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.

Exploring Australian intensive care physicians clinical judgement during donation after cardiac death : an exploratory qualitative study

BACKGROUND: Donation after Cardiac Death (DCD) is one possible solution to the world wide organ shortage. Intensive care physicians are central to DCD becoming successful since they are responsible for making the clinical judgements and decisions associated with DCD. Yet international evidence shows health care professionals have not embraced DCD and are often reluctant to consider it as an option for patients. PURPOSE: To explore intensive care physicians' clinical judgements when selecting a suitable DCD candidate. METHODS: Using interpretative exploratory methods six intensive care physicians were interviewed from three hospital sites in Australia. Following verbatim transcription, data was subjected to thematic analysis. FINDINGS: Three distinct themes emerged. Reducing harm and increasing benefit was a major focus of intensive care physicians during determination of DCD. There was an acceptance of DCD if there was clear evidence that donation was what the patient and family wanted. Characteristics of a defensible decision reflected the characteristics of sequencing, separation and isolation, timing, consensus and collaboration, trust and communication to ensure that judgements were robust and defensible. The final theme revealed the importance of minimising uncertainty and discomfort when predicting length of survival following withdrawal of life-sustaining treatment. CONCLUSION: DCD decisions are made within an environment of uncertainty due to the imprecision associated with predicting time of death. Lack of certainty contributed to the cautious and collaborative strategies used by intensive care physicians when dealing with patients, family members and colleagues around end-of-life decisions, initiation of withdrawal of life-sustaining treatment and the discussion about DCD. This study recommends that nationally consistent policies are urgently needed to increase the degree of certainty for intensive care staff concerning the DCD processes.

Factors potentiating the risk of sudden infant death syndrome associated with the prone position

Background. In several studies the sudden infant death syndrome (SIDS) has been significantly associated with sleeping in the prone position. It is not known how the prone position increases the risk of SIDS. Methods. We analyzed data from a case-control study (58 infants with SIDS and 120 control infants) and a prospective cohort study (22 infants with SIDS and 213 control infants) in Tasmania. Interactions were examined in matched analyses with a multiplicative model of interaction. Results. In the case-control study, SIDS was significantly associated with sleeping in the prone position, as compared with other positions (unadjusted odds ratio, 4.5; 95 percent confidence interval, 2.1 to 9.6). The strength of this association was increased among infants who slept on natural-fiber mattresses (P = 0.05), infants who were swaddled (P = 0.09), infants who slept in heated rooms (P = 0.006), and infants who had had a recent illness (P = 0.02). These variables had no significant effect on infants who did not sleep in the prone position. A history of recent illness was significantly associated with SIDS among infants who slept prone (odds ratio, 5.7; 95 percent confidence interval, 1.8 to 19) but not among infants who slept in other positions (odds ratio, 0.83). In the cohort study, the risk of SIDS was greater among infants who slept prone on natural-fiber mattresses (odds ratio, 6.6; 95 percent confidence interval, 1.3 to 33) than among infants who slept prone on other types of mattresses (odds ratio, 1.8). Conclusions. When infants sleep prone, the elevated risk of SIDS is increased by each of four factors: the use of natural-fiber mattresses, swaddling, recent illness, and the use of heating in bedrooms.

Home monitoring for infants at risk of the sudden infant death syndrome

This study evaluates the effectiveness and social implications of home monitoring of 31 infants at risk of sudden infant death syndrome (SIDS). Thirteen siblings of children dying of SIDS, nine near miss SIDS infants and nine preterm infants with apnoea persisting beyond 40 weeks post conceptual age were monitored from a mean age of 15 days to a mean of 10 months. Chest movement detection monitors were used in 27 and thoracic impedance monitors in four. Genuine apnoeic episodes were reported by 21 families, and 13 infants required resuscitation. Apnoeic episodes occurred in all nine preterm infants but in only five (38%) of the siblings of SIDS (P<0.05). Troublesome false alarms were a major problem occurring with 61% of the infants and were more common with the preterm infants than the siblings of SIDS. All but two couples stated that the monitor decreased anxiety and improved their quality of life. Most parents accepted that the social restrictions imposed by the monitor were part of the caring process but four couples were highly resentful of the changes imposed on their lifestyle. The monitors used were far from ideal with malfunction occurring in 17, necessitating replacement in six, repair in six and cessation of monitoring in three. The parents became ingenious in modifying the monitors to their own individual requirements Although none of these 31 ‘at risk’ infants died the study sample was far too small to conclude whether home monitoring prevented any cases of SIDS.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Letter: Brain natriuretic peptide is a potentially useful screening tool for the detection of cardiovascular disease in patients with rheumatoid arthritis

Patients with rheumatoid arthritis (RA) have a significantly higher risk of coronary heart disease, despite being less likely to report symptoms of angina, and are more likely to experience unrecognised myocardial infarction and sudden cardiac death than non-RA controls.1 Furthermore, left ventricular diastolic dysfunction has been described in up to 40% of patients with RA.2...

Death : is it fatal?

Death is a common occurrence in the world of today and impacts upon families in a variety of ways, depending upon a range of circumstances. This paper examines the impact that a sudden, unexpected death of the founder of a family business had on both the business and the family. The basis for the research is a single case study of a family business operating in regional Victoria. At the time of the death of the family founder (from a fatal motorcycle accident) his adult children had not been prepared to take over the company. The paper examines if the death of the founder proved fatal to the family business.

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.

Infant wrapping as an evidence-based strategy to encourage settling and the utilisation of the supine sleep position

Objective: To identify knowledge, attitudes and practices of child health nurses relating to infant wrapping as an effective settling/sleep strategy. Methods: A pre-test/post-test intervention design was used to explore knowledge, attitudes and practices relating to wrapping in a sample of child health nurses (n=182): a) pre-test survey; b) educational intervention incorporating evidence relating to infant wrapping; SIDS&KIDS endorsed infant wrapping pamphlet; Safe Sleeping recommendations. Emphasis was placed on infant wrapping as an effective settling strategy for parents to use as an alternative to prone positioning; c) post-test survey to evaluate intervention effectiveness. Results: Pretest results identified wide variation in nurses’ knowledge, attitudes and practices of infant wrapping as a settling/sleep strategy. The intervention increased awareness of wrapping guidelines and self-reported practices relating to parent advice. Significant positive changes in nurses’ awareness of wrapping guidelines (p<0.001); to wrap in supine position only (p<0.001); and parental advice to use wrapping as an alternative strategy to prone positioning to assist settling/sleep (p<0.01), were achieved post-test. Conclusions: Managing unsettled infants and promoting safe sleeping practices are issues routinely addressed by child health nurses working with parents of young infants. Queensland has a high incidence of prone sleeping. Infant wrapping is an evidence-based strategy to improve settling and promote supine sleep consistent with public health recommendations. Infant wrapping guidelines are now included in Queensland Health’s state policy and Australian SIDSandKids information relating to safe infant sleeping. In communicating complex health messages to parents, health professionals have a key role in reinforcing safe sleeping recommendations and offering safe, effective settling/sleep strategies to address the non-recommended use of prone positioning for unsettled infants.

Changing parental high risk behaviour in the reduction of SIDS : the imperative of translational research

Objectives To investigate the factors associated with sudden infant death syndrome (SIDS) from birth to age 2 years, whether recent advice has been followed, whether any new risk factors have emerged, and the specific circumstances in which SIDS occurs while cosleeping (infant sharing the same bed or sofa with an adult or child). Design Four year population based case-control study. Parents were interviewed shortly after the death or after the reference sleep (within 24 hours) of the two control groups. Setting South west region of England (population 4.9 million, 184 800 births). Participants 80 SIDS infants and two control groups weighted for age and time of reference sleep: 87 randomly selected controls and 82 controls at high risk of SIDS (young, socially deprived, multiparous mothers who smoked). Results The median age at death (66 days) was more than three weeks less than in a study in the same region a decade earlier. Of the SIDS infants, 54% died while cosleeping compared with 20% among both control groups. Much of this excess may be explained by a significant multivariable interaction between cosleeping and recent parental use of alcohol or drugs (31% v 3% random controls) and the increased proportion of SIDS infants who had coslept on a sofa (17% v 1%). One fifth of SIDS infants used a pillow for the last sleep (21% v 3%) and one quarter were swaddled (24% v 6%). More mothers of SIDS infants than random control infants smoked during pregnancy (60% v 14%), whereas one quarter of the SIDS infants were preterm (26% v 5%) or were in fair or poor health for the last sleep (28% v 6%). All of these differences were significant in the multivariable analysis regardless of which control group was used for comparison. The significance of covering the infant’s head, postnatal exposure to tobacco smoke, dummy use, and sleeping in the side position has diminished although a significant proportion of SIDS infants were still found prone (29% v 10%). Conclusions Many of the SIDS infants had coslept in a hazardous environment. The major influences on risk, regardless of markers for socioeconomic deprivation, are amenable to change and specific advice needs to be given, particularly on use of alcohol or drugs before cosleeping and cosleeping on a sofa.

Growth and meaning from negotiating the complex journey of being an Emergency Medical Dispatcher

Emergency Medical Dispatchers (EMDs) are charged with taking the calls of those who ring the national emergency number for urgent medical assistance, for dispatching paramedical crews, and for providing as much assistance as can be offered remotely until paramedics arrive. In a job role which is filled with vicarious trauma, emergency situations, pressure, abuse, grief and loss, EMDs are often challenged in maintaining their mental health. The seemingly senseless death of a teenager who commits suicide, the devastating loss of a baby to Sudden Infant Death Syndrome, lives lost through natural disasters, and multiple vehicle fatalities are only a few of the types of experiences EMDs are faced with in the course of their work. However, amongst the horror are positive stories such as coaching a caller to negotiate the birth of a baby and saving a life in jeopardy from heart failure. EMD’s need to cope with the daily challenges of the role; make sense of their work and create meaning in order to have a fulfilled and sustainable career. Although some people in this work struggle greatly to withstand the impacts of vicarious trauma, there are also stories of personal growth. In this Chapter we use a case study to explore how meaning is made for those who are an auditory witness to a continual flux of trauma for others and how the traumatic experiences EMDs bear witness to can also be a catalyst for posttraumatic growth.

Cardiac ARIA Index: Measuring Accessibility to Cardiovascular Services in Rural and Remote Australia via Applied Geographic Spatial Technology

Cardiovascular disease (CVD) continues to impose a heavy burden in terms of cost, disability and death in Australia. Evidence suggests that increasing remoteness, where cardiac services are scarce, is linked to an increased risk of dying from CVD. Fatal CVD events are reported to be between 20% and 50% higher in rural areas compared to major cities. The Cardiac ARIA project, with its extensive use of geographic Information Systems (GIS), ranks each of Australia’s 20,387 urban, rural and remote population centres by accessibility to essential services or resources for the management of a cardiac event. This unique, innovative and highly collaborative project delivers a powerful tool to highlight and combat the burden imposed by cardiovascular disease (CVD) in Australia. Cardiac ARIA is innovative. It is a model that could be applied internationally and to other acute and chronic conditions such as mental health, midwifery, cancer, respiratory, diabetes and burns services. Cardiac ARIA was designed to: 1. Determine by expert panel, what were the minimal services and resources required for the management of a cardiac event in any urban, rural or remote population locations in Australia using a single patient pathway to access care. 2. Derive a classification using GIS accessibility modelling for each of Australia’s 20,387 urban, rural and remote population locations. 3. Compare the Cardiac ARIA categories and population locations with census derived population characteristics. Key findings are as follows: • In the event of a cardiac emergency, the majority of Australians had very good access to cardiac services. Approximately 71% or 13.9 million people lived within one hour of a category one hospital. • 68% of older Australians lived within one hour of a category one hospital (Principal Referral Hospital with access to Cardiac Catheterisation). • Only 40% of indigenous people lived within one hour of the category one hospital. • 16% (74000) of indigenous people lived more than one hour from a hospital. • 3% (91,000) of people 65 years of age or older lived more than one hour from any hospital or clinic. • Approximately 96%, or 19 million, of people lived within one hour of the four key services to support cardiac rehabilitation and secondary prevention. • 75% of indigenous people lived within one hour of the four cardiac rehabilitation services to support cardiac rehabilitation and secondary prevention. Fourteen percent (64,000 persons) indigenous people had poor access to the four key services to support cardiac rehabilitation and secondary prevention. • 12% (56,000) of indigenous people were more than one hour from a hospital and only had access one the four key services (usually a medical service) to support cardiac rehabilitation and secondary prevention.

Mapping Services to Support a Patient's Journey through Evidence-Based Care Pathways After a Cardiac Event

Background: There are inequalities in geographical access and delivery of health care services in Australia, particularly for cardiovascular disease (CVD), Australia's major cause of death. Analyses and models that can inform and positively influence strategies to augment services and preventative measures are needed. The Cardiac-ARIA project is using geographical spatial technology (GIS) to develop a national index for each of Australia's 13,000 population centres. The index will describe the spatial distribution of CVD health care services available to support populations at risk, in a timely manner, after a major cardiac event. Methods: In the initial phase of the project, an expert panel of cardiologists and an emergency physician have identified key elements of national and international guidelines for management of acute coronary syndromes, cardiac arrest, life-threatening arrhythmias and acute heart failure, from the time of onset (potentially dial 000) to return from the hospital to the community (cardiac rehabilitation). Results: A systematic search has been undertaken to identify the geographical location of, and type of, cardiac services currently available. This has enabled derivation of a master dataset of necessary services, e.g. telephone networks, ambulance, RFDS, helicopter retrieval services, road networks, hospitals, general practitioners, medical community centres, pathology services, CCUs, catheterisation laboratories, cardio-thoracic surgery units and cardiac rehabilitation services. Conclusion: This unique and innovative project has the potential to deliver a powerful tool to both highlight and combat the burden of disease of CVD in urban and regional Australia.