Voltage-Mode Multifunction Biquadratic Filters Using New Ultra-Low-Power Differential Difference Current Conveyors

This paper presents two low-power voltage-mode multifunction biquadratic filters using differential difference current conveyors. Each proposed circuit employs three differential difference current conveyors, two grounded capacitors and two grounded resistors. The low-voltage ultra-low-power differential difference current conveyor is used to provide low-power consumption of the proposed filters. By appropriately connecting the input and output terminals, the proposed filters can provide low-pass, band-pass, high-pass, band-stop and all-pass voltage responses at high-input terminals, which is a desirable feature for voltage-mode operations. The natural frequency and the quality factor can be orthogonally set by adjusting the circuit components. For realizing all the filter responses, no inverting-type input signal requirements as well as no component-matching conditional requirements are imposed. The incremental parameter sensitivities are also low. The characteristics of the proposed circuits are simulated by using PSPICE simulators to confirm the presented theory

Low-Power Wireless Medical Systems and Circuits for Invasive and Non-Invasive Applications

Approximately 75% of the health care yearly budget of public health systems around the world is spent on the treatment of patients with chronic diseases. This, along with advances on the medical and technological fields has given rise to the use of preventive medicine, resulting on a high demand of wireless medical systems (WMS) for patient monitoring and drug safety research. In this dissertation, the main design challenges and solutions for designing a WMS are addressed from system-level, using off-the-shell components, to circuit implementation. Two low-power oriented WMS aiming to monitor blood pressure of small laboratory animals (implantable) and cardiac-activity (12-lead electrocardiogram) of patients with chronic diseases (wearable) are presented. A power consumption vs. lifetime analysis to estimate the monitoring unit lifetime for each application is included. For the invasive/non-invasive WMS, in-vitro test benches are used to verify their functionality showing successful communication up to 2.1 m/35 m with the monitoring unit consuming 0.572 mA/33 mA from a 3 V/4.5 V power supply, allowing a two-year/ 88-hour lifetime in periodic/continuous operation. This results in an improvement of more than 50% compared with the lifetime commercial products. Additionally, this dissertation proposes transistor-level implementations of an ultra-low-noise/low-power biopotential amplifier and the baseband section of a wireless receiver, consisting of a channel selection filter (CSF) and a variable gain amplifier (VGA). The proposed biopotential amplifier is intended for electrocardiogram (ECG)/ electroencephalogram (EEG)/ electromyogram (EMG) monitoring applications and its architecture was designed focused on improving its noise/power efficiency. It was implemented using the ON-SEMI 0.5 µm standard process with an effective area of 360 µm2. Experimental results show a pass-band gain of 40.2 dB (240 mHz - 170 Hz), input referred noise of 0.47 Vrms, minimum CMRR of 84.3 dBm, NEF of 1.88 and a power dissipation of 3.5 µW. The CSF was implemented using an active-RC 4th order inverse-chebyshev topology. The VGA provides 30 gain steps and includes a DC-cancellation loop to avoid saturation on the sub-sequent analog-to-digital converter block. Measurement results show a power consumption of 18.75 mW, IIP3 of 27.1 dBm, channel rejection better than 50 dB, gain variation of 0-60dB, cut-off frequency tuning of 1.1-2.29 MHz and noise figure of 33.25 dB. The circuit was implemented in the standard IBM 0.18 µm CMOS process with a total area of 1.45 x 1.4 mm^(2). The presented WMS can integrate the proposed biopotential amplifier and baseband section with small modifications depending on the target signal while using the low-power-oriented algorithm to obtain further power optimization

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

PROCESS AWARE ANALOG-CENTRIC SINGLE LEAD ECG ACQUISITION AND CLASSIFICATION CMOS FRONTEND

The primary objective of this research work is the development of a low power single-lead ECG analog front-end (AFE) architecture which includes acquisition, digitization, process aware efficient gain and frequency control mechanism and a low complexity classifier for the detecting asystole, extreme bardycardia and tachycardia. Recent research on ECG recording systems focuses on the design of a compact single-lead wearable/portable devices with ultra-low-power consumption and in-built hardware for diagnosis and prognosis. Since, the amplitude of the ECG signal varies from hundreds of µV to a few mV, and has a bandwidth of DC to 250 Hz, conventional front-ends use an instrument amplifier followed by a programmable gain amplifier (PGA) to amplify the input ECG signal appropriately. This work presents an mixed signal ECG fronted with an ultra-low power two-stage capacitive-coupled signal conditioning circuit (or an AFE), providing programmable amplification along with tunable 2nd order high pass and lowpass filter characteristics. In the contemporary state-of-the-art ECG recording systems, the gain of the amplifier is controlled by external digital control pins which are in turn dynamically controlled through a DSP. Therefore, an efficient automatic gain control mechanism with minimal area overhead and consuming power in the order of nano watts only. The AGC turns the subsequent ADC on only after output of the PGA (or input of the ADC) reaches a level for which the ADC achieves maximum signal-to-noise-ratio (SNR), hence saving considerable startup power and avoiding the use of DSP. Further, in any practical filter design, the low pass cut-off frequency is prone to deviate from its nominal value across process and temperature variations. Therefore, post-fabrication calibration is essential, before the signal is fed to an ADC, to minimize this deviation, prevent signal degradation due to aliasing of higher frequencies into the bandwidth for classification of ECG signals, to switch to low resolution processing, hence saving power and enhances battery lifetime. Another short-coming noticed in the literature published so far is that the classification algorithm is implemented in digital domain, which turns out to be a power hungry approach. Moreover, Although analog domain implementations of QRS complexes detection schemes have been reported, they employ an external micro-controller to determine the threshold voltage. In this regard, finally a power-efficient low complexity CMOS fully analog classifier architecture and a heart rate estimator is added to the above scheme. It reduces the overall system power consumption by reducing the computational burden on the DSP. The complete proposed scheme consists of (i) an ultra-low power QRS complex detection circuit using an autonomous dynamic threshold voltage, hence discarding the need of any external microcontroller/DSP and calibration (ii) a power efficient analog classifier for the detection of three critical alarm types viz. asystole, extreme bradycardia and tachycardia. Additionally, a heart rate estimator that provides the number of QRS complexes within a period of one minute for cardiac rhythm (CR) and heart rate variability (HRV) analysis. The complete proposed architecture is implemented in UMC 0.18 µm CMOS technology with 1.8 V supply. The functionality of each of the individual blocks are successfully validated using postextraction process corner simulations and through real ECG test signals taken from the PhysioNet database. The capacitive feedback amplifier, Σ∆ ADC, AGC and the AFT are fabricated, and the measurement results are discussed here. The analog classification scheme is successfully validated using embed NXP LPC1768 board, discrete peak detector prototype and FPGA software interfac

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