Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates.

Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan-Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal-Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn-Sidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups.

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities.

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

Impact of Local Electrodes on Brain Stroke Type Differentiation using Electrical Impedance Tomography.

Electrical impedance tomography (EIT) of the head has the potential to provide rapid characterization of brain stroke. This study builds on previous work by implementing a more anatomically complex head model, contrasting results of bleed and clot simulations, and by establishing the electrodes which dominate in voltage difference measurements. This work provides the basis for machine learning with clusters of small numbers of electrodes as unique features for stroke-type detection and differentiation.Clinical Relevance- This application of EIT can aid in early detection, classification, and localization of brain stroke, allowing for faster treatment.

Dielectric Properties of Ovine Heart at Microwave Frequencies.

Accurate knowledge of the dielectric properties of biological tissues is important in dosimetry studies and for medical diagnostic, monitoring and therapeutic technologies. In particular, the dielectric properties of the heart are used in numerical simulations of radiofrequency and microwave heart ablation. In one recent study, it was demonstrated that the dielectric properties of different components of the heart can vary considerably, contrary to previous literature that treated the heart as a homogeneous organ with measurements that ignored the anatomical location. Therefore, in this study, we record and report the dielectric properties of the heart as a heterogeneous organ. We measured the dielectric properties at different locations inside and outside of the heart over the 500 MHz to 20 GHz frequency range. Different parts of the heart were identified based on the anatomy of the heart and their function; they include the epicardium, endocardium, myocardium, exterior and interior surfaces of atrial appendage, and the luminal surface of the great vessels. The measured dielectric properties for each part of the heart are reported at both a single frequency (2.4 GHz), which is of interest in microwave medical applications, and as parameters of a broadband Debye model. The results show that in terms of dielectric properties, different parts of the heart should not be considered the same, with more than 25% difference in dielectric properties between some parts. The specific Debye models and single frequency dielectric properties from this study can be used to develop more detailed models of the heart to be used in electromagnetic modeling.

Developing a morphomics framework to optimize implant site-specific design parameters for islet macroencapsulation devices.

Delivering a clinically impactful cell number is a major design challenge for cell macroencapsulation devices for Type 1 diabetes. It is important to understand the transplant site anatomy to design a device that is practical and that can achieve a sufficient cell dose. We identify the posterior rectus sheath plane as a potential implant site as it is easily accessible, can facilitate longitudinal monitoring of transplants, and can provide nutritive support for cell survival. We have investigated this space using morphomics across a representative patient cohort (642 participants) and have analysed the data in terms of gender, age and BMI. We used a shape optimization process to maximize the volume and identified that elliptical devices achieve a clinically impactful cell dose while meeting device manufacture and delivery requirements. This morphomics framework has the potential to significantly influence the design of future macroencapsulation devices to better suit the needs of patients.

Bi-Frequency Symmetry Difference EIT-Feasibility and Limitations of Application to Stroke Diagnosis.

OBJECTIVE: Bi-Frequency Symmetry Difference (BFSD)-EIT can detect, localize and identify unilateral perturbations in symmetric scenes. Here, we test the viability and robustness of BFSD-EIT in stroke diagnosis. METHODS: A realistic 4-layer Finite Element Method (FEM) head model with and without bleed and clot lesions is developed. Performance is assessed with test parameters including: measurement noise, electrode placement errors, contact impedance errors, deviations in assumed tissue conductivity, deviations in assumed anatomy, and a frequency-dependent background. A final test is performed using ischemic patient data. Results are assessed using images and quantitative metrics. RESULTS: BFSD-EIT may be feasible for stroke diagnosis if a signal-to-noise ratio (SNR) of ≥60 dB is achievable. Sensitivity to errors in electrode positioning is seen with a tolerance of only ±5 mm, but a tolerance of up to ±30 mm is possible if symmetry is maintained between symmetrically opposite partner electrodes. The technique is robust to errors in contact impedance and assumed tissue conductivity up to at least ±50%. Asymmetric internal anatomy affects performance but may be tolerable for tissues with frequency-dependent conductivity. Errors in assumed external geometry marginally affect performance. A frequency-dependent background does not affect performance with carefully chosen frequency points or use of multiple frequency points across a band. The Global Left-Hand Side (LHS) & Right-Hand Side (RHS) Mean Intensity metric is particularly robust to errors. CONCLUSION: BFSD-EIT is a promising technique for stroke diagnosis, provided parameters are within the tolerated ranges. SIGNIFICANCE: BFSD-EIT may prove an important step forward in imaging of static scenes such as stroke.

Effect of Glycosaminoglycan Replacement on Markers of Interstitial Cystitis In Vitro.

Aims: To examine the effect of three commercial intravesical formulations of glycosaminoglycan on in vitro inflammatory models of IC/BPS to better understand there effect on specific markers of disease. Methods: Human urothelial cells (HTB-4) were cultured under four conditions in the presence or absence of commercial GAG formulations. Cells were cultured under a basal condition or pre-treated with protamine sulfate (100 ng/ml) (damages the endogenous glycosaminoglycan layer), hydrogen peroxide (1%) (a metabolic stressor) or TNFα (10 ng/ml) (creating an inflammatory environment). Each of these four culture conditions was then treated with one of three GAG formulations, CystistatⓇ, iAluRilⓇ and HyacystⓇ. Assays were then performed to examine the effect of the exogenous GAGs on cell viability, cell migration, sGAG production, cytokine and gene expression. Results: All GAG formulations were well tolerated by the HTB-4 cells and supported cell growth and migration. iAluRilⓇ was most effective at stimulating endogenous sGAG production under all conditions, increasing sGAGs by up to 15-fold. All GAG formulations significantly reduced the production of the pro-inflammatory cytokine IL-8 under basal conditions, while no GAG treatment suppressed cytokine production under any other condition. Only CystistatⓇ had a significant effect on HA receptor expression, significantly increasing ICAM-1 expression at 3 h that returned to basal levels at 24 h. No GAG treatment significantly changed the expression of GAG synthesis enzymes (CSGALNACT1, CSGALNACT2) or markers of tissue remodeling (MMP2, TIMP1) and pain (COX-1/PTGS-1, NGF). Conclusions: The data presented in this study reveal that commercial intravesical formulation support cell viability and migration. In addition, the commercial GAG formulations have a mild anti-inflammatory effect in the in vitro model of interstitial cystitis/bladder pain syndrome.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis.

OBJECTIVE: Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning. METHODS: Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values. MAIN RESULTS: An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data. CONCLUSION: Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes. SIGNIFICANCE: The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

Dielectric properties of bones for the monitoring of osteoporosis.

Osteoporosis is one of the most common diseases that leads to bone fractures. Dual-energy X-ray absorptiometry is currently employed to measure the bone mineral density and to diagnose osteoporosis. Alternatively, the dielectric properties of bones are found to be influenced by bone mineral density; hence, dielectric properties of bones may potentially be used to diagnose osteoporosis. Microwave tomographic imaging is currently in development to potentially measure in vivo dielectric properties of bone. Therefore, the foci of this work are to summarize all available dielectric data of bone in the microwave frequency range and to analyze the confounders that may have resulted in variations in reported data. This study also compares the relationship between the dielectric properties and bone quality reported across different studies. The review suggests that variations exist in the dielectric properties of bone and the relationship between bone volume fraction and dielectric properties is in agreement across all studies. Conversely, the evidence of a relationship between bone mineral density and dielectric properties is inconsistent across the studies. This summary of dielectric data of bone along with a comparison of the relationship between the dielectric properties and bone quality will accelerate the development of microwave tomographic imaging devices for the monitoring of osteoporosis. Graphical abstract ᅟ.

Bi-frequency symmetry difference electrical impedance tomography-a novel technique for perturbation detection in static scenes.

OBJECTIVE: A novel method for the imaging of static scenes using electrical impedance tomography (EIT) is reported with implementation and validation using numerical and phantom models. The technique is applicable to regions featuring symmetry in the normal case, asymmetry in the presence of a perturbation, and where there is a known, frequency-dependent change in the electrical conductivity of the materials in the region. APPROACH: The stroke diagnostic problem is used as a motivating sample application. The head is largely symmetrical across the sagittal plane. A haemorrhagic or ischaemic lesion located away from the sagittal plane will alter this natural symmetry, resulting in a symmetrical imbalance that can be detected using EIT. Specifically, application of EIT stimulation and measurement protocols at two distinct frequencies detects deviations in symmetry if an asymmetrically positioned lesion is present, with subsequent identification and localisation of the perturbation based on known frequency-dependent conductivity changes. Anatomically accurate computational models are used to demonstrate the feasibility of the proposed technique using different types, sizes, and locations of lesions with frequency-dependent (or independent) conductivity. Further, a realistic experimental head phantom is used to validate the technique using frequency-dependent perturbations emulating the key numerical simulations. MAIN RESULTS: Lesion presence, type, and location are detectable using this novel technique. Results are presented in the form of images and corresponding robust quantitative metrics. Better detection is achieved for larger lesions, those further from the sagittal plane, and when measurements have a higher signal-to-noise ratio. SIGNIFICANCE: Bi-frequency symmetry difference EIT is an exciting new modality of EIT with the ability to detect deviations in the symmetry of a region that occur due to the presence of a lesion. Notably, this modality does not require a time change in the region and thus may be used in static scenarios such as stroke detection.

Gamma Band Light Stimulation in Human Case Studies: Groundwork for Potential Alzheimer's Disease Treatment.

BACKGROUND: It is known that proteins associated with Alzheimer's disease (AD) pathogenesis are significantly reduced by 40 Hz entrainment in mice. If this were to translate to humans, verifying that such a light stimulus can induce a 40 Hz entrainment response in humans and harnessing insights from these case studies could be one step in the development of a multisensory device to prevent and treat AD. OBJECTIVE: Verify the inducement of a 40 Hz response in the human brain by a 40 Hz light stimulus and obtain insights that could potentially aid in the development of a multisensory device for the prevention and treatment of AD. METHODS: Electroencephalographic brain activity was recorded simultaneously with application of stimulus at different frequencies and intensities. Power spectral densities were analyzed. RESULTS: Entrainment to visual stimuli occurred with the largest response at 40 Hz. The high intensity 40 Hz stimulus caused widespread entrainment. The number of electrodes demonstrating entrainment increased with increasing light intensity. Largest amplitudes for the high intensity 40 Hz stimulus were consistently found at the primary visual cortex. There was a harmonic effect at double the frequency for the 40 Hz stimulus. An eyes-open protocol caused more entrainment than an eyes-closed protocol. CONCLUSION: It was possible to induce widespread entrainment using a 40 Hz light stimulus in this sample cohort. Insights gleaned from these case studies could potentially aid in the development of a multisensory medical device to prevent and treat AD.

Brain haemorrhage detection using a SVM classifier with electrical impedance tomography measurement frames.

Brain haemorrhages often require urgent treatment with a consequent need for quick and accurate diagnosis. Therefore, in this study, we investigate Support Vector Machine (SVM) classifiers for detecting brain haemorrhages using Electrical Impedance Tomography (EIT) measurement frames. A 2-layer model of the head, along with a series of haemorrhages, is designed as both numerical models and physical phantoms. EIT measurement frames, taken from an electrode array placed on the head surface, are used to train and test linear SVM classifiers. Various scenarios are implemented on both platforms to examine the impact of variables such as noise level, lesion location, lesion size, variation in electrode positioning, and variation in anatomy, on the classifier performance. The classifier performed well in numerical models (sensitivity and specificity of 90%+) with signal-to-noise ratios of 60 dB+, was independent of lesion location, and could detect lesions reliably down to the tested minimum volume of 5 ml. Slight variations in electrode layout did not affect performance. Performance was affected by variations in anatomy however, emphasising the need for large training sets covering different anatomies. The phantom models proved more challenging, with maximal sensitivity and specificity of 75% when used with the linear SVM. Finally, the performance of two more complex classifiers is briefly examined and compared to the linear SVM classifier. These results demonstrate that a radial basis function (RBF) SVM classifier and a neural network classifier can improve detection accuracy. Classifiers applied to EIT measurement frames is a novel approach for lesion detection and may offer an effective diagnostic tool clinically. A challenge is to translate the strong results from numerical models into real world phantoms and ultimately human patients, as well as the selection and development of optimal classifiers for this application.

Symmetry difference electrical impedance tomography-a novel modality for anomaly detection.

OBJECTIVE: The theoretical basis, experimental implementation, and proof of concept of a novel electrical impedance tomography (EIT) imaging technique, called symmetry difference EIT, is described. This technique is applicable in situations where there is inherent symmetry in the region being imaged. METHODS: The sample scenario of the human head is used to describe the technique. The head is largely symmetrical across the sagittal plane. A unilateral lesion such as a haemorrhage or region of ischaemia distorts that symmetry. This distortion may be visualised using EIT. Measurement sets from a ring of electrodes placed on the boundary in both clockwise and counter-clockwise orientations are compared to detect the anomaly. Computer simulations featuring a hemispherical model of the head and brain are used initially to demonstrate the theory. Then, a more complex numerical model with anatomically accurate finite element models (FEMs) is used to expand on the concept with a more realistic scenario. Finally, tank experiments are performed with phantom lesions to validate the technique in the real world. RESULTS: Deviations from normal symmetry, due to the presence of lesions, are detectable using this new modality. The ease of detection improves with larger lesions and those far from the plane of symmetry. Quantitative metrics, as well as an image, help to robustly detect and identify both the presence of an abnormality and the cause (haemorrhagic or ischaemic lesion in the scenarios tested) or indeed indicate where no detection is possible. CONCLUSION: Symmetry difference EIT is a valuable new modality that is applicable to cases where the 'normal' features symmetry across a plane. Significantly, a change in the region of interest is not required and hence this technique may be suited to static or quasi-static cases where time difference EIT cannot be used.

Gamma Band Neural Stimulation in Humans and the Promise of a New Modality to Prevent and Treat Alzheimer's Disease.

Existing treatments for Alzheimer's disease (AD) have questionable efficacy with a need for research into new and more effective therapies to both treat and possibly prevent the condition. This review examines a novel therapeutic modality that shows promise for treating AD based on modulating neuronal activity in the gamma frequency band through external brain stimulation. The gamma frequency band is roughly defined as being between 30 Hz-100 Hz, with the 40 Hz point being of particular significance. The epidemiology, diagnostics, existing pathological models, and related current treatment targets are initially briefly reviewed. Next, the concept of external simulation triggering brain activity in the gamma band with potential demonstration of benefit in AD is introduced with reference to a recent important study using a mouse model of the disease. The review then presents a selection of relevant studies that describe the neurophysiology involved in brain stimulation by external sources, followed by studies involving application of the modality to clinical scenarios. A table summarizing the results of clinical studies applied to AD patients is also reported and may aid future development of the modality. The use of a therapy based on modulation of gamma neuronal activity represents a novel non-invasive, non-pharmacological approach to AD. Although use in clinical scenarios is still a relatively recent area of research, the technique shows good signs of efficacy and may represent an important option for treating AD in the future.