40 research outputs found
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
Recommended from our members
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 thisĂ finalĂ 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.Ă 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 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
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 0.25”m CMOS process with chopper stabilized pseudo open-loop preamplifier and area-efficient SAR ADC. The measured channel power, noise and area are 1.68”W at 2.5V power-supply, 1.69”Vrms (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 0.25”m 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, 4.26”Vrms (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 0.18”m 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), 3.26”Vrms (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 GehirnaktivitÀt 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, nĂ€mlich das Collaborative BCI. Einerseits erfordert die DurchfĂŒhrung solch eines
Gruppenexperiments drahtlose Hochleistungs-EEG Systeme, basierend auf BCI, welches
kostengĂŒnstig 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 fĂŒr 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 Hzâ150 Hz) Biopotentiale ableiten, bestehend aus
Elektromyographie (EMG), Elektrokardiografie (EKG), Elektroencephalografie (EEG),
wurde insgesamt bezeichnet als ExG bzw. das Messsystem als ExG-System benannt. Die
ModularitÀt des ExG-Systems erlaubt 8 bis hin zu 256 KanÀle 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 ”Vvon-Spitze-zu-Spitze und benötigt 41 mW/Kanal der
Datenaufnahme im aktiven Zustand. Ein WiFi-Modul wurde fĂŒr eine drahtlose
DatenĂŒbertragung 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 durchgefĂŒhrt. Die Hardwareteile
bestehen aus einigen Leiterplatten, nÀmlich 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 angehĂ€ngt wird. Das Softwareteil ĂŒbernimmt 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. AuĂerdem 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% berĂŒcksichtigend, erlangt. Da fĂŒr Collaborative BCI, P300,
ein Ereignis bezogenes Potenzial mit dem Auslöseimpuls, der 300â500 ms nach dem
Vorgang eintritt, eingestellt wurde, ist die erreichte Synchronisationsgenauigkeit genĂŒgend,
um solch ein Experiment durchzufĂŒhren.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 Hzâ200 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 itâs 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 ”Vpeak-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 300â500 ms after
the event, the achieved synchronization accuracy is sufficient to conduct such experiments
Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control
After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis
An implantable micro-system for neural prosthesis control and sensory feedback restoration in amputees
In this work, the prototype of an electronic bi-directional interface between the Peripheral
Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is
composed of two Integrated Circuits (ICs): a standard CMOS device for neural recording and
a High Voltage (HV) CMOS device for neural stimulation. The integrated circuits have been
realized in two different 0.35ÎŒm CMOS processes available fromAustriaMicroSystem(AMS).
The recoding IC incorporates 8 channels each including the analog front-end and the A/D
conversion based on a sigma delta architecture. It has a total area of 16.8mm2 and exhibits
an overall power consumption of 27.2mW. The neural stimulation IC is able to provide biphasic
current pulses to stimulate 8 electrodes independently. A voltage booster generates a
17V voltage supply in order to guarantee the programmed stimulation current even in case
of high impedances at the electrode-tissue interface in the order of tens of kÂ. The stimulation
patterns, generated by a 5-bit current DAC, are programmable in terms of amplitude,
frequency and pulse width. Due to the huge capacitors of the implemented voltage boosters,
the stimulation IC has a wider area of 18.6mm2. In addition, a maximum power consumption
of 29mW was measured. Successful in-vivo experiments with rats having a TIME
electrode implanted in the sciatic nerve were carried out, showing the capability of recording
neural signals in the tens of microvolts, with a global noise of 7ÎŒVrms , and to selectively
elicit the tibial and plantarmuscles using different active sites of the electrode.
In order to get a completely implantable interface, a biocompatible and biostable package
was designed. It hosts the developed ICs with the minimal electronics required for their
proper operation. The package consists of an alumina tube closed at both extremities by
two ceramic caps hermetically sealed on it. Moreover, the two caps serve as substrate for
the hermetic feedthroughs to enable the device powering and data exchange with the external
digital controller implemented on a Field-Programmable Gate Array (FPGA) board. The
package has an outer diameter of 7mm and a total length of 26mm. In addition, a humidity
and temperature sensor was also included inside the package to allow future hermeticity
and life-time estimation tests.
Moreover, a wireless, wearable and non-invasive EEG recording system is proposed in order
to improve the control over the artificial limb,by integrating the neural signals recorded from
the PNS with those directly acquired from the brain. To first investigate the system requirements,
a Component-Off-The-Shelf (COTS) device was designed. It includes a low-power 8-
channel acquisition module and a Bluetooth (BT) transceiver to transmit the acquired data
to a remote platform. It was designed with the aimof creating a cheap and user-friendly system
that can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates and with a maximum
bandwidth of 524Hz. It was realized on a 19cm2 custom PCB with a maximum power consumption
of 270mW
Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations
Novel Multimodal Sensing Systems for Wearable Knee Health Assessment
Wearable technologies for healthcare represent a popular research area, as they can provide quantitative metrics during rehabilitation, enable long-term, at-home monitoring of chronic conditions, and facilitate preventativeâversus reactiveâmedical interventions. Moreover, their low cost makes them accessible to broad subject populations and enables more frequent measures of biomarkers. Such technologies are particularly useful for areas of medicine where the diagnostic or evaluation tools are expensive, not readily available, or time consuming. Orthopedics, in particular joint health assessment, is an area where wearable devices may provide clinicians and patients with more readily available quantitative data. The objective of this research is to investigate wearable, multimodal sensing technologies to facilitate joint health and rehabilitation monitoring, ultimately providing a âjoint health scoreâ based on evaluation of joint acoustics, electrical bioimpedance, inertial measures, and temperature data. This joint health score may be employed in various applicationsâincluding during rehabilitation after an acute injury and management of joint diseases, such as arthritisâproviding an actionable metric for physicians based on the underlying physiological changes of the joint itself. This work specifically investigates the hardware for such a system. First, we examined microphones suited for wearable applications (e.g., miniature, inexpensive) that still provide robust measurements in terms of signal quality and consistency for repeated measurements. Second, we implemented a microcontroller-based system to sample high-throughput audio data as well as lower-rate electrical bioimpedance, inertial, and temperature data, which was incorporated into a fully untethered âbrace.â Importantly, this work provides the fundamental hardware system for wearable knee joint health assessment.Ph.D
Recent Advances and Future Trends in Nanophotonics
Nanophotonics has emerged as a multidisciplinary frontier of science and engineering. Due to its high potential to contribute to breakthroughs in many areas of technology, nanophotonics is capturing the interest of many researchers from different fields. This Special Issue of Applied Sciences on âRecent advances and future trends in nanophotonicsâ aims to give an overview on the latest developments in nanophotonics and its roles in different application domains. Topics of discussion include, but are not limited to, the exploration of new directions of nanophotonic science and technology that enable technological breakthroughs in high-impact areas mainly regarding diffraction elements, detection, imaging, spectroscopy, optical communications, and computing