Enhancing acoustic emission signals from multi-cylinder diesel engine

This paper presents techniques which can be viewed as pre-processing step towards diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time-frequency analysis, selection of optimum frequency band. Some results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals are also outlined. The results on separation of RMS signals show this technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Rumination, post-traumatic growth, and distress : structural equation modelling with cancer survivors

Objective Theoretical models of post-traumatic growth (PTG) have been derived in the general trauma literature to describe the post-trauma experience that facilitates the perception of positive life changes. To develop a statistical model identifying factors that are associated with PTG, structural equation modelling (SEM) was used in the current study to assess the relationships between perception of diagnosis severity, rumination, social support, distress, and PTG. Method A statistical model of PTG was tested in a sample of participants diagnosed with a variety of cancers (N=313). Results An initial principal components analysis of the measure used to assess rumination revealed three components: intrusive rumination, deliberate rumination of benefits, and life purpose rumination. SEM results indicated that the model fit the data well and that 30% of the variance in PTG was explained by the variables. Trauma severity was directly related to distress, but not to PTG. Deliberately ruminating on benefits and social support were directly related to PTG. Life purpose rumination and intrusive rumination were associated with distress. Conclusions The model showed that in addition to having unique correlating factors, distress was not related to PTG, thereby providing support for the notion that these are discrete constructs in the post-diagnosis experience. The statistical model provides support that post-diagnosis experience is simultaneously shaped by positive and negative life changes and that one or the other outcome may be prevalent or may occur concurrently. As such, an implication for practice is the need for supportive care that is holistic in nature.

Immigration distress and associated factors among Vietnamese momen in transnational marriages in Taiwan

The purpose of this study was to explore the types and predictors of immigration distress among Vietnamese women in transnational marriages in Taiwan. A cross-sectional survey with face-toface interviews was conducted for data collection. A convenient sample of 203 Vietnamese women in transnational marriages in southern Taiwan was recruited. The Demographic Inventory measured the participants’ age, education, employment status, religion, length of residency and number of children, as well as their spouse’s age, education, employment status and religion. The Demand of Immigration Specific Distress scale measured the level of distress and had six subscales: loss, novelty, occupational adjustment, language accommodation, discrimination and alienation. Among the 203 participants, 6.4% had a high level of immigration distress; 91.1% had moderate distress; and 2.5% had minor distress. Higher mean scores were found for the loss, novelty and language accommodation subscales of the Demand of Immigration specific Distress scale. Participant’s (r = 0.321, p < 0.01) and spouse’s (r = 0.375, p < 0.01) unemployment, and more children (r = 0.129, p < 0.05) led to greater immigration distress. Length of residency in Taiwan (r = 0.576, p < 0.001) was an effective predictor of immigration distress. It indicated that the participants who had stayed fewer years in Taiwan had a higher level of immigrant distress. Health care professionals need to be aware that the female newcomers in transnational marriages are highly susceptible to immigration distress. The study suggests that healthcare professionals need to provide a comprehensive assessment of immigration distress to detect health problems early and administer culturally appropriate healthcare for immigrant women in transnational marriages.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

The psychological distress of the young driver : a brief report

Objective: To explore the role of psychological distress in the self-reported risky driving of young novice drivers. Design: Cross-sectional online survey of 761 tertiary students aged 17-25 years with an intermediate (Provisional) driving licence who completed Kessler’s Psychological Distress Scale and the Behaviour of Young Novice Drivers Scale. Setting: Queensland, Australia, August-October 2009. Main outcome measures: Psychological distress, risky driving. Results: Regression analyses revealed that psychological distress uniquely explained 8.5% of the variance in young novice’s risky driving, with adolescents experiencing psychological distress also reporting higher levels of risky driving. Psychological distress uniquely explained a significant 6.7% and 9.5% of variance in risky driving for males and females respectively. Conclusions: Medical practitioners treating adolescents who have been injured through risky behaviour need to aware of the potential contribution of psychological distress, whilst mental health professionals working with adolescents experiencing psychological distress need to be aware of this additional source of potential harm. The nature of the causal relationships linking psychological distress and risky driving behaviour are not yet fully understood, indicating a need for further research so that strategies such as screening can be investigated.

Feasibility of brief psychological distress screening by a community-based telephone helpline for cancer patients and carers

Background Up to one-third of people affected by cancer experience ongoing psychological distress and would benefit from screening followed by an appropriate level of psychological intervention. This rarely occurs in routine clinical practice due to barriers such as lack of time and experience. This study investigated the feasibility of community-based telephone helpline operators screening callers affected by cancer for their level of distress using a brief screening tool (Distress Thermometer), and triaging to the appropriate level of care using a tiered model. Methods Consecutive cancer patients and carers who contacted the helpline from September-December 2006 (n = 341) were invited to participate. Routine screening and triage was conducted by helpline operators at this time. Additional socio-demographic and psychosocial adjustment data were collected by telephone interview by research staff following the initial call. Results The Distress Thermometer had good overall accuracy in detecting general psychosocial morbidity (Hospital Anxiety and Depression Scale cut-off score ≥ 15) for cancer patients (AUC = 0.73) and carers (AUC = 0.70). We found 73% of participants met the Distress Thermometer cut-off for distress caseness according to the Hospital Anxiety and Depression Scale (a score ≥ 4), and optimal sensitivity (83%, 77%) and specificity (51%, 48%) were obtained with cut-offs of ≥ 4 and ≥ 6 in the patient and carer groups respectively. Distress was significantly associated with the Hospital Anxiety and Depression Scale scores (total, as well as anxiety and depression subscales) and level of care in cancer patients, as well as with the Hospital Anxiety and Depression Scale anxiety subscale for carers. There was a trend for more highly distressed callers to be triaged to more intensive care, with patients with distress scores ≥ 4 more likely to receive extended or specialist care. Conclusions Our data suggest that it was feasible for community-based cancer helpline operators to screen callers for distress using a brief screening tool, the Distress Thermometer, and to triage callers to an appropriate level of care using a tiered model. The Distress Thermometer is a rapid and non-invasive alternative to longer psychometric instruments, and may provide part of the solution in ensuring distressed patients and carers affected by cancer are identified and supported appropriately.

When does pain and distress relief hastening death become killing?

This paper discusses the question of when pain and distress relief known to hasten death would cross the line between permissible conduct and killing. The issue is discussed in the context of organ donation after cardiac death, and considers the administration of analgesics, sedatives, and the controversial use of paralysing agents in the provision and withdrawal of ventilation.

Three carrier ambiguity resolution : distance-independent performance demonstrated using semi-generated triple frequency GPS signals

In spite of significant research in the development of efficient algorithms for three carrier ambiguity resolution, full performance potential of the additional frequency signals cannot be demonstrated effectively without actual triple frequency data. In addition, all the proposed algorithms showed their difficulties in reliable resolution of the medium-lane and narrow-lane ambiguities in different long-range scenarios. In this contribution, we will investigate the effects of various distance-dependent biases, identifying the tropospheric delay to be the key limitation for long-range three carrier ambiguity resolution. In order to achieve reliable ambiguity resolution in regional networks with the inter-station distances of hundreds of kilometers, a new geometry-free and ionosphere-free model is proposed to fix the integer ambiguities of the medium-lane or narrow-lane observables over just several minutes without distance constraint. Finally, the semi-simulation method is introduced to generate the third frequency signals from dual-frequency GPS data and experimentally demonstrate the research findings of this paper.