There has been considerable interest in electrical impedance tomography (EIT) to provide low-cost, radiation-free, real-time and wearable means for physiological status monitoring. To be competitive with other well-established imaging modalities, it is important to understand the requirements of the specific application and determine a suitable system design. This paper presents an overview of EIT circuits and systems including architectures, current drivers, analog front-end and demodulation circuits, with emphasis on integrated circuit implementations. Commonly used circuit topologies are detailed, and tradeoffs are discussed to aid in choosing an appropriate design based on the application and system priorities. The paper also describes a number of integrated EIT systems for biomedical applications, as well as discussing current challenges and possible future directions.

This paper presents a human-machine interface that establishes a link between the user and a hand prosthesis. It successfully uses electrical impedance tomography, a conventional bio-impedance imaging technique, using an array of electrodes contained in a wristband on the user's forearm. Using a high-performance analog front-end application specific integrated circuit (ASIC) the user's forearm inner bio-impedance redistribution is accurately assessed. These bio-signatures are strongly related to hand motions and using artificial neural networks, they can be learned so as to recognize the user's intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation with a gesture switching enabled sub-grouping method. Experiments with five subjects show that the system can achieve 98.5% accuracy with a grouping of three gestures and an accuracy of 94.4% with two sets of five gestures. The ASIC comprises a current driver with common-mode reduction capability and a current feedback instrumentation amplifier. The ASIC operates from $\pm$ 1.65 V power supplies, occupies an area of 0.07 mm2, and has a minimum bio-impedance sensitivity of 12.7 mΩp-p.

This paper presents a passive phase-shift keying (PPSK) modulator for uplink data transmission for biomedical implants with simultaneous power and data transmission over a single 13.56 MHz inductive link. The PPSK modulator provides a data rate up to 1.35 Mbps with a modulation index between 3% and 38% for a variation of the coupling coefficient between 0.05 and 0.26. This modulation scheme is particularly suited for biomedical implants that have high power demand and low coupling coefficients. The PPSK modulator operates in conjunction with on-off-keying downlink communication. The same inductive link is used to provide up to 100 mW of power to a multi-channel stimulator. The majority of the system on the implant side was implemented as an application specific integrated circuit (ASIC), fabricated in 0.6-μm high voltage CMOS technology. The theory of PPSK modulation, simulated and measured performance evaluation, and comparison with other state-of-the-art impedance modulation techniques is presented. The measured bit error rate around critical coupling at 1.35 Mbps is below 6 × 10 ^-8 .

A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented. Each electrode has an application specific integrated circuit (ASIC) mounted on a flexible printed circuit board, which is then wrapped inside a disposable fabric cover containing silver-coated electrodes to form the wearable belt. It is connected to a central hub that operates all the 32 ASICs. Each ASIC comprises a high-performance current driver capable of up to 6 mA _p-p output, a voltage buffer for EIT and heart rate signal recording as well as contact impedance monitoring, and a sensor buffer that provides multi-parameter sensing. The ASIC was designed in a CMOS 0.35- $\mu \text{m}$ high-voltage process technology. It operates from ±9 V power supplies and occupies a total die area of 3.9 mm ² . The EIT system has a bandwidth of 500 kHz and employs two parallel data acquisition channels to achieve a frame rate of 107 frames/s, the fastest wearable EIT system reported to date. Measured results show that the system has a measurement accuracy of 98.88% and a minimum EIT detectability of 0.86 $\Omega $ /frame. Its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated.

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has sixteen active electrodes. Each has an application specific integrated circuit (ASIC) connected to an electrode. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier (IA), a digital controller and multiplexors. The wearable EIT belt features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It provides intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio. The ASIC was designed in a CMOS 0.35-μm high-voltage technology. The high specification EIT belt has an image frame rate of 122 fps, a wide operating bandwidth of 1 MHz and multi-frequency operation. It measures impedance with 98% accuracy and has less than 0.5 $\Omega$ and 1° variation across all possible channels. The image results confirmed the advantage of the new active electrode architecture and the benefit of wideband, multi-frequency EIT operation. The wearable EIT belt can also detect patient position and torso shape information using a MEMS sensor interfaced to each ASIC. The system successfully captured high quality lung respiration EIT images, breathing cycle and heart rate.

This brief presents a 16-electrode electrical impedance tomography (EIT) system for hand gesture recognition. The hardware of the system is based on integrated circuits including a 12-bit high spectral purity current-steering DAC implemented in 0.18 μm CMOS technology, a current driver and an instrumentation amplifier in 0.35 μm CMOS technology. Both 2D and 3D EIT electrode arrangements were tested for hand gesture recognition. It is shown that using machine learning algorithms, eight hand gestures can be distinguished from the measured bio-impedance data with an accuracy of 97.9% when the electrodes are placed on a single wristband, and an accuracy of 99.5% with the same number of electrodes distributed on two wristbands for 3D EIT measurement. In particular 3D EIT demonstrated significant superiority in its ability to discriminate between gestures with similar muscle contractions.