Analog integrated circuits design for processing physiological signals

Design and Analysis of Digitally Tunable Transconductance Amplifier (DTTA) Using CNTFETs

Carbon nanotube-FETs (CNTFETs) have become a potential challenger because of their exceptional electrical properties and compatibility with conventional CMOS technology. The design and study of digitally tunable transconductance amplifiers (DTTAs) using CNTFETs are the main topics of this work. By utilizing the special characteristics of CNTFETs, the suggested DTTA design makes transconductance tunable, providing a versatile method of adjusting amplifier settings without requiring modifications to the hardware architecture. This study provides a complete description of the CNTFET modeling techniques utilized for realistic circuit simulations, along with a detailed analysis of the DTTA based on CNTFETs. The circuit is implemented using a 32 nm CNTFET model and verified results with HSPICE.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

A 0.8 V, 14.76 nVrms, Multiplexer-Based AFE for Wearable Devices Using 45 nm CMOS Techniques

Wearable medical devices (WMDs) that continuously monitor health conditions enable people to stay healthy in everyday situations. A wristband is a monitoring format that can measure bioelectric signals. The main part of a wearable device is its analog front end (AFE). Wearables have issues such as low reliability, high power consumption, and large size. A conventional AFE device uses more analog-to-digital converters, amplifiers, and filters for individual electrodes. Our proposed MUX-based AFE design requires fewer components than a conventional AFE device, reducing power consumption and area. It includes a single-ended differential feedback operational transconductance amplifier (OTA) and n-pass MUX-based AFE circuits which are related to the emergence of low power, low area, and low cost AFE-integrated chips that are required for wearable biomedical applications. The proposed 6T n-pass multiplexer measures a gain of -68 dB across a frequency range of 100 kHz with a 136.5 nW power consumption and a delay of 0.07 ns. The design layout area is approximately 9.8 µm2 and uses 45 nm complementary metal oxide semiconductor (CMOS) technology. Additionally, the proposed single-ended differential OTA has an obtained input referred noise of 0.014 µVrms, and a gain of -5.5 dB, while the design layout area is about 2 µm2 and was designed with the help of the Cadence Virtuoso layout design tool.

A Non-Newtonian liquid metal enabled enhanced electrography

Biopotential signals, like electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG), can help diagnose cardiological, musculoskeletal and neurological disorders. Dry silver/silver chloride (Ag/AgCl) electrodes are commonly used to obtain these signals. While a conductive hydrogel can be added to Ag/AgCl electrodes to improve the contact and adhesion between the electrode and the skin, dry electrodes are prone to movement. Considering that the conductive hydrogel dries over time, the use of these electrodes often creates an imbalanced skin-electrode impedance and a number of sensing issues in the front-end analogue circuit. This issue can be extended to several other electrode types that are commonly in use, in particular, for applications with a need for long-term wearable monitoring such as ambulatory epilepsy monitoring. Liquid metal alloys, such as eutectic gallium indium (EGaIn), can address key critical requirements around consistency and reliability but present challenges on low viscosity and the risk of leakage. To solve these problems, here, we demonstrate the use of a non-eutectic Ga-In alloy as a shear-thinning non-Newtonian fluid to offer superior performance to commercial hydrogel electrodes, dry electrodes, and conventional liquid metals for electrography measurements. This material has high viscosity when still and can flow like a liquid metal when sheared, preventing leakage while allowing the effective fabrication of electrodes. Moreover, the Ga-In alloy not only has good biocompatibility but also offers an outstanding skin-electrode interface, allowing for the long-term acquisition of high-quality biosignals. The presented Ga-In alloy is a superior alternative to conventional electrode materials for real-world electrography or bioimpedance measurement.

A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control

Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors.

A CMOS Low Pass Filter for SoC Lock-in-Based Measurement Devices

This paper presents a fully integrated G_m–C low pass filter (LPF) based on a current steering G_m reduction-tuning technique, specifically designed to operate as the output stage of a SoC lock-in amplifier. To validate this proposal, a first-order and a second-order single-ended topology were integrated into a 1.8 V to 0.18 µm CMOS (Complementary Metal-Oxide-Semiconductor) process, showing experimentally a tuneable cutoff frequency that spanned five orders of magnitude, from tens of mHz to kHz, with a constant current consumption (below 3 µA/pole), compact size (<0.0140 mm²/pole), and a dynamic range better than 70 dB. Compared to state-of-the-art solutions, the proposed approach exhibited very competitive performances while simultaneously fully satisfying the demanding requirements of on-chip portable measurement systems in terms of highly efficient area and power. This is of special relevance, taking into account the current trend towards multichannel instruments to process sensor arrays, as the total area and power consumption will be proportional to the number of channels.