Development of a Biosensor for fast point-of-care Blood Analysis of Troponin.

We present the development of novel tetrapolar EIS biosensor for the detect of troponin. Troponin has considerable diagnostic power and provide invaluable prognostic information for risk stratification. of acute coronary syndromes. Clinical Relevance- A feasibility study was undertaken to assess the diagnostic performance of serial cardiac troponin measurements which is excellent as these structural proteins are unique to the heart and thus sensitive and specific of damage to the myocardium. clinical molecular diagnostics and home healthcare. Troponin's biosensors would provide point-of-care and rapid decision making for the early detection of CS. Clinically relevant window of cTnI testing, concentrations from 10pM to 0.1µM were achieved.

Compressive sensing in electrical impedance tomography for breathing monitoring.

OBJECTIVE: Electrical impedance tomography (EIT) is a functional imaging technique in which cross-sectional images of structures are reconstructed based on boundary trans-impedance measurements. Continuous functional thorax monitoring using EIT has been extensively researched. Increasing the number of electrodes, number of planes and frame rate may improve clinical decision making. Thus, a limiting factor in high temporal resolution, 3D and fast EIT is the handling of the volume of raw impedance data produced for transmission and its subsequent storage. Owing to the periodicity (i.e. sparsity in frequency domain) of breathing and other physiological variations that may be reflected in EIT boundary measurements, data dimensionality may be reduced efficiently at the time of sampling using compressed sensing techniques. This way, a fewer number of samples may be taken. APPROACH: Measurements using a 32-electrode, 48-frames-per-second EIT system from 30 neonates were post-processed to simulate random demodulation acquisition method on 2000 frames (each consisting of 544 measurements) for compression ratios (CRs) ranging from 2 to 100. Sparse reconstruction was performed by solving the basis pursuit problem using SPGL1 package. The global impedance data (i.e. sum of all 544 measurements in each frame) was used in the subsequent studies. The signal to noise ratio (SNR) for the entire frequency band (0 Hz-24 Hz) and three local frequency bands were analysed. A breath detection algorithm was applied to traces and the subsequent error-rates were calculated while considering the outcome of the algorithm applied to a down-sampled and linearly interpolated version of the traces as the baseline. MAIN RESULTS: SNR degradation was generally proportional with CR. The mean degradation for 0 Hz-8 Hz (of interest for the target physiological variations) was below ~15 dB for all CRs. The error-rates in the outcome of the breath detection algorithm in the case of decompressed traces were lower than those associated with the corresponding down-sampled traces for CR ⩾ 25, corresponding to sub-Nyquist rate for breathing frequency. For instance, the mean error-rate associated with CR = 50 was ~60% lower than that of the corresponding down-sampled traces. SIGNIFICANCE: To the best of our knowledge, no other study has evaluated the applicability of compressive sensing techniques on raw boundary impedance data in EIT. While further research should be directed at optimising the acquisition and decompression techniques for this application, this contribution serves as the baseline for future efforts.

Optimized breath detection algorithm in electrical impedance tomography.

OBJECTIVE: This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern. APPROACH: Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project. MAIN RESULTS: Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device. SIGNIFICANCE: Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.

Torso shape detection to improve lung monitoring.

OBJECTIVE: Newborns with lung immaturity often require continuous monitoring and treatment of their lung ventilation in intensive care units, especially if born preterm. Recent studies indicate that electrical impedance tomography (EIT) is feasible in newborn infants and children, and can quantitatively identify changes in regional lung aeration and ventilation following alterations to respiratory conditions. Information on the patient-specific shape of the torso and its role in minimizing the artefacts in the reconstructed images can improve the accuracy of the clinical parameters obtained from EIT. Currently, only idealized models or those segmented from CT scans are usually adopted. APPROACH: This study presents and compares two methodologies that can detect the patient-specific torso shape by means of wearable devices based on (1) previously reported bend sensor technology, and (2) a novel approach based on the use of accelerometers. MAIN RESULTS: The reconstruction of different phantoms, taking into account anatomical asymmetries and different sizes, are produced for comparison. SIGNIFICANCE: As a result, the accelerometers are more versatile than bend sensors, which cannot be used on bigger cross-sections. The computational study estimates the optimal number of accelerometers required in order to generate an image reconstruction comparable to the use of a CT scan as the forward model. Furthermore, since the patient position is crucial to monitoring lung ventilation, the orientation of the phantoms is automatically detected by the accelerometer-based method.

Clinical performance of a novel textile interface for neonatal chest electrical impedance tomography.

OBJECTIVE: Critically ill neonates and infants might particularly benefit from continuous chest electrical impedance tomography (EIT) monitoring at the bedside. In this study a textile 32-electrode interface for neonatal EIT examination has been developed and tested to validate its clinical performance. The objectives were to assess ease of use in a clinical setting, stability of contact impedance at the electrode-skin interface and possible adverse effects. APPROACH: Thirty preterm infants (gestational age: 30.3 ± 3.9 week (mean ± SD), postnatal age: 13.8 ± 28.2 d, body weight at inclusion: 1727 ± 869 g) were included in this multicentre study. The electrode-skin contact impedances were measured continuously for up to 3 d and analysed during the initial 20-min phase after fastening the belt and during a 10 h measurement interval without any clinical interventions. The skin condition was assessed by attending clinicians. MAIN RESULTS: Our findings imply that the textile electrode interface is suitable for long-term neonatal chest EIT imaging. It does not cause any distress for the preterm infants or discomfort. Stable contact impedance of about 300 Ohm was observed immediately after fastening the electrode belt and during the subsequent 20 min period. A slight increase in contact impedance was observed over time. Tidal variation of contact impedance was less than 5 Ohm. SIGNIFICANCE: The availability of a textile 32-electrode belt for neonatal EIT imaging with simple, fast, accurate and reproducible placement on the chest strengthens the potential of EIT to be used for regional lung monitoring in critically ill neonates and infants.

A method for removing artefacts from continuous EEG recordings during functional electrical impedance tomography for the detection of epileptic seizures.

Electrical impedance tomography (EIT) is a portable, non-invasive medical imaging method, which could be employed to image the seizure onset in subjects undergoing assessment prior to epilepsy surgery. Each image is obtained from impedance measurements conducted with imperceptible current at tens of kHz. For concurrent imaging with video electroencephalogram (EEG), the EIT introduces a substantial artefact into the EEG due to current switching at frequencies in the EEG band. We present here a method for its removal, so that EIT and the EEG could be acquired simultaneously. A low-pass analogue filter for EEG channels (-6 dB at 48 Hz) and a high-pass filter (-3 dB at 72 Hz) for EIT channels reduced the artefact from 2-3 mV to 50-300 microV, but still left a periodic artefact at about 3 Hz. This was reduced to less than 10 microV with a software filter, which subtracted an artefact template from the EEG raw traces. The EEG was made clinically acceptable at four times its acquisition speed. This method could enable EIT to become a technique for imaging on telemetry units alongside EEG, without interfering with routine EEG reporting.

The influence of reactivity of the electrode-brain interface on the crossing electric current in therapeutic deep brain stimulation.

The use of deep brain stimulation (DBS) as an effective clinical therapy for a number of neurological disorders has been greatly hindered by the lack of understanding of the mechanisms which underlie the observed clinical improvement in patients. This problem is confounded by the difficulty of investigating the neuronal effects of DBS in situ, and the impossibility of measuring the induced current in vivo. In our recent computational work using a quasi-static finite element (FEM) model we have quantitatively shown that the properties of the depth electrode-brain interface (EBI) have a significant effect on the electric field induced in the brain volume surrounding the DBS electrode. In the present work, we explore the influence of the reactivity of the EBI on the crossing electric current using the Fourier-FEM approach to allow the investigation of waveform attenuation in the time domain. Results showed that the EBI affected the waveform shaping differently at different post-implantation stages, and that this in turn had implications on induced current distribution across the EBI. Furthermore, we investigated whether hypothetical waveforms, which were shown to have potential usefulness for neural stimulation but are not yet applied clinically, would have any advantage over the currently used square pulse. In conclusion, the influence of reactivity of the EBI on the crossing stimulation current in therapeutic DBS is significant, and affects the predictive estimation of current distribution around the implanted DBS electrode in the human brain.

Development of a neonate lung reconstruction algorithm using a wavelet AMG and estimated boundary form.

Objective, non-invasive measures of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants, are urgently required to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologists. At present, no system for continuous monitoring of neonate lung function to reduce the risk of chronic lung disease in infancy in intensive care units exists. We are in the process of developing a new integrated electrical impedance tomography (EIT) system based on wearable technology to integrate measures of the boundary diameter from the boundary form for neonates into the reconstruction algorithm. In principle, this approach could provide a reduction of image artefacts in the reconstructed image associated with incorrect boundary form assumptions. In this paper, we investigate the required accuracy of the boundary form that would be suitable to minimize artefacts in the reconstruction for neonate lung function. The number of data points needed to create the required boundary form is automatically determined using genetic algorithms. The approach presented in this paper is to assist quality of the reconstruction using different approximations to the ideal boundary form. We also investigate the use of a wavelet algebraic multi-grid (WAMG) preconditioner to reduce the reconstruction computation requirements. Results are presented that demonstrate a full 3D model is required to minimize artefact in the reconstructed image and the implementation of a WAMG for EIT.

Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation.

A depth electrode-brain interface (EBI) is formed once electrodes are implanted into the human brain. We investigated the impact of the EBI on the crossing electric currents during both deep brain recording (DBR) and deep brain stimulation (DBS) over the acute, chronic and transitional stages post-implantation, in order to investigate and quantify the effect which changes at the EBI have on both DBR and DBS. We combined two complementary methods: (1) physiological recording of local field potentials via the implanted electrode in patients; and (2) computational simulations of an EBI model. Our depth recordings revealed that the physiological modulation of the EBI in the acute stage via brain pulsation selectively affected the crossing neural signals in a frequency-dependent manner, as the amplitude of the electrode potential was inversely correlated with that of the tremor-related oscillation, but not the beta oscillation. Computational simulations of DBS during the transitional period showed that the shielding effect of partial giant cell growth on the injected current could shape the field in an unpredictable manner. These results quantitatively demonstrated that physiological modulation of the EBI significantly affected the crossing currents in both DBR and DBS. Studying the microenvironment of the EBI may be a key step in investigating the mechanisms of DBR and DBS, as well as brain-computer interactions in general.

Use of statistical parametric mapping (SPM) to enhance electrical impedance tomography (EIT) image sets.

Use of statistical parametric mapping (SPM), which is widely used in analysis of neuroimaging studies with fMRI and PET, has the potential to improve quality of EIT images for clinical use. Minimal modification to SPM is needed, but statistical analysis based on height, not extent thresholds, should be employed, due to the 20-80% variation of the point spread function, across EIT images. SPM was assessed in EIT images reconstructed with a linear time difference algorithm utilizing an anatomically realistic finite element model of the human head. Images of the average of data sets were compared with those produced using SPM over 10-40 individual image data sets without averaging. For a point disturbance, a sponge 15% of the diameter of an anatomically realistic saline-filled tank including a skull, with a contrast of 15%, and for visual evoked response data in 14 normal human volunteers, images produced with SPM were less noisy than the average images. For the human data, no consistent physiologically realistic changes were seen with either SPM or direct reconstruction; however, only a small data set was available, limiting the power of the SPM analysis. SPM may be used on EIT images and has the potential to extract improved images from clinical data series with a low signal-to-noise ratio.

Improving the finite element forward model of the human head by warping using elastic deformation.

As the use of realistic geometry in the forward model of electrical impedance tomography (EIT) of brain function appears to improve image reconstruction, the generation of patient-specific finite element meshes has been the subject of much recent work. This paper presents a more rapid method of generating more geometrically accurate finite element meshes of the human head by warping existing meshes such that the surface boundary beneath the electrodes closely matches that of the subject with minimal degradation to the quality of the mesh. Pre-existing meshes of spheres and adult head models incorporating key internal anatomical features are warped, using elastic deformation, to match a phantom latex tank incorporating a real skull. The algorithm is described and tests are carried out to optimize the key parameters to ensure minimal degradation of mesh quality and distortion of internal features. Results show that the algorithm operating with the optimum parameters produces meshes of sound quality and could represent an important step in the timely and productive creation of forward models in clinical applications.

Bioimpedance tomography (electrical impedance tomography).

Electrical impedance tomography (EIT) is a relatively new imaging method that has evolved over the past 20 years. It has the potential to be of great value in clinical diagnosis; however, EIT is a technically difficult problem to solve in terms of developing hardware for data capture and the algorithms to reconstruct the images. This review looks at the development of EIT and how it has evolved. It focuses on its clinical applications, examining hardware for the collection of data and reconstruction algorithms to generate images. Finally, this review looks at future developments that are evolving from EIT. These new variations use mixed modalities that may produce interesting new clinical imaging tools.

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.

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.

Factors limiting the application of electrical impedance tomography for identification of regional conductivity changes using scalp electrodes during epileptic seizures in humans.

Electrical impedance tomography (EIT) has the potential to produce images during epileptic seizures. This might improve the accuracy of the localization of epileptic foci in patients undergoing presurgical assessment for curative neurosurgery. It has already been shown that impedance increases by up to 22% during induced epileptic seizures in animal models, using cortical or implanted electrodes in controlled experiments. The purpose of this study was to determine if reproducible raw impedance changes and EIT images could be collected during epileptic seizures in patients who were undergoing observation with video-electroencephalography (EEG) telemetry as part of evaluation prior to neurosurgery to resect the region of brain causing the epilepsy. A secondary purpose was to develop an objective method for processing and evaluating data, as seizures arose at unpredictable times from a noisy baseline. Four-terminal impedance measurements from 258 combinations were collected continuously using 32 EEG scalp electrodes in 22 seizure episodes from 7 patients during their presurgical assessment together with the standard EEG recordings. A reliable method for defining the pre-seizure baseline and recording impedance data and EIT images was developed, in which EIT and EEG could be acquired simultaneously after filtering of EIT artefact from the EEG signal. Fluctuations of several per cent over minutes were observed in the baseline between seizures. During seizures, boundary voltage changes diverged with a standard deviation of 1-54% from the baseline. No reproducible changes with the expected time course of some tens of seconds and magnitude of about 0.1% could be reliably measured. This demonstrates that it is feasible to acquire EIT images in parallel with standard EEG during presurgical assessment but, unfortunately, expected EIT changes on the scalp of about 0.1% are swamped by much larger movement and systematic artefact. Nevertheless, EIT has the unique potential to provide invaluable neuroimaging data for this purpose and may still become possible with improvements in electrode design and instrumentation.

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

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.

Applications of GRID in clinical neurophysiology and Electrical Impedance Tomography of brain function.

The computational requirements in Neurophysiology are increasing with the development of new analysis methods. The resources the GRID has to offer are ideally suited for this complex processing. A practical implementation of the GRID, Condor, has been assessed using a local cluster of 920 PCs. The reduction in processing time was assessed in spike recognition of the Electroencephalogram (EEG) in epilepsy using wavelets and the computationally demanding task of non-linear image reconstruction with Electrical Impedance Tomography (EIT). Processing times were decreased by 25 and 40 times respectively. This represents a substantial improvement in processing time, but is still sub optimal due to factors such as shared access to resources and lack of checkpoints so that interrupted jobs had to be restarted. Future work will be to use these methods in non-linear EIT image reconstruction of brain function and methods for automated EEG analysis, if possible with further optimized GRID middleware.

Using the GRID to improve the computation speed of electrical impedance tomography (EIT) reconstruction algorithms.

In our group at University College London, we have been developing electrical impedance tomography (EIT) of brain function. We have attempted to improve image quality by the use of realistic anatomical meshes and, more recently, non-linear reconstruction methods. Reconstruction with linear methods, with pre-processing, may take up to a few minutes per image for even detailed meshes. However, iterative non-linear reconstruction methods require much more computational resources, and reconstruction with detailed meshes was taking far too long for clinical use. We present a solution to this timing bottleneck, using the resources of the GRID, the development of coordinated computing resources over the internet that are not subject to centralized control using standard, open, general-purpose protocols and are transparent to the user. Optimization was performed by splitting reconstruction of image series into individual jobs of one image each; no parallelization was attempted. Using the GRID middleware 'Condor' and a cluster of 920 nodes, reconstruction of EIT images of the human head with a non-linear algorithm was speeded up by 25-40 times compared to serial processing of each image. This distributed method is of direct practical value in applications such as EIT of epileptic seizures where hundreds of images are collected over the few minutes of a seizure and will be of value to clinical data collection with similar requirements. In the future, the same resources could be employed for the more ambitious task of parallelized code.

Generating accurate finite element meshes for the forward model of the human head in EIT.

The use of realistic anatomy in the model used for image reconstruction in EIT of brain function appears to confer significant improvements compared to geometric shapes such as a sphere. Accurate model geometry may be achieved by numerical models based on magnetic resonance images (MRIs) of the head, and this group has elected to use finite element meshing (FEM) as it enables detailed internal anatomy to be modelled and has the capability to incorporate information about tissue anisotropy. In this paper a method for generating accurate FEMs of the human head is presented where MRI images are manually segmented using custom adaptation of industry standard commercial design software packages. This is illustrated with example surface models and meshes from adult epilepsy patients, a neonatal baby and a phantom latex tank incorporating a real skull. Mesh quality is assessed in terms of element stretch and hence distortion.

The use of a European telemedicine system to examine the effects of pollutants and allergens on asthmatic respiratory health.

The experience of using a telemedicine feasibility study to integrate respiratory health response and environmental stimuli information is presented. The effects of ambient air quality, pollen and local environment conditions on asthmatic patients' lung function were investigated through the use of a novel European health telematic system. The Medical Diagnosis, Communication and Analysis Throughout Europe (MEDICATE) project developed and tested the feasibility of using a telemedicine system for chronic asthmatics in London, UK, and Barcelona, Spain. The key to this was the determination of the real time health (lung function) response to the ambient environment and allergens. Air quality, pollen and environmental lifestyle information were related to respiratory measurements for recruited asthmatic patients in the study through the design of a dedicated environmental management system (EMS) database. In total, 28 patients completed the study trial, subject to ambulatory monitoring of spirometric lung function (PEF, FEV(1) and FVC) up to four times a day over a 2-week period recorded during the year 2000. Alongside this, ambient air quality and pollen counts were used to represent local exposure to potential environmental stimuli. Personal questionnaire interviewing collected additional data about patient lifestyles, social-economic conditions and quality of life perceptions. The methods and indicative results of integrating environmental and health data in this respect are examined. Assessment tools such as GIS and object-orientated databases were designed to locate and compile environmental information about the patients' locations and lifestyles in the study areas (London and Barcelona). Socioeconomic and lifestyle factors, such as exposure to smoking, pets, personal journey lengths and modes, income, household occupancy and domestic fuel use, were found to have limited detectable effects on the patients' basic lung function levels. Patients' gender, age and predicted PEFR were significantly associated with the 2-week mean and minimum respiratory measurements. Lung function data were compared with air quality and pollen indicators to examine relationships on a daily or lagged-day basis controlling for confounding factors. The paper discusses the new methodology and the practicalities of using the telemedical system as a tool for assessing the impacts of environmental stimuli on respiratory health.