of intervention. They are also required to transmit this data outside of the body for evaluation of the treatment’s efficacy. However, the ability to perform these demanding electronic functions in the complex physiological environment with minimum disruption to the biological tissue remains a big challenge. An optimal fully implantable bioelectronic device would require each component from the front-end to the data transmission to be conformable and biocompatible. For this reason, organic material-based conformable electronics are ideal candidates for components of bioelectronic circuits due to their inherent flexibility, and soft nature. In this work, first an organic mixed-conducting particulate composite material (MCP) able to form functional electronic components and non-invasively acquire high–spatiotemporal resolution electrophysiological signals by directly interfacing human skin is presented. Secondly, we introduce organic electrochemical internal ion-gated transistors (IGTs) as a high-density, high-amplification sensing component as well as a low leakage, high-speed processing unit. Finally, a novel wireless, battery-free strategy for electrophysiological signal acquisition, processing, and transmission that employs IGTs and an ionic communication circuit (IC) is introduced. We show that the wirelessly-powered IGTs are able to acquire and modulate neurophysiological data in-vivo and transmit them transdermally, eliminating the need for any hard Si-based electronics in the implant

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system