Exposure to total 36-hr sleep deprivation reduces physiological and psychological thermal strain to whole-body uncompensable passive heat stress in young adult men.

Total sleep deprivation (TSD) is associated with endothelial dysfunction and a consequent decrease in vascular reactivity and increase in peripheral vascular resistance. These effectors compromise the body's ability to thermoregulate in hot and cold stress conditions. We investigated heat-unacclimated young adult men (26 ± 2 years) to determine whether 36 hr of TSD compared to an 8 or 4-hr sleep condition, would suppress the responses of the autonomic system (body rectal temperature [Tre ], heart rate [HR], root mean square of successive interbeat intervals, physiological strain, blood pressure [BP], circulating blood catecholamines, sweating rate and subjective sensations) to whole-body uncompensable passive heat stress in traditional Finnish sauna heat (Tair  = 80-90°C, rh = 30%). Sauna bathing that induced whole-body hyperthermia had a residual effect on reducing BP in the 8-hr and 4-hr sleep per night conditions according to BP measurements. By contrast, 36 hr of total wakefulness led to an increase in BP. These observed sleep deprivation-dependent differences in BP modifications were not accompanied by changes in the blood plasma epinephrine and norepinephrine concentrations. However, during sauna bathing, an increase in BP following 36 hr of TSD was accompanied by significant decreases in body Tre , HR and physiological strain, together with a diminished sweating rate, enhanced vagus-mediated autonomic control of HR variability, and improved thermal perception by the subjects. Our results suggest the impaired ability of the body to accumulate external heat in the body's core under uncompensable passive heat conditions following 36 hr of TSD, because of the TSD-attenuated autonomic system response to acute heat stress.

Main Uncertainties in the RF Ultrasound Scanning Simulation of the Standard Ultrasound Phantoms.

Ultrasound echoscopy technologies are continuously evolving towards new modalities including quantitative parameter imaging, elastography, 3D scanning, and others. The development and analysis of new methods and algorithms require an adequate digital simulation of radiofrequency (RF) signal transformations. The purpose of this paper is the quantitative evaluation of RF signal simulation uncertainties in resolution and contrast reproduction with the model of a phased array transducer. The method is based on three types of standard physical phantoms. Digital 3D models of those phantoms are composed of point scatterers representing the weak backscattering of the background material and stronger backscattering from inclusions. The simulation results of echoscopy with sector scanning transducer by Field II software are compared with the RF output of the Ultrasonix scanner after scanning standard phantoms with 2.5 MHz phased array. The quantitative comparison of axial, lateral, and elevation resolutions have shown uncertainties from 9 to 22% correspondingly. The echoscopy simulation with two densities of scatterers is compared with contrast phantom imaging on the backscattered RF signals and B-scan reconstructed image, showing that the main sources of uncertainties limiting the echoscopy RF signal simulation adequacy are an insufficient knowledge of the scanner and phantom's parameters. The attempt made for the quantitative evaluation of simulation uncertainties shows both problems and the potential of echoscopy simulation in imaging technology developments. The analysis presented could be interesting for researchers developing quantitative ultrasound imaging and elastography technologies looking for simulated raw RF signals comparable to those obtained from real ultrasonic scanning.

Investigation of Radiofrequency Ultrasound-Based Fibrotic Tissue Strain Imaging Method Employing Endogenous Motion.

OBJECTIVES: The paper presents the results of an initial clinical study, which were obtained using the strain elastography imaging method based on radio frequency ultrasound signal analysis. METHODS: The technique employs endogenous motion of the liver induced by beating heart and vascular pulsatility as an excitation source of tissue microdisplacement. The potential for fibrotic tissue characterization was demonstrated using a clinical data set of radio frequency ultrasound signals (23 healthy controls, 21 subjects with hepatitis, and 16 subjects with liver cirrhosis). Parametric maps, which represent the tissue strain, were derived from the gradient of the integrated spectral coefficient parameter, and correlations with the stage of liver disease were evaluated. Average endogenous strain derived from the gradient of the integrated spectral coefficient parameter and variability (standard deviation) of the strain were evaluated in the rectangular regions of interest (sizes, 1 × 1 and 2 × 2 cm) defined by the observer. The assessment of strain was performed in different frequency subbands of endogenous motion (0-10 Hz and 10-20 Hz). RESULTS: The best distinction between the groups was observed for the average strain derived from the gradient of the integrated spectral coefficient parameter: the controls, 13.30 ± 6.62; hepatitis, 7.12 ± 7.45; cirrhosis, 3.95 ± 2.44 µm/cm (region of interest, 1 × 1 cm; frequency subband 0-10 Hz), and 10.48 ± 6.02, 8.27 ± 5.41, 3.89 ± 2.07 µm/cm, respectively (2 × 2 cm, 0-10 Hz). CONCLUSION: The investigated strain parameters showed statistically significant differences (P < .001) for the different stages of liver fibrosis in most of the cases and proved this method to be feasible.

Transcranial Ultrasonographic Image Analysis System for Decision Support in Parkinson Disease.

OBJECTIVES: Transcranial ultrasonography (US) is a relatively new neuroimaging modality proposed for early diagnostics of Parkinson disease (PD). The main limitation of transcranial US image-based diagnostics is a high degree of subjectivity caused by low quality of the transcranial images. The article presents a developed image analysis system and evaluates the potential of automated image analysis on transcranial US. METHODS: The system consists of algorithms for the segmentation and assessment of informative brain regions (midbrain and substantia nigra) and a decision support subsystem, which is equipped with 64 classification algorithms. Transcranial US images of 191 participants (118 patients with a clinical PD diagnosis and 73 healthy control participants) were analyzed. RESULTS: The diagnostic sensitivity and specificity achieved by the proposed system were 85% and 75%, respectively. CONCLUSIONS: Digital transcranial US image analysis is challenging, and the application of a such system as the sole instrument for decisions in clinical practice remains inconclusive. However, the proposed system could be used as a supplementary tool for automated assessment of US parameters for decision support in PD diagnostics and to reduce observer variability.

Specificity of transcranial sonography in parkinson spectrum disorders in comparison to degenerative cognitive syndromes.

BACKGROUND: Hyperechogenicity of the substantia nigra (SN+), detected by transcranial sonography (TCS), was reported as a characteristic finding in Parkinson's disease (PD), with high diagnostic accuracy values, when compared mainly to healthy controls or essential tremor (ET) group. However, some data is accumulating that the SN + could be detected in other neurodegenerative and even in non-neurodegenerative disorders too. Our aim was to estimate the diagnostic accuracy of TCS, mainly focusing on the specificity point, when applied to a range of the parkinsonian disorders, and comparing to the degenerative cognitive syndromes. METHODS: A prospective study was carried out at the Hospital of Lithuanian University of Health Sciences from January until September 2011. Initially, a TCS and clinical examination were performed on 258 patients and 76 controls. The General Electric Voluson 730 Expert ultrasound system was used. There were 12.8% of cases excluded with insufficient temporal bones, and 4.3% excluded with an unclear diagnosis. The studied sample consisted of the groups: PD (n = 71, 33.2%), ET (n = 58, 27.1%), PD and ET (n = 10, 4.7%), atypical parkinsonian syndromes (APS) (n = 3, 1.4%), hereditary neurodegenerative parkinsonism (HDP) (n = 3, 1.4%), secondary parkinsonism (SP) (n = 23, 10.8%), mild cognitive impairment (MCI) (n = 33, 15.4%), dementia (n = 13, 6.1%), and control (n = 71). RESULTS: There were 80.3% of PD patients at stages 1 & 2 according to Hoehn and Yahr. At the cut-off value of 0.20 cm² of the SN+, the sensitivity for PD was 94.3% and the specificity - 63.3% (ROC analysis, AUC 0.891), in comparison to the rest of the cohort. At the cut-off value of 0.26 cm², the sensitivity was 90% and the specificity 82.4%.The estimations for the lowest specificity for PD, in comparison to the latter subgroups (at the cut-off values of 0.20 cm² and 0.26 cm², respectively) were: 0% and 33.3% to APS, 33.3% and 66.7% to HDP, 34.8% and 69.6% to SP, 55.2% and 82.8% to ET, 75% and 91.7% to dementia. CONCLUSIONS: The high sensitivity of the test could be employed as a valuable screening tool. But TCS is more useful as a supplementary diagnostic method, due to the specificity values not being comprehensive.

Microbubble sonodestruction rate as a metric to evaluate sonoporation efficiency.

OBJECTIVES: The efficiency of sonoporation is directly related to microbubble cavitation and can be dependent on the microbubble sonodestruction rate. The objective of this study was to investigate whether the rate of microbubble sonodestruction can be used as a parameter to develop an implicit dosimetric method for sonoporation efficiency evaluation. METHODS: To evaluate the rate of microbubble sonodestruction as a function of the ultrasound (US) peak negative ultrasound pressure, 12-MHz diagnostic US was used in the B-scan mode. Chinese hamster ovary cells were exposed to therapeutic US at 880 kHz in the absence or presence of microbubbles. The sonoporation efficiency was evaluated by the sonotransfer of bleomycin, a cytotoxic, membrane-impermeable anticancer drug. RESULTS: At a low microbubble sonodestruction rate of 1/τ < 0.5 second(-1) (τ providing the time necessary to decrease the microbubble concentration to 37% of its initial value), cell viability remained basically unaffected, but the percentage of sonoporated cells did not reach 10%. At higher microbubble sonodestruction rates, the efficiencies of irreversible and reversible sonoporation started to increase linearly and reached the plateau at 5 seconds(-1). CONCLUSIONS: These results show that the microbubble sonodestruction rate can be used to predict the percentage of reversible and irreversible sonoporation.

Simulation of Ultrasound RF Signals Backscattered from a 3D Model of Pulsating Artery Surrounded by Tissue.

Arterial stiffness is an independent predictor of cardiovascular events. The motion of arterial tissues during the cardiac cycle is important as a mechanical deformation representing vessel elasticity and is related to arterial stiffness. In addition, arterial pulsation is the main source of endogenous tissue micro-motions currently being studied for tissue elastography. Methods based on artery motion detection are not applied in clinical practice these days, because they must be carefully investigated in silico and in vitro before wide usage in vivo. The purpose of this paper is to propose a dynamic 3D artery model capable of reproducing the biomechanical behavior of human blood vessels surrounded by elastic tissue for endogenous deformation elastography developments and feasibility studies. The framework is based on a 3D model of a pulsating artery surrounded by tissue and simulation of linear scanning by Field II software to generate realistic dynamic RF signals and B-mode ultrasound image sequential data. The model is defined by a spatial distribution of motions, having patient-specific slopes of radial and longitudinal motion components of the artery wall and surrounding tissues. It allows for simulating the quantified mechanical micro-motions in the volume of the model. Acceptable simulation errors calculated between modeled motion patterns and those estimated from simulated RF signals and B-scan images show that this approach is suitable for the development and validation of elastography algorithms based on motion detection.

A comparison of conductive textile-based and silver/silver chloride gel electrodes in exercise electrocardiogram recordings.

BACKGROUND: The goal of this study was to compare disposable silver/silver chloride and reusable conductive textile-based electrodes in electrocardiogram (ECG) signal monitoring during physical activity. MATERIALS AND METHODS: The reusable electrodes were produced using thin silver-plated nylon 117/17 2-ply conductive thread (Statex Productions & Vertriebs GmbH, Bremen, Germany) sewed with a sewing machine on a chest belt. The disposable and reusable electrodes were compared in vivo according to ECG signal baseline drift, broadband electrode noise properties, and influence of electrode area to ECG signal morphology and frequency content. Twelve volunteers were included in this study. RESULTS: Electroconductive textile-based ECG electrodes produce significantly more noise in a very low frequency band (0-0.67 Hz) and not significantly less of broadband noise (0-250 Hz) than disposable silver/silver chloride electrodes. Decreasing area of textile electrodes decreases fidelity of registered ECG signals at low frequencies. CONCLUSION: Textile electrodes having adequate area can be used in more applications than only R-R interval monitoring.

Diagnostic Ability of Radiofrequency Ultrasound in Parkinson's Disease Compared to Conventional Transcranial Sonography and Magnetic Resonance Imaging.

We aimed to estimate tissue displacements' parameters in midbrain using ultrasound radiofrequency (RF) signals and to compare diagnostic ability of this RF transcranial sonography (TCS)-based dynamic features of disease affected tissues with conventional TCS (cTCS) and magnetic resonance imaging (MRI) while differentiating patients with Parkinson's disease (PD) from healthy controls (HC). US tissue displacement waveform parametrization by RF TCS for endogenous brain tissue motion, standard neurological examination, cTCS and MRI data collection were performed for 20 PD patients and for 20 age- and sex-matched HC in a prospective manner. Three logistic regression models were constructed, and receiver operating characteristic (ROC) curve analyses were applied. The model constructed of RF TCS-based brain tissue displacement parameters-frequency of high-end spectra peak and root mean square-revealed presumably increased anisotropy in the midbrain and demonstrated rather good diagnostic ability in the PD evaluation, although it was not superior to that of the cTCS or MRI. Future studies are needed in order to establish the true place of RF TCS detected tissue displacement parameters for the evaluation of pathologically affected brain tissue.

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer's Disease.

We aim to estimate brain tissue displacements in the medial temporal lobe (MTL) using backscattered ultrasound radiofrequency (US RF) signals, and to assess the diagnostic ability of brain tissue displacement parameters for the differentiation of patients with Alzheimer's disease (AD) from healthy controls (HC). Standard neuropsychological evaluation and transcranial sonography (TCS) for endogenous brain tissue motion data collection are performed for 20 patients with AD and for 20 age- and sex-matched HC in a prospective manner. Essential modifications of our previous method in US waveform parametrization, raising the confidence of micrometer-range displacement signals in the presence of noise, are done. Four logistic regression models are constructed, and receiver operating characteristic (ROC) curve analyses are applied. All models have cut-offs from 61.0 to 68.5% and separate AD patients from HC with a sensitivity of 89.5% and a specificity of 100%. The area under a ROC curve of predicted probability in all models is excellent (from 95.2 to 95.7%). According to our models, AD patients can be differentiated from HC by a sharper morphology of some individual MTL spatial point displacements (i.e., by spreading the spectrum of displacements to the high-end frequencies with higher variability across spatial points within a region), by lower displacement amplitude differences between adjacent spatial points (i.e., lower strain), and by a higher interaction of these attributes.

Quantification of Endogenous Brain Tissue Displacement Imaging by Radiofrequency Ultrasound.

The purpose of this paper is a quantification of displacement parameters used in the imaging of brain tissue endogenous motion using ultrasonic radiofrequency (RF) signals. In a preclinical study, an ultrasonic diagnostic system with RF output was equipped with dedicated signal processing software and subject head-ultrasonic transducer stabilization. This allowed the use of RF scanning frames for the calculation of micrometer-range displacements, excluding sonographer-induced motions. Analysis of quantitative displacement estimates in dynamical phantom experiments showed that displacements of 55 µm down to 2 µm were quantified as confident according to Pearson correlation between signal fragments (minimum p ≤ 0.001). The same algorithm and scanning hardware were used in experiments and clinical imaging which allows translating phantom results to Alzheimer's disease patients and healthy elderly subjects as examples. The confident quantitative displacement waveforms of six in vivo heart-cycle episodes ranged from 8 µm up to 263 µm (Pearson correlation p ≤ 0.01). Displacement time sequences showed promising possibilities to evaluate the morphology of endogenous displacement signals at each point of the scanning plane, while displacement maps-regional distribution of displacement parameters-were essential for tissue characterization.

Endogenous motion of liver correlates to the severity of portal hypertension.

BACKGROUND: Degree of portal hypertension (PH) is the most important prognostic factor for the decompensation of liver cirrhosis and death, therefore adequate care for patients with liver cirrhosis requires timely detection and evaluation of the presence of clinically significant PH (CSPH) and severe PH (SPH). As the most accurate method for the assessment of PH is an invasive direct measurement of hepatic venous pressure gradient (HVPG), the search for non-invasive methods to diagnose these conditions is actively ongoing. AIM: To evaluate the feasibility of parameters of endogenously induced displacements and strain of liver to assess degree of PH. METHODS: Of 36 patients with liver cirrhosis and measured HVPG were included in the case-control study. Endogenous motion of the liver was characterized by derived parameters of region average tissue displacement signal (d antero, dr etro, d RMS) and results of endogenous tissue strain imaging using specific radiofrequency signal processing algorithm. Average endogenous strain µ and standard deviation σ of strain were assessed in the regions of interest (ROI) (1 cm × 1 cm and 2 cm × 2 cm in size) and different frequency subbands of endogenous motion (0-10 Hz and 10-20 Hz). RESULTS: Four parameters showed statistically significant (P < 0.05) correlation with HVPG measurement. The strongest correlation was obtained for the standard deviation of strain (estimated at 0-10 Hz and 2 cm × 2 cm ROI size). Three parameters showed statistically significant differences between patient groups with CSPH, but only d retro showed significant results in SPH analysis. According to ROC analysis area under the curve (AUC) of the σ ROI[0…10Hz, 2 cm × 2 cm] parameter reached 0.71 (P = 0.036) for the diagnosis of CSPH; with a cut-off value of 1.28 µm/cm providing 73% sensitivity and 70% specificity. AUC for the diagnosis of CSPH for µ ROI[0…10Hz, 1 cm × 1 cm] was 0.78 (P = 0.0024); with a cut-off value of 3.92 µm/cm providing 73% sensitivity and 80% specificity. D retro parameter had an AUC of 0.86 (P = 0.0001) for the diagnosis of CSPH and 0.84 (P = 0.0001) for the diagnosis of SPH. A cut-off value of -132.34 µm yielded 100% sensitivity for both conditions, whereas specificity was 80% and 72% for CSPH and SPH respectively. CONCLUSION: The parameters of endogenously induced displacements and strain of the liver correlated with HVPG and might be used for non-invasive diagnosis of PH.

Current Directions in the Auricular Vagus Nerve Stimulation I - A Physiological Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging technology in the field of bioelectronic medicine with applications in therapy. Modulation of the afferent vagus nerve affects a large number of physiological processes and bodily states associated with information transfer between the brain and body. These include disease mitigating effects and sustainable therapeutic applications ranging from chronic pain diseases, neurodegenerative and metabolic ailments to inflammatory and cardiovascular diseases. Given the current evidence from experimental research in animal and clinical studies we discuss basic aVNS mechanisms and their potential clinical effects. Collectively, we provide a focused review on the physiological role of the vagus nerve and formulate a biology-driven rationale for aVNS. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the framework of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on physiological aspects - a discussion of engineering aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Current Directions in the Auricular Vagus Nerve Stimulation II - An Engineering Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve - a powerful entrance to the brain - affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects - a discussion of physiological aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Algorithm for diabetes risk evaluation from past gestational diabetes data.

Gestational diabetes mellitus (GDM) is defined as glucose intolerance that is diagnosed in pregnancy period, leading to possible complications for both mother and fetus during pregnancy. The aim of this study was to build an objective method to evaluate diabetes mellitus (DM) risk from past GDM data recorded 15 years ago and find a short list of most informative indicators. The dataset consists of demographic, lifestyle, clinical, genetic and pregnancy related information recorded 15 years ago. Due to the large time gap data are limited and have missing values (MVs). Follow-up tests were performed to see if DM or impaired metabolism has developed after pregnancy with previously diagnosed GDM. The research steps involve pre-processing data to evaluate MVs, finding most informative attributes and testing standard classification algorithms to combine in to most effective voting meta-algorithm. Initially the attributes and records with large number of MVs were rejected. A small percentage (2.04%) was imputed using regression based methods. The data set was prepared for two scenarios: classification in two classes (1-healthy; 2-impaired metabolism including DM) and three classes (1-healthy; 2-impaired metabolism; 3-DM). Voting meta-algorithm combining best algorithms of 21 from five different groups including Bayesian, regression, lazy, rule, and decision trees makes classification more objective and not depending on preferences. Relative frequency of occurrence (RFO) analysis of attributes combined with voting meta-algorithm helped finding optimal amount of attributes giving best possible classification result. The algorithm applied to two class data set with 12 selected attributes produced accuracy of 75.85 and AUC = 0.82 with standard error of 0.11. Similarly for three class dataset the 9 attributes were selected allowing to reach classification accuracy 63.77 and AUC = 0.76 with standard error of 0.1. Meta-algorithm based classification of limited anamnestic GDM related data for DM prediction is proving to be effective. Testing multiple algorithms and performing RFO analysis appears to be natural and objective way of selecting most informative attributes and evaluating their importance.

Noninvasive Evaluation of Portal Hypertension Using a Supervised Learning Technique.

Portal hypertension (PHT) is a key event in the evolution of different chronic liver diseases and leads to the morbidity and mortality of patients. The traditional reliable PHT evaluation method is a hepatic venous pressure gradient (HVPG) measurement, which is invasive and not always available or acceptable to patients. The HVPG measurement is relatively expensive and depends on the experience of the physician. There are many potential noninvasive methods to predict PHT, of which liver transient elastography is determined to be the most accurate; however, even transient elastography lacks the accuracy to be a perfect noninvasive diagnostic method of PHT. In this research, we are focusing on noninvasive PHT assessment methods that rely on selected best-supervised learning algorithms which use a wide set of noninvasively obtained data, including demographical, clinical, laboratory, instrumental, and transient elastography measurements. In order to build the best performing classification meta-algorithm, a set of 21 classification algorithms have been tested. The problem was expanded by selecting the best performing clinical attributes using algorithm-specific filtering methods that give the lowest error rate to predict clinically significant PHT. The suggested meta-algorithm objectively outperforms other methods found in literature and can be a good substitute for invasive PHT evaluation methods.

Multiscale detection of transient evoked otoacoustic emissions.

This paper presents a unified approach to multiscale detection of transient evoked otoacoustic emissions (TEOAEs). Using statistical detection theory, it is shown that the optimal detector involves a time windowing operation where the window can be estimated from ensemble correlation information. The detector performs adaptive splitting of the signal into different frequency bands using either wavelet or wavelet packet decomposition. A simplified detector is proposed in which signal energy is omitted. The results show that the simplified detector performs significantly better than existing TEOAE detectors based on wave reproducibility or the modified variance ratio, whereas the detector involving signal energy does not offer such a performance advantage.

Computer-Aided Segmentation of the Mid-Brain in Trans-Cranial Ultrasound Images.

This paper presents a novel and rapid method developed for semi-automated segmentation of the mid-brain region in B-mode trans-cranial ultrasound (TCS) images. TCS is a relatively new neuroimaging tool having promising application in early diagnosis of Parkinson's disease. The quality of TCS images is much lower compared with the ultrasound images obtained during scanning of the soft tissues; the structures of interest in TCS are difficult to extract and to evaluate. The combination of an experience-based statistical shape model and intensity-amplitude invariant edge detector was proposed for the extraction of fuzzy boundaries of the mid-brain in TCS images. A statistical shape model was constructed using 90 manual delineations of the mid-brain region made by professional neurosonographer. Local phase-based edge detection strategy was applied for determination of plausible mid-brain boundary points used for statistical shape fitting. The proposed method was tested on other 40 clinical TCS images evaluated by two experts. The obtained averaged results of segmentation revealed that the differences between manual and automated measurements are statistically insignificant (p > 0.05).

Detection of occult paroxysmal atrial fibrillation.

This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to [Formula: see text] RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

Modelling of nonlinear effects and the response of ultrasound contrast micro bubbles: simulation and experiment.

The propagation of diagnostic ultrasonic imaging pulses in tissue and their interaction with contrast micro bubbles is a very complex physical process, which we assumed to be separable into three stages: pulse propagation in tissue, the interaction of the pulse with the contrast bubble, and the propagation of the scattered echo. The model driven approach is used to gain better knowledge of the complex processes involved. A simplified way of field simulation is chosen due to the complexity of the task and the necessity to estimate comparative contributions of each component of the process. Simulations are targeted at myocardial perfusion estimation. A modified method for spatial superposition of attenuated waves enables simulations of low intensity pulse pressure fields from weakly focused transducers in a nonlinear, attenuating, and liquid-like biological medium. These assumptions enable the use of quasi-linear calculations of the acoustic field. The simulations of acoustic bubble response are carried out with the Rayleigh-Plesset equation with the addition of radiation damping. Theoretical simulations with synthesised and experimentally sampled pulses show that the interaction of the excitation pulses with the contrast bubbles is the main cause of nonlinear scattering, and a 2-3 dB increase of second harmonic amplitude depends on nonlinear distortions of the incident pulse.