Dragana Nikolic

Identification of sound-localization cues in the HRTF of the bat-head model

Abstract Animals such as bats and dolphins exhibit impressive echolocation abilities in terms of ranging, resolution and imaging and therefore represent a valuable learning model for the study of spatial hearing and sound source localization leading to a better understanding of the hearing mechanism and further improvement of the existing localization strategies. This study aims to examine and understand the directional characteristics of a sonar receiver modeled upon the bat auditory system via measurements of the head-related transfer function (HRTF) in the horizontal plane. Four different models of the bat head were considered here and used to evaluate acoustic spectral characteristics of the sound received by the bats ears – a sphere model, a sphere model with a pinna attached (two pinnae of different size were used in this study) and a bat-head cast. The performed HRTF measurements of the bat-head models were further analyzed and compared to identify monaural spectral localization cues in the horizontal plane defined by the bats head and pinna shape and size. Our study suggests that the acoustical characteristics of a bio-inspired sonar head measured and specified in advance can potentially improve the performance of a receiver. Moreover, the generated auditory models may hold clues for the design of receiver characteristics in ultrasound imaging and navigation systems.

A model of breathing abnormalities in sleep for development of classification and diagnosis techniques

Following the recognition of the obstructive sleep apnoea (OSA) disease, snoring achieved a completely new status from being a mere social problem to an important clinical disorder. Using modern signal analysis equipment and software enables breath-by-breath investigation of snoring and opens possibilities for automatic detection of specific acoustic events. The simulation model proposed in this study provides a repeat-able test-bed for signal processing techniques that are under development to aid clinical diagnosis of underlying pathological problems and to identify periods of sleep apnea that may develop into more serious conditions. Noise and artifacts can be added to the simulated data for assessment of algorithm robustness prior to application to clinical data that is being recorded non-invasively and remotely.

At what data length do cerebral autoregulation measures stabilise

OBJECTIVE Cerebral autoregulation is commonly assessed through mathematical models that use non-invasive measurements of arterial blood pressure and cerebral blood flow velocity. There is no agreement in the literature as to what is the minimum length of data needed for the cerebral autoregulation coefficients to stabilise. APPROACH We introduce a simple empirical tool for studying the minimum length of time series needed to parameterise three popular cerebral autoregulation coefficients ARI, Mx and Phase (in the low frequency range [0.07-0.2] Hz), which can be easily applied in a more general context. We use our recently collected data, from which we select high quality (absence of non-physiological artefacts), baseline ABP-CBFV time series (16 min each). The data were beat-to-beat averaged and downsampled at 10 Hz. MAIN RESULT On average, ARI exhibits greater variability than Mx and Phase, when calculated for short intervals; however, it stabilises fastest. SIGNIFICANCE Our results show that values of ARI, Mx and Phase calculated on intervals shorter than 3 min (1800 samples), 6 min (3600 samples) and 5 min (3000 samples), respectively, may be very sensitive to changes in the length of data interval.

Increased blood pressure variability upon standing up improves reproducibility of cerebral autoregulation indices

Dynamic cerebral autoregulation, that is the transient response of cerebral blood flow to changes in arterial blood pressure, is currently assessed using a variety of different time series methods and data collection protocols. In the continuing absence of a gold standard for the study of cerebral autoregulation it is unclear to what extent does the assessment depend on the choice of a computational method and protocol. We use continuous measurements of blood pressure and cerebral blood flow velocity in the middle cerebral artery from the cohorts of 18 normotensive subjects performing sit-to-stand manoeuvre. We estimate cerebral autoregulation using a wide variety of black-box approaches (including the following six autoregulation indices ARI, Mx, Sx, Dx, FIR and ARX) and compare them in the context of reproducibility and variability. For all autoregulation indices, considered here, the intra-class correlation was greater during the standing protocol, however, it was significantly greater (Fishers Z-test) for Mx (p < 0.03), Sx (p < 0.003) and Dx (p < 0.03). In the specific case of the sit-to-stand manoeuvre, measurements taken immediately after standing up greatly improve the reproducibility of the autoregulation coefficients. This is generally coupled with an increase of the within-group spread of the estimates.

Assessing Inter-subject Variability in Cerebral Blood Flow Control Measurements

The aim of this study is to assess inter-individual variability and repeatability in measures assessing blood flow control in the brain during spontaneous and enhanced fluctuations in blood pressure. There is clear evidence of inter-individual difference during enhanced blood pressure variability, but not at rest. This difference exceeds the within-individual variability by factor of 2.73.

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study

OBJECTIVE Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. APPROACH Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). MAIN RESULTS For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p  =  0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p  <  0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). SIGNIFICANCE When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

“Velcro-type” crackles predict specific radiologic features of fibrotic interstitial lung disease

Background“Velcro-type” crackles on chest auscultation are considered a typical acoustic finding of Fibrotic Interstitial Lung Disease (FILD), however whether they may have a role in the early detection of these disorders has been unknown. This study investigated how “Velcro-type” crackles correlate with the presence of distinct patterns of FILD and individual radiologic features of pulmonary fibrosis on High Resolution Computed Tomography (HRCT).MethodsLung sounds were digitally recorded from subjects immediately prior to undergoing clinically indicated chest HRCT. Audio files were independently assessed by two chest physicians and both full volume and single HRCT sections corresponding to the recording sites were extracted. The relationships between audible “Velcro-type” crackles and radiologic HRCT patterns and individual features of pulmonary fibrosis were investigated using multivariate regression models.Results148 subjects were enrolled: bilateral “Velcro-type” crackles predicted the presence of FILD at HRCT (OR 13.46, 95% CI 5.85–30.96, p < 0.001) and most strongly the Usual Interstitial Pneumonia (UIP) pattern (OR 19.8, 95% CI 5.28–74.25, p < 0.001). Extent of isolated reticulation (OR 2.04, 95% CI 1.62–2.57, p < 0.001), honeycombing (OR 1.88, 95% CI 1.24–2.83, < 0.01), ground glass opacities (OR 1.74, 95% CI 1.29–2.32, p < 0.001) and traction bronchiectasis (OR 1.55, 95% CI 1.03–2.32, p < 0.05) were all independently associated with the presence of “Velcro-type” crackles.Conclusions“Velcro-type” crackles predict the presence of FILD and directly correlate with the extent of distinct radiologic features of pulmonary fibrosis. Such evidence provides grounds for further investigation of lung sounds as an early identification tool in FILD.

Assessing cerebral blood flow control from variability in blood pressure and arterial CO2 levels.

Blood flow to the brain is controlled by a number of physiological mechanisms that respond to changes in arterial blood pressure, arterial CO2 levels and many other factors. Assessing the integrity of this control system is a major challenge. We report on repeatability of measures based on single and multiple input models during spontaneous and enhanced fluctuations in blood pressure.

Multivariable Approach to Dynamic ECG Classification

Abstract The first step towards a heart attack is a condition called Ventricular Arrhythmia. This paper proposes a system that uses the advanced digital signal processing techniques to analyse electrocardiogram (ECG) signals and recognise the Arrhythmia condition. In addition, proposed method can differentiate the more dangerous condition, Ventricular Arrhythmia from a simple Arrhythmia. The proposed technique combines the classical ECG signal parameters (e.g. Heart Rate Variability) with the standard statistical signal parameters, nonlinear parameters used in the fields of Chaos Theory and parameters obtained using Symbolic Analysis techniques. Linear Discriminant Analysis (LDA) is employed in order to reduce the size of ECG parameter set and is followed by a clustering algorithm.

Monitoring and Assessing Crew Performance in High-Speed Marine Craft - Methodological Considerations

This paper proposes a method to monitor and assess human perform ance specific to high-speed marine craft operation. The high-speed craft crew’s ability to efficiently perform their allotted tasks is affected by the manner in which the vessel responds to the variable sea conditions. In general, the reaction of human body to high-speed boat motion and vibration is recognized as the main cause of fatigue during and post transits; whereas random shock repre sents the most likely cause of injuries during transits. The pilot experiment in troduced in this paper was designed and performed with the intention to identify and evaluate measures of crew performance during and after a transit in a ma rine environment that can serve to indicate increasing fatigue, decreased func tional capabilities and thus possible increased risk of injury.