Thin Slice Judgments in the Clinical Context

Clinicians make a variety of judgments about their clients, from judging personality traits to making diagnoses, and a variety of methods are available to do so, ranging from observations to structured interviews. A large body of work demonstrates that from a brief glimpse of another’s nonverbal behavior, a variety of traits and inner states can be accurately perceived. Additionally, from these thin slices of behavior, even future outcomes can be predicted with some accuracy. Certain clinical disorders such as Parkinson’s disease and facial paralysis disrupt nonverbal behavior and may impair clinicians’ ability to make accurate judgments. In certain contexts, personality disorders, anxiety, depression, suicide attempts and outcomes can be detected from others’ nonverbal behavior. Additionally, thin slices can predict psychological adjustment to divorce, bereavement, sexual abuse, and well-being throughout life. Thus, for certain traits and disorders, judgments from a thin slice could provide a complementary tool for the clinician’s toolbox.Keywords: person perception, psychological disorders, nonverbal behavior, accurac

Multimodal analysis of verbal and nonverbal behaviour on the example of clinical depression

Clinical depression is a common mood disorder that may last for long periods, vary in severity, and could impair an individual’s ability to cope with daily life. Depression affects 350 million people worldwide and is therefore considered a burden not only on a personal and social level, but also on an economic one. Depression is the fourth most significant cause of suffering and disability worldwide and it is predicted to be the leading cause in 2020. Although treatment of depression disorders has proven to be effective in most cases, misdiagnosing depressed patients is a common barrier. Not only because depression manifests itself in different ways, but also because clinical interviews and self-reported history are currently the only ways of diagnosis, which risks a range of subjective biases either from the patient report or the clinical judgment. While automatic affective state recognition has become an active research area in the past decade, methods for mood disorder detection, such as depression, are still in their infancy. Using the advancements of affective sensing techniques, the long-term goal is to develop an objective multimodal system that supports clinicians during the diagnosis and monitoring of clinical depression. This dissertation aims to investigate the most promising characteristics of depression that can be “heard” and “seen” by a computer system for the task of detecting depression objectively. Using audio-video recordings of a clinically validated Australian depression dataset, several experiments are conducted to characterise depression-related patterns from verbal and nonverbal cues. Of particular interest in this dissertation is the exploration of speech style, speech prosody, eye activity, and head pose modalities. Statistical analysis and automatic classification of extracted cues are investigated. In addition, multimodal fusion methods of these modalities are examined to increase the accuracy and confidence level of detecting depression. These investigations result in a proposed system that detects depression in a binary manner (e.g. depressed vs. non-depressed) using temporal depression behavioural cues. The proposed system: (1) uses audio-video recordings to investigate verbal and nonverbal modalities, (2) extracts functional features from verbal and nonverbal modalities over the entire subjects’ segments, (3) pre- and post-normalises the extracted features, (4) selects features using the T-test, (5) classifies depression in a binary manner (i.e. severely depressed vs. healthy controls), and finally (6) fuses the individual modalities. The proposed system was validated for scalability and usability using generalisation experiments. Close studies were made of American and German depression datasets individually, and then also in combination with the Australian one. Applying the proposed system to the three datasets showed remarkably high classification results - up to a 95% average recall for the individual sets and 86% for the three combined. Strong implications are that the proposed system has the ability to generalise to different datasets recorded under quite different conditions such as collection procedure and task, depression diagnosis testing and scale, as well as cultural and language background. High performance was found consistently in speech prosody and eye activity in both individual and combined datasets, with head pose features a little less remarkable. Strong indications are that the extracted features are robust to large variations in recording conditions. Furthermore, once the modalities were combined, the classification results improved substantially. Therefore, the modalities are shown both to correlate and complement each other, working in tandem as an innovative system for diagnoses of depression across large variations of population and procedure

A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph.D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care

A Framework for Evaluating Cortical Architectural Anomalies in Temporal Lobe Epilepsy Patients

Focal cortical dysplasia (FCD) are localized regions of malformed cerebral cortex that are frequently associated with drug-resistant epilepsy. Currently, there is a lack of research towards providing quantitative methods for characterizing minor abnormalities in cortical architecture, hindering efforts to determine whether removal affects surgical outcome, and define potential imaging correlates. In our work, we have developed a tool to extract relevant features associated with cortical architectural abnormalities that can deal with artifacts including cortical layer distortions and morphological differences caused by cortical folding effects, and processing artifacts due to improper sectioning. This framework was applied to detect abnormalities across multiple subjects and slides using an unsupervised anomaly detection algorithm. Our results suggest that the technique is able to identify anomalies that correspond to visually-identifiable histological abnormalities. The frequency of abnormalities was found to differ among patients; however, the clinical significance of these findings is yet to be investigated

A clinical patient vital signs parameter measurement, processing and predictive algorithm using ECG

In the modern clinical and healthcare setting, the electronic collection and analysis of patient related vital signs and parameters are a fundamental part of the relevant treatment plan and positive patient response. Modern analytical techniques combined with readily available computer software today allow for the near real time analysis of digitally acquired measurements. In the clinical context, this can directly relate to patient survival rates and treatment success. The processing of clinical parameters, especially the Electrocardiogram (ECG) in the critical care setting has changed little in recent years and the analytical processes have mostly been managed by highly trained and experienced cardiac specialists. Warning, detection and measurement techniques are focused on the post processing of events relying heavily on averaging and analogue filtering to accurately capture waveform morphologies and deviations. This Ph. D. research investigates an alternative and the possibility to analyse, in the digital domain, bio signals with a focus on the ECG to determine if the feasibility of bit by bit or near real time analysis is indeed possible but more so if the data captured has any significance in the analysis and presentation of the wave patterns in a patient monitoring environment. The research and experiments have shown the potential for the development of logical models that address both the detection and short term predication of possible follow-on events with a focus on Myocardial Ischemic (MI) and Infraction based deviations. The research has shown that real time waveform processing compared to traditional graph based analysis, is both accurate and has the potential to be of benefit to the clinician by detecting deviations and morphologies in a real time domain. This is a significant step forward and has the potential to embed years of clinical experience into the measurement processes of clinical devices, in real terms. Also, providing expert analytical and identification input electronically at the patient bedside. The global human population is testing the healthcare systems and care capabilities with the shortage of clinical and healthcare providers in ever decreasing coverage of treatment that can be provided. The research is a moderate step in further realizing this and aiding the caregiver by providing true and relevant information and data, which assists in the clinical decision process and ultimately improving the required standard of patient care.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An absence of dystrophin in cerebellar Purkinje cells impairs inhibitory synaptic function in mature dystrophic mice

Duchenne muscular dystrophy (DMD) is a rapidly progressive X-linked recessive disease affecting about 1 in 3500 live male births. It is caused by mutations in the dystrophin gene, which result in the loss of dystrophin or expression of a non-functional truncated protein product. Full-length dystrophin is mainly expressed in muscles and the central nervous system. In addition to the degeneration of skeletal musculature, about one-third of patients with DMD display various degrees of intellectual impairment, commonly found with intelligence quotient (IQ) scores of one standard deviation below (IQ of 85) the normal population mean (IQ of 100). However, the mechanism underlying the cognitive deficits in DMD remains unclear and no effective treatment is available to reverse this condition in the affected individual. Recent studies showed that the life span of DMD patients today has increased from teens to their fourth decades. With longer survival, the quality of life becomes increasing important. Therefore, research on the cognitive aspect of DMD is as important as research on the muscular aspects because improvements in cognitive function will enhance the quality of life for the growing population of adult DMD patients. The aim of this thesis was to investigate the role of dystrophin in the central nervous system of the mdx mouse, a widely accepted murine model for DMD. This study employed the use of animal with different age groups, corresponding to young (3-4 months), adult (11-12 months), and aged (23-26 months). Adult and aged mdx mice are the focus in this study with findings from the older mouse especially valuable as, disease progression in aged mice closely resembling that of DMD. As numerous evidence has shown a high similarity between the specific cognitive dysfunctions seen in DMD (i.e. impaired verbal intelligence) and in patients with cerebellar lesions (i.e. language disorders), this study examined the function of cerebellar Purkinje cells in mdx mice using electrophysiological recording and calcium imaging. Overall, the data presented in this thesis provides new insights into the role of dystrophin in cerebellar Purkinje neurons. The findings suggest that dystrophin is important for normal inhibitory synaptic function, intrinsic electrophysiological properties, and calcium handling of the mature cerebellar Purkinje cells. The consequences of the absence of dystrophin including the altered GABAA receptor clustering and reduced peak amplitude of mIPSCs could be ameliorated when dystrophin was successfully rescued with Pip6f-PMO in an organotypic mdx cerebellar culture. If mdx mice and DMD patients share similar neuropathogenesis, the development of drugs targeting the altered functions in mdx Purkinje cells may serve as a potential therapy in alleviating the cognitive impairments seen in DMD

The Alliance as a Prerequisite to Emotional Processing in Psychotherapy

The quality of the therapeutic alliance has been shown to predict treatment outcomes across approaches to psychotherapy. However, the underlying mechanism by which the alliance leads to improvement remains to be clarified. In the emotion-focused therapy framework, it is theorized that a strong alliance facilitates emotional processing, which in turn leads to outcome. The hypothesis that a strong alliance creates the conditions for emotional processing has not been tested. Additionally, while research on emotion-focused therapy has shown that emotional processing predicts outcome over and above the alliance, this finding has not been evaluated within cognitive-behavioural therapy. The primary goals of this study were to 1) test the hypothesis that high levels of emotional processing primarily occur in the context of a strong alliance and 2) examine whether emotional processing predicts outcome over and above the alliance in cognitive-behavioural therapy. Observer-rated measures were used to assess emotional processing and the alliance in working phase psychotherapy sessions from adults who completed cognitive-behavioural therapy at a graduate training clinic. Interquartile ranges and results from one-way ANOVA (n = 31) showed higher means and lower variability in the alliance at high levels of emotional processing, suggesting a threshold. A Pearson correlation yielded a remarkably high association between emotional processing and treatment gains (r = .597). Additionally, hierarchical regression analyses (n = 19) indicated that working phase peak emotional processing predicted treatment gains over and above working phase alliance. The implications of these results for psychotherapy research and practice are discussed