Alistair McEwan

Bio: Alistair McEwan is an academic researcher from University of Sydney. The author has contributed to research in topics: Electrical impedance tomography & Electrical impedance. The author has an hindex of 25, co-authored 192 publications receiving 2330 citations. Previous affiliations of Alistair McEwan include Royal Prince Alfred Hospital & University of Oxford.

Multi-frequency electrical impedance tomography (EIT) of the adult human head: initial findings in brain tumours, arteriovenous malformations and chronic stroke, development of an analysis method and calibration

Abstract: MFEIT (multi-frequency electrical impedance tomography) could distinguish between ischaemic and haemorrhagic stroke and permit the urgent use of thrombolytic drugs in patients with ischaemic stroke. The purpose of this study was to characterize the UCLH Mk 2 MFEIT system, designed for this purpose, with 32 electrodes and a multiplexed 2 kHz to 1.6 MHz single impedance measuring circuit. Data were collected in seven subjects with brain tumours, arteriovenous malformations or chronic stroke, as these resembled the changes in haemorrhagic or ischaemic stroke. Calibration studies indicated that the reliable bandwidth was only 16-64 kHz because of front-end components placed to permit simultaneous EEG recording. In raw in-phase component data, the SD of 16-64 kHz data for one electrode combination across subjects was 2.45 +/- 0.9%, compared to a largest predicted change of 0.35% estimated using the FEM of the head. Using newly developed methods of examining the most sensitive channels from the FEM, and nonlinear imaging constrained to the known site of the lesion, no reproducible changes between pathologies were observed. This study has identified a specification for accuracy in EITS in acute stroke, identified the size of variability in relation to this in human recordings, and presents new methods for analysis of data. Although no reproducible changes were identified, we hope this will provide a foundation for future studies in this demanding but potentially powerful novel application.

Design and calibration of a compact multi-frequency EIT system for acute stroke imaging

Abstract: A new, compact UCLH Mk 2.5 EIT system has been developed and calibrated for EIT imaging of the head. Improvements include increased input and output impedances, increased bandwidth and improved CMRR (80 dB) and linearity over frequencies and load (0.2% on a single channel, +/-0.7% on a saline tank over 20 Hz-256 kHz and 10-65 Omega). The accuracy of the system is sufficient to image severe acute stroke according to the specification from recent detailed anatomical modelling (Horesh et al 2005 3rd European Medical and Biological Engineering Conference EMBEC'05). A preliminary human study has validated the main specifications of the modelling, the range of trans-impedance from the head (8-70 Omega) using a 32 electrode, 258 combination protocol and contact impedances of 300 Omega to 2.7 kOmega over 20 Hz to 256 kHz.

A review of errors in multi-frequency EIT instrumentation

Abstract: Multi-frequency electrical impedance tomography (MFEIT) was proposed over 10 years ago as a potential spectroscopic impedance imaging method. At least seven systems have been developed for imaging the lung, heart, breast and brain, yet none has yet achieved clinical acceptance. While the absolute impedance varies considerably between different tissues, the changes in the spectrum due to physiological changes are expected to be quite small, especially when measured through a volume. This places substantial requirements on the MFEIT instrumentation to maintain a flat system frequency response over a broad frequency range (dc-MHz). In this work, the EIT measurement problem is described from a multi-frequency perspective. Solutions to the common problems are considered from recent MFEIT systems, and the debate over four-terminal or two-terminal (multiple source) architecture is revisited. An analysis of the sources of MFEIT errors identifies the major sources of error as stray capacitance and common-mode voltages which lead to a load dependence in the frequency response of MFEIT systems. A system that employs active electrodes appears to be the most able to cope with these errors (Li et al 1996). A distributed system with digitization at the electrode is suggested as a next step in MFEIT system development.

A review on electrical impedance tomography for pulmonary perfusion imaging

Abstract: Although electrical impedance tomography (EIT) for ventilation monitoring is on the verge of clinical trials, pulmonary perfusion imaging with EIT remains a challenge, especially in spontaneously breathing subjects. In anticipation of more research on this subject, we believe a thorough review is called for. In this paper, findings related to the physiological origins and electrical characteristics of this signal are summarized, highlighting properties that are particularly relevant to EIT. The perfusion impedance change signal is significantly smaller in amplitude compared with the changes due to ventilation. Therefore, the hardware used for this purpose must be more sensitive and more resilient to noise. In previous works, some signal- or image-processing methods have been required to separate these two signals. Three different techniques are reviewed in this paper, including the ECG-gating method, frequency-domain-filtering-based methods and a principal-component-analysis-based method. In addition, we review a number of experimental studies on both human and animal subjects that employed EIT for perfusion imaging, with promising results in the diagnosis of pulmonary embolism (PE) and pulmonary arterial hypertension as well as other potential applications. In our opinion, PE is most likely to become the main focus for perfusion EIT in the future, especially for heavily instrumented patients in the intensive care unit (ICU).

Multi-Frequency Electrical Impedance Tomography System With Automatic Self-Calibration for Long-Term Monitoring

Abstract: Electrical Impedance Tomography (EIT) is a safe medical imaging technology, requiring no ionizing or heating radiation, as opposed to most other imaging modalities. This has led to a clinical interest in its use for long-term monitoring, possibly at the bedside, for ventilation monitoring, bleeding detection, gastric emptying and epilepsy foci diagnosis. These long-term applications demand auto-calibration and high stability over long time periods. To address this need we have developed a new multi-frequency EIT system called the KHU Mark2.5 with automatic self-calibration and cooperation with other devices via a timing signal for synchronization with other medical instruments. The impedance measurement module (IMM) for flexible configuration as a key component includes an independent constant current source, an independent differential voltmeter, and a current source calibrator, which allows automatic self-calibration of the current source within each IMM. We installed a resistor phantom inside the KHU Mark2.5 EIT system for intra-channel and inter-channel calibrations of all voltmeters in multiple IMMs. We show the deterioration of performance of an EIT system over time and the improvement due to automatic self-calibration. The system is able to maintain SNR of 80 dB for frequencies up to 250 kHz and below 0.5% reciprocity error over continuous operation for 24 hours. Automatic calibration at least every 3 days is shown to maintain SNR above 75 dB and reciprocity error below 0.7% over 7 days at 1 kHz. A clear degradation in performance results with increasing time between automatic calibrations allowing the tailoring of calibration to suit the performance requirements of each application.

Neuromorphic Silicon Neuron Circuits

Abstract: Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

Measurement of intraocular distances by backscattering spectral interferometry

Abstract: The diffraction tomography theorem is adapted to one-dimensional length measurement. The resulting spectral interferometry technique is described and the first length measurements using this technique on a model eye and on a human eye in vivo are presented.

Art of electronics

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Nanomaterials for In Vivo Imaging

Abstract: In vivo imaging, which enables us to peer deeply within living subjects, is producing tremendous opportunities both for clinical diagnostics and as a research tool. Contrast material is often required to clearly visualize the functional architecture of physiological structures. Recent advances in nanomaterials are becoming pivotal to generate the high-resolution, high-contrast images needed for accurate, precision diagnostics. Nanomaterials are playing major roles in imaging by delivering large imaging payloads, yielding improved sensitivity, multiplexing capacity, and modularity of design. Indeed, for several imaging modalities, nanomaterials are now not simply ancillary contrast entities, but are instead the original and sole source of image signal that make possible the modality’s existence. We address the physicochemical makeup/design of nanomaterials through the lens of the physical properties that produce contrast signal for the cognate imaging modality—we stratify nanomaterials on the basis of thei...

Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

Abstract: Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.