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

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

Low-Power Low-Noise CMOS Analog and Mixed-Signal Design towards Epileptic Seizure Detection

About 50 million people worldwide suffer from epilepsy and one third of them have seizures that are refractory to medication. In the past few decades, deep brain stimulation (DBS) has been explored by researchers and physicians as a promising way to control and treat epileptic seizures. To make the DBS therapy more efficient and effective, the feedback loop for titrating therapy is required. It means the implantable DBS devices should be smart enough to sense the brain signals and then adjust the stimulation parameters adaptively. This research proposes a signal-sensing channel configurable to various neural applications, which is a vital part for a future closed-loop epileptic seizure stimulation system. This doctoral study has two main contributions, 1) a micropower low-noise neural front-end circuit, and 2) a low-power configurable neural recording system for both neural action-potential (AP) and fast-ripple (FR) signals. The neural front end consists of a preamplifier followed by a bandpass filter (BPF). This design focuses on improving the noise-power efficiency of the preamplifier and the power/pole merit of the BPF at ultra-low power consumption. In measurement, the preamplifier exhibits 39.6-dB DC gain, 0.8 Hz to 5.2 kHz of bandwidth (BW), 5.86-μVrms input-referred noise in AP mode, while showing 39.4-dB DC gain, 0.36 Hz to 1.3 kHz of BW, 3.07-μVrms noise in FR mode. The preamplifier achieves noise efficiency factor (NEF) of 2.93 and 3.09 for AP and FR modes, respectively. The preamplifier power consumption is 2.4 μW from 2.8 V for both modes. The 6th-order follow-the-leader feedback elliptic BPF passes FR signals and provides -110 dB/decade attenuation to out-of-band interferers. It consumes 2.1 μW from 2.8 V (or 0.35 μW/pole) and is one of the most power-efficient high-order active filters reported to date. The complete front-end circuit achieves a mid-band gain of 38.5 dB, a BW from 250 to 486 Hz, and a total input-referred noise of 2.48 μVrms while consuming 4.5 μW from the 2.8 V power supply. The front-end NEF achieved is 7.6. The power efficiency of the complete front-end is 0.75 μW/pole. The chip is implemented in a standard 0.6-μm CMOS process with a die area of 0.45 mm^2. The neural recording system incorporates the front-end circuit and a sigma-delta analog-to-digital converter (ADC). The ADC has scalable BW and power consumption for digitizing both AP and FR signals captured by the front end. Various design techniques are applied to the improvement of power and area efficiency for the ADC. At 77-dB dynamic range (DR), the ADC has a peak SNR and SNDR of 75.9 dB and 67 dB, respectively, while consuming 2.75-mW power in AP mode. It achieves 78-dB DR, 76.2-dB peak SNR, 73.2-dB peak SNDR, and 588-μW power consumption in FR mode. Both analog and digital power supply voltages are 2.8 V. The chip is fabricated in a standard 0.6-μm CMOS process. The die size is 11.25 mm^2. The proposed circuits can be extended to a multi-channel system, with the ADC shared by all channels, as the sensing part of a future closed-loop DBS system for the treatment of intractable epilepsy

Resource-efficient algorithms and circuits for highly-scalable BMI channel architectures

The study of the human brain has for long fascinated mankind. This organ that controls all cognitive processes and physical actions remains, to this day, among the least understood biological systems. Several billions of neurons form intricate interconnected networks communicating information through through complex electrochemical activities. Electrode arrays, such as for EEG, ECoG, and MEAs (microelectrode arrays), have enabled the observation of neural activity through recording of these electrical signals for both investigative and clinical applications. Although MEAs are widely considered the most invasive such method for recording, they do however provide highest resolution (both spatially and temporally). Due to close proximity, each microelectrode can pick up spiking activity from multiple neurons. This thesis focuses on the design and implementation of novel circuits and systems suitable for high channel count implantable neural interfaces. Implantability poses stringent requirements on the design, such as ultra-low power, small silicon footprint, reduced communication bandwidth and high efficiency to avoid information loss. The information extraction chain typically involves signal amplification and conditioning, spike detection, and spike sorting to determine the spatial and time firing pattern of each neuron. This thesis first provides a background to the origin and basic electrophysiology of these biopotential signals followed by a thorough review of the relevant state-of-the circuits and systems for facilitating the neural interface. Within this context, novel front-end circuits are presented for achieving resource-constrained biopotential amplification whilst additionally considering the signal dynamics and realistic requirements for effective classification. Specifically, it is shown how a band-limited biopotential amplifier can reduce power requirements without compromising detectability. Furthermore through the development of a novel automatic gain control for neural spike recording, the dynamic range of the signal in subsequent processing blocks can be maintained in multichannel systems. This is particularly effective if now considering systems that no longer requiring independent tuning of amplification gains for each individual channel. This also alleviates the common requirement to over-spec the resolution in data conversion therefore saving power, area and data capacity. Dealing with basic spike detection and feature extraction, a novel circuit for maxima detection is presented for identifying and signalling the onset of spike peaks and troughs. This is then combined with a novel non-linear energy operator (NEO) preprocessor and applied to spike detection. This again contributes to the general theme of achieving a calibration-free multi-channel system that is signal-driven and adaptive. Another original contribution herein includes a spike rate encoder circuit suitable for applications that are not are not affected by providing multi-unit responses. Finally, spike sorting (feature extraction and clustering) is examined. A new method for feature extraction is proposed based on utilising the extrema of the first and second derivatives of the signal. It is shown that this provides an extremely resource-efficient metric than can achieve noise immunity than other methods of comparable complexity. Furthermore, a novel unsupervised clustering method is proposed which adaptively determines the number of clusters and assigns incoming spikes to appropriate cluster on-the-fly. In addition to high accuracy achieved by the combination of these methods for spike sorting, a major advantage is their low-computational complexity that renders them readily implementable in low-power hardware.Open Acces

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

Multichannel biomedical telemetry system using delta modulation

Telemetering of biomedical data from unrestrained subjects requires a system to be compact, reliable and efficient. A survey of the existing multi-channel biomedical telemetry showed that most of the systems employ analogue or uncoded (digital) techniques of encoding biomedical signals. These techniques are less reliable, employ wider bandwidth and are difficult to implement compared to the coded (digital) techniques of modulation. A theoretical study of the coded techniques of modulation for encoding biomedical signals showed-that pulse code modulation, though more efficient, calls for extensive circuitry and makes it expensive and difficult to implement. Delta modulation and delta sigma modulation were found to be simpler, easier to Implement and efficient. [Continues.

Design of agile signal conditioning circuits for microelectromechanical sensors

Microelectromechanical systems (MEMS) are used in many applications to detect physical parameters and convert them to an electrical signal. The output of MEMS-based transducers is usually not suitable to be directly processed in the digital or the analog domain, and they could be as small as femto farads in capacitive sensing and micro volts in resistive sensing. Consequently, high sensitivity signal conditioning circuits are essential. In this thesis, it is shown that both the noise and input capacitance are important parameters to ensure optimal capacitive sensing. The dominant noise source in MEMS conditioning circuits is flicker noise, and one of the best methods to mitigate flicker noise is the chopping technique. Three different chopping techniques are considered: single chopper amplifier (SCA), dual chopper amplifier (DCA), and two-stage single chopper amplifier (TCA). Also, their sensitivity and power consumption based on the total gain and sensing capacitance are extracted. It is shown that the distribution of gain between the two stages in the DCA and TCA has a significant effect on the sensitivity, and, based on this distribution, the sensitivity and power consumption change significantly. For small sensor capacitances, the highest sensitivity could be achieved by a DCA because of its ability to decrease the noise floor and the input sensor capacitance simultaneously. A novel DCA is proposed to reach higher sensitivity and reduced power consumption. In this DCA, two supply voltages are utilized, and the second stage is composed of two parallel paths that improve the SNR and provide two gain settings. This circuit is fabricated in the GlobalFoundries 0.13 μm CMOS technology. The measurement results show a power consumption of 2.66 μW for the supply voltage of 0.7 V and of 3.26 μW for the supply voltage of 1.2 V. The single path DCA has a gain of 34 dB with bandwidth of 4 kHz and input noise floor of 25 nV/√Hz. The dual path DCA has a gain of 38 dB with bandwidth of 3 kHz and input noise floor of 40 nV/√Hz. To be able to detect the signal near DC frequencies, another circuit is proposed which has a configurable bandwidth and a sub-μHz noise corner frequency. This circuit is composed of three stages, and three chopping frequencies are used to mitigate the flicker noise of the three stages. The simulated circuit is designed in the GlobalFoundries 0.13 μm CMOS technology with supply voltages of 0.4 V and 1.2 V. The total power consumption is of 6.7 μW. A gain of 68 dB and bandwidths of 1, 10, 100 and 1000 Hz are achieved. The input referred noise floor is of 20.5 nV/√Hz and the design attains a good power efficiency factor of 4.0. In the capacitive mode, the noise floor is of 3.6 zF for a 100 fF capacitance sensor

Ion camera development for real–time acquisition of localised pH responses using the CMOS based 64×64–pixel ISFET sensor array technology

This thesis presents the development and test of an integrated ion camera chip for monitoring highly localised ion fluxes of electrochemical processes using an ion sensitive sensor array. Ionic concentration fluctuations are shown to travel across the sensor array as a result of citric acid injection and the BZ-reaction. The imaging capability of non-equilibrium chemical activities is also demonstrated monitoring self-assembling micrometre sized polyoxometalate tubular and membranous architectures. The sufficient spatial resolution for the visualisation of the 10-60 µm wide growing trajectories is provided by the dense sensor array containing 64×64 pixels. In the case of citric acid injection and the BZ-reaction the ion camera chip is shown to be able to resolve pH differences with resolution as low as the area of one pixel. As a result of the transient and volatile ionic fluxes high time resolution is required, thus the signal capturing can be performed in real.time at the maximum sampling rate of 40 µs per pixel, 10.2 ms per array. The extracted sensor data are reconstructed into ionic images and thus the ionic activities can be displayed as individual figures as well as continuous video recordings. This chip is the first prototype in the envisioned establishment of a fully automated CMOS based ion camera system which would be able to image the invisible activity of ions using a single microchip. In addition the capability of detecting ultra-low level pH oscillations in the extracellular space is demonstrated using cells of the slime mould organism. The detected pH oscillations with extent of ~0.022 pH furthermore raise the potential for observing fluctuations of ion currents in cell based tissue environments. The intrinsic noise of the sensor devices are measured to observe noise effect on the detected low level signals. It is experimentally shown that the used ion sensitive circuits, similarly to CMOS, also demonstrate 1/f noise. In addition the reference bias and pH sensitivity of the measured noise is confirmed. Corresponding to the measurement results the noise contribution is approximated with a 28.2 µV peak-to-peak level and related to the 450 µV �+/- 70 µV peak-to-peak oscillations amplitudes of the slime mould. Thus a maximum intrinsic noise contribution of 6.2 �+/- 1.2 % is calculated. A H+ flickering hypothesis is also presented that correlates the pH fluctuations on the surface of the device with the intrinsic 1/f noise. The ion camera chip was fabricated in an unmodified 4-metal 0.35 µm CMOS process and the ionic imaging technology was based on a 64�×64-pixel ion sensitive field effect transistor (ISFET) array. The high-speed and synchronous operation of the 4096 ISFET sensors occupying 715.8×715.8 µm space provided a spatial resolution as low as one pixel. Each pixel contained 4 transistors with 10.2×10.2 µm layout dimensions and the pixels were separated by a 1 µm separation gap. The ion sensitive silicon nitride based passivation layer was in contact with the floating gates of the ISFET sensors. It allowed the capacitive measurements of localised changes in the ionic concentrations, e.g. pH, pNa, on the surface of the chip. The device showed an average ionic sensitivity of 20 mV/pH and 9 mV/pNa. The packaging and encapsulation was carried out using PGA-100 chip carriers and two-component epoxies. Custom designed printed circuit boards (PCBs) were used to provide interface between the ISFET array chip and the data acquisition system. The data acquisition and extraction part of the developed software system was based on LabVIEW, the data processing was carried out on Matlab platform