34 research outputs found

    Clockless Continuous-Time Neural Spike Sorting: Method, Implementation and Evaluation

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    In this paper, we present a new method for neural spike sorting based on Continuous Time (CT) signal processing. A set of CT based features are proposed and extracted from CT sampled pulses, and a complete event-driven spike sorting algorithm that performs classification based on these features is developed. Compared to conventional methods for spike sorting, the hardware implementation of the proposed method does not require any synchronisation clock for logic circuits, and thus its power consumption depend solely on the spike activity. This has been implemented using a variable quantisation step CT analogue to digital converter (ADC) with custom digital logic that is driven by level crossing events. Simulation results using synthetic neural data shows a comparable accuracy compared to template matching (TM) and Principle Components Analysis (PCA) based discrete sampled classification

    Low Power Circuits for Smart Flexible ECG Sensors

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    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

    Continuous-time acquisition of biosignals using a charge-based ADC topology

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    This paper investigates continuous-time (CT) signal acquisition as an activity-dependent and nonuniform sampling alternative to conventional fixed-rate digitisation. We demonstrate the applicability to biosignal representation by quantifying the achievable bandwidth saving by nonuniform quantisation to commonly recorded biological signal fragments allowing a compression ratio of ā‰ˆ5 and 26 when applied to electrocardiogram and extracellular action potential signals, respectively. We describe several desirable properties of CT sampling, including bandwidth reduction, elimination/reduction of quantisation error, and describe its impact on aliasing. This is followed by demonstration of a resource-efficient hardware implementation. We propose a novel circuit topology for a charge-based CT analogue-to-digital converter that has been optimized for the acquisition of neural signals. This has been implemented in a commercially available 0.35 Ī¼m CMOS technology occupying a compact footprint of 0.12 mm 2 . Silicon verified measurements demonstrate an 8-bit resolution and a 4 kHz bandwidth with static power consumption of 3.75 Ī¼W from a 1.5 V supply. The dynamic power dissipation is completely activity-dependent, requiring 1.39 pJ energy per conversion

    Smart Sensor Networks For Sensor-Neural Interface

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    One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface. In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5Ī¼m CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18Ī¼m CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18Ī¼m CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system. The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption

    Power efficient, event driven data acquisition and processing using asynchronous techniques

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    PhD ThesisData acquisition systems used in remote environmental monitoring equipment and biological sensor nodes rely on limited energy supply soured from either energy harvesters or battery to perform their functions. Among the building blocks of these systems are power hungry Analogue to Digital Converters and Digital Signal Processors which acquire and process samples at predetermined rates regardless of the monitored signalā€™s behavior. In this work we investigate power efficient event driven data acquisition and processing techniques by implementing an asynchronous ADC and an event driven power gated Finite Impulse Response (FIR) filter. We present an event driven single slope ADC capable of generating asynchronous digital samples based on the input signalā€™s rate of change. It utilizes a rate of change detection circuit known as the slope detector to determine at what point the input signal is to be sampled. After a sample has been obtained itā€™s absolute voltage value is time encoded and passed on to a Time to Digital Converter (TDC) as part of a pulse stream. The resulting digital samples generated by the TDC are produced at a rate that exhibits the same rate of change profile as that of the input signal. The ADC is realized in 0.35mm CMOS process, covers a silicon area of 340mm by 218mm and consumes power based on the input signalā€™s frequency. The samples from the ADC are asynchronous in nature and exhibit random time periods between adjacent samples. In order to process such asynchronous samples we present a FIR filter that is able to successfully operate on the samples and produce the desired result. The filter also poses the ability to turn itself off in-between samples that have longer sample periods in effect saving power in the process

    Recent Advances in Neural Recording Microsystems

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    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

    Data Conversion Within Energy Constrained Environments

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    Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

    A time-based energy-efficient analog-to-digital converter

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 123-129).Dual-slope converters use time to perform analog-to-digital conversion but require 2N+1 clock cycles to achieve N bits of precision. We describe a novel algorithm that also uses time to perform analog-to-digital conversion but requires 5N clock cycles to achieve N bits of precision via a successive sub-ranging technique. The algorithm requires one asynchronous comparator, two capacitors, one current source, and a state machine. Amplification of two is achieved without the use of an explicit amplifier by simply doing things twice in time. The use of alternating Voltage-to-Time and Time-to-Voltage conversions provides natural error cancellation of comparator offset and delay, 1/f noise, and switching charge-injection. The use of few components and an effcient mechanism for amplification and error cancellation allow for energy-effcient operation: In a 0.35 [mu]m implementation, we were able to achieve 12 bits of DNL limited precision or 11 bits of thermal noise-limited precision at a sampling frequency of 31.25kHz with 75 [mu] W of total analog and digital power consumption. These numbers yield a thermal noise-limited energy-efficiency of 1.17pJ per quantization level making it one of the most energy-effcient converters to date in the 10 to 12 bit precision range.(cont.) This converter could be useful in low-power hearing aids after analog gain control has been performed on a microphone front-end. An 8 bit audio version of our converter in a 0.18 [mu] m process consumes 960nW and yields an energy-efficiency of 0.12pJ per quantization level, perhaps the lowest ever reported. This converter may be useful in biomedical and sensor-network applications where energy-efficiency is paramount. Our algorithm has inherent advantages in time-to-digital conversion. It can be generalized to easily digitize power-law functions of its input, and it can be used in an interleaved architecture if higher speed is desired.by Heemin Yi Yang.Ph.D

    ULTRA LOW POWER CIRCUITS FOR WEARABLE BIOMEDICAL SENSORS

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    Ph.DDOCTOR OF PHILOSOPH

    Systems and Methods for the Spectral Calibration of Swept Source Optical Coherence Tomography Systems

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    This dissertation relates to the transition of the state of the art of swept source optical coherence tomography (SS-OCT) systems to a new realm in which the image acquisition speed is improved by an order of magnitude. With the aid of a better quality imaging technology, the speed-up factor will considerably shorten the eye-exam clinical visits which in turn improves the patient and doctor interaction experience. These improvements will directly lower associated medical costs for eye-clinics and patients worldwide. There are several other embodiments closely related to Optical Coherence Tomography (OCT) that could benefit from the ideas presented in this dissertation including: optical coherence microscopy (OCM), full-field OCT (FF-OCT), optical coherence elastography (OCE), optical coherence tomography angiography (OCT-A), anatomical OCT (aOCT), optical coherence photoacoustic microscopy (OC-PAM), micro optical coherence tomography (Āµ OCT), among others. In recent decades, OCT has established itself as the de-facto imaging process that most ophthalmologists refer to in their clinical practices. In a broader sense, optical coherence tomography is used in applications when low penetration and high resolution are desired. These applications include different fields of biomedical sciences including cardiology, dermatology, and pulmonary related sciences. Many other industrial applications including quality control and precise measurements have also been reported that are related to the OCT technology. Every new iteration of OCT technology has always come about with advanced signal processing and data acquisition algorithms using mixed-signal architectures, calibration and signal processing techniques. The existing industrial practices towards data acquisition, processing, and image creation relies on conventional signal processing design flows, which extensively employ continuous/discrete techniques that are both time-consuming and costly. The ideas presented in this dissertation can take the technology to a new dimension of quality of service
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