Low-Noise Micro-Power Amplifiers for Biosignal Acquisition

There are many different types of biopotential signals, such as action potentials (APs), local field potentials (LFPs), electromyography (EMG), electrocardiogram (ECG), electroencephalogram (EEG), etc. Nerve action potentials play an important role for the analysis of human cognition, such as perception, memory, language, emotions, and motor control. EMGs provide vital information about the patients which allow clinicians to diagnose and treat many neuromuscular diseases, which could result in muscle paralysis, motor problems, etc. EEGs is critical in diagnosing epilepsy, sleep disorders, as well as brain tumors. Biopotential signals are very weak, which requires the biopotential amplifier to exhibit low input-referred noise. For example, EEGs have amplitudes from 1 μV [microvolt] to 100 μV [microvolt] with much of the energy in the sub-Hz [hertz] to 100 Hz [hertz] band. APs have amplitudes up to 500 μV [microvolt] with much of the energy in the 100 Hz [hertz] to 7 kHz [hertz] band. In wearable/implantable systems, the low-power operation of the biopotential amplifier is critical to avoid thermal damage to surrounding tissues, preserve long battery life, and enable wirelessly-delivered or harvested energy supply. For an ideal thermal-noise-limited amplifier, the amplifier power is inversely proportional to the input-referred noise of the amplifier. Therefore, there is a noise-power trade-off which must be well-balanced by the designers. In this work I propose novel amplifier topologies, which are able to significantly improve the noise-power efficiency by increasing the effective transconductance at a given current. In order to reject the DC offsets generated at the tissue-electrode interface, energy-efficient techniques are employed to create a low-frequency high-pass cutoff. The noise contribution of the high-pass cutoff circuitry is minimized by using power-efficient configurations, and optimizing the biasing and dimension of the devices. Sufficient common-mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are achieved to suppress common-mode interferences and power supply noises. Our design are fabricated in standard CMOS processes. The amplifiers’ performance are measured on the bench, and also demonstrated with biopotential recordings

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

A Power-Efficient Bio-Potential Acquisition Device with DS-MDE Sensors for Long-Term Healthcare Monitoring Applications

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery

Recent Advances in Neural Recording Microsystems

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

A reconfigurable medically cohesive biomedical front-end with ΣΔ ADC in 0.18µm CMOS

This paper presents a generic programmable analog front-end (AFE) for acquisition and digitization of various biopotential signals. This includes a lead-off detection circuit, an ultra-low current capacitively coupled signal conditioning stage with programmable gain and bandwidth, a new mixed signal automatic gain control (AGC) mechanism and a medically cohesive reconfigurable ΣΔ ADC. The full system is designed in UMC 0.18μm CMOS. The AFE achieves an overall linearity of more 10 bits with 0.47μW power consumption. The ADC provides 2nd order noise-shaping while using single integrator and an ENOB of ~11 bits with 5μW power consumption. The system was successfully verified for various ECG signals from PTB database. This system is intended for portable batteryless u-Healthcare devices

Amplifiers in Biomedical Engineering: A Review from Application Perspectives

Continuous monitoring and treatment of various diseases with biomedical technologies and wearable electronics has become significantly important. The healthcare area is an important, evolving field that, among other things, requires electronic and micro-electromechanical technologies. Designed circuits and smart devices can lead to reduced hospitalization time and hospitals equipped with high-quality equipment. Some of these devices can also be implanted inside the body. Recently, various implanted electronic devices for monitoring and diagnosing diseases have been presented. These instruments require communication links through wireless technologies. In the transmitters of these devices, power amplifiers are the most important components and their performance plays important roles. This paper is devoted to collecting and providing a comprehensive review on the various designed implanted amplifiers for advanced biomedical applications. The reported amplifiers vary with respect to the class/type of amplifier, implemented CMOS technology, frequency band, output power, and the overall efficiency of the designs. The purpose of the authors is to provide a general view of the available solutions, and any researcher can obtain suitable circuit designs that can be selected for their problem by reading this survey

Low Power Analog Front End for ExG Acquisition with Automatic Gain Control and Analog Classification

Cardiovascular diseases have been known to cause large number of deaths globally. For prevention and early detection of these diseases, continuous monitoring of ecg signals is required. With recent advances in IC technology, implantable ICs have seen the light of the day. Considering the im-plantable devices, power consumed by the system needs to be as less as possible without sacrificing the performance of the readout circuit

A True 1V 1µW Biomedical Front End with Reconfigurable ADC for Self powered Smarter IoT Healthcare Systems

This work proposes an ultralow power highly linear analog front-end (AFE) with an input dynamic range from 200μVpp to 20mVpp. The system consists of a signal conditioning instrumentation amplifier (IA), two programmable gain amplifiers (PGA), a mixed signal automatic gain control (AGC), two sample and hold (S/H), a 10 bit successive approximation register (SAR) analog to digital converter (ADC), and a ΣΔ modulator with 10 bit effective number of bits (ENOB). A highly linear capacitively-coupled IA is achieved by increasing its feedback factor. Moreover, a transconductance (gm) cancellation technique is proposed for achieving a high common mode rejection ratio (CMRR). The conditioned signal is digitized using a SAR ADC for an input range of 200μVpp to 2mVpp, and, an opamp-shared ΣΔ ADC for an input range of 2mVpp to 20mVpp. The selection between the two ADCs is done by the AGC. The full system is designed using 1V supply in UMC 0.18μm CMOS technology. The AFE (IA and the two PGAs) achieves an overall linearity of more than 12 bits, for an input range of 200μVpp to 20mVpp while consuming 300nW with a bandwidth of 0.05 - 250Hz. The power consumption of the SAR ADC is 40nW while operating at a sampling frequency of 1KHz. The ΣΔ ADC consumes 300nW at a sampling frequency of 32KHz with an OSR of 32. The proposed system is intended to be powered by an energy scavenging circuit without compromising its own performance. The system was successfully tested for an ECG signal obtained from PTB database

A CMOS Analog Front-End for Tunnelling Magnetoresistive Spintronic Sensing Systems

This paper presents a CMOS readout circuit for an integrated and highly-sensitive tunnel-magnetoresistive (TMR) sensor. Based on the characterization of the TMR sensor in the finite-element simulation, using COMSOL Multiphysics, the circuit including a Wheatstone bridge and an analogue front-end (AFE) circuit, were designed to achieve low-noise and low-power sensing. We present a transimpedance amplifier (TIA) that biases and amplifies a TMR sensor array using switched-capacitors external noise filtering and allows the integration of TMR sensors on CMOS without decreasing the measurement resolution. Designed using TSMC 0.18 μm 1V technology, the amplifier consumes 160 nA at 1.8 V supply to achieve a dc gain of 118 dB and a bandwidth of 3.8 MHz. The results confirm that the full system is able to detect the magnetic field in the pico-Tesla range with low circuit noise (2.297 pA/√Hz) and low power consumption (86 μW). A concurrent reduction in the power consumption and attenuation of noise in TMR sensors makes them suitable for long-term deployment in spintronic sensing systems

An implantable mixed-signal CMOS die for battery-powered in vivo blowfly neural recordings

© 2018 A mixed-signal die containing two differential input amplifiers, a multiplexer and a 50 KSPS, 10-bit SAR ADC, has been designed and fabricated in a 0.35 μm CMOS process for in vivo neural recording from freely moving blowflies where power supplied voltage drops quickly due to the space/weight limited insufficient capacity of the battery. The designed neural amplifier has a 66 + dB gain, 0.13 Hz-5.3 KHz bandwidth and 0.39% THD. A 20% power supply voltage drop causes only a 3% change in amplifier gain and 0.9-bit resolution degrading for SAR ADC while the on-chip data modulation reduces the chip size, rendering the designed chip suitable for battery-powered applications. The fabricated die occupies 1.1 mm2 while consuming 238 μW, being suitable for implantable neural recordings from insects as small as a blowfly for electrophysiological studies of their sensorimotor control mechanisms. The functionality of the die has been validated by recording the signals from identified interneurons in the blowfly visual system