1,220 research outputs found
Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches
Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people.
Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy.
The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech.
In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection
Life on a scale:Deep brain stimulation in anorexia nervosa
Anorexia nervosa (AN) is a severe psychiatric disorder marked by low body weight, body image abnormalities, and anxiety and shows elevated rates of morbidity, comorbidity and mortality. Given the limited availability of evidence-based treatments, there is an urgent need to investigate new therapeutic options that are informed by the disorder’s underlying neurobiological mechanisms. This thesis represents the first study in the Netherlands and one of a limited number globally to evaluate the efficacy, safety, and tolerability of deep brain stimulation (DBS) in the treatment of AN. DBS has the advantage of being both reversible and adjustable. Beyond assessing the primary impact of DBS on body weight, psychological parameters, and quality of life, this research is novel in its comprehensive approach. We integrated evaluations of efficacy with critical examinations of the functional impact of DBS in AN, including fMRI, electroencephalography EEG, as well as endocrinological and metabolic assessments. Furthermore, this work situates AN within a broader theoretical framework, specifically focusing on its manifestation as a form of self-destructive behavior. Finally, we reflect on the practical, ethical and philosophical aspects of conducting an experimental, invasive procedure in a vulnerable patient group. This thesis deepens our understanding of the neurobiological underpinnings of AN and paves the way for future research and potential clinical applications of DBS in the management of severe and enduring AN
Deep Learning Techniques for Electroencephalography Analysis
In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
EEG mismatch responses in a multimodal roving stimulus paradigm provide evidence for probabilistic inference across audition, somatosensation, and vision
The human brain is constantly subjected to a multimodal stream of probabilistic sensory inputs. Electroencephalography (EEG) signatures, such as the mismatch negativity (MMN) and the P3, can give valuable insight into neuronal probabilistic inference. Although reported for different modalities, mismatch responses have largely been studied in isolation, with a strong focus on the auditory MMN. To investigate the extent to which early and late mismatch responses across modalities represent comparable signatures of uni- and cross-modal probabilistic inference in the hierarchically structured cortex, we recorded EEG from 32 participants undergoing a novel tri-modal roving stimulus paradigm. The employed sequences consisted of high and low intensity stimuli in the auditory, somatosensory and visual modalities and were governed by unimodal transition probabilities and cross-modal conditional dependencies. We found modality specific signatures of MMN (~100–200 ms) in all three modalities, which were source localized to the respective sensory cortices and shared right lateralized prefrontal sources. Additionally, we identified a cross-modal signature of mismatch processing in the P3a time range (~300–350 ms), for which a common network with frontal dominance was found. Across modalities, the mismatch responses showed highly comparable parametric effects of stimulus train length, which were driven by standard and deviant response modulations in opposite directions. Strikingly, P3a responses across modalities were increased for mispredicted stimuli with low cross-modal conditional probability, suggesting sensitivity to multimodal (global) predictive sequence properties. Finally, model comparisons indicated that the observed single trial dynamics were best captured by Bayesian learning models tracking unimodal stimulus transitions as well as cross-modal conditional dependencies
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Cardiovascular health, orthostatic hypotension, and cognitive aging
Cardiovascular health (CVH) plays an important role in dementia development. Ideal CVH, defined by Life’s Simple 7 (LS7), has been associated with a lower risk of dementia in older adults. Orthostatic hypotension (OH) may be a novel cardiovascular risk factor that can affect dementia development. In this thesis, population-based cohort studies were conducted to investigate the role of LS7-defined CVH and OH in cognitive aging in people aged ≥60 years using data from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K).
Study I investigated LS7-defined CVH in relation to transitions between normal cognition, cognitive impairment, no dementia (CIND), and dementia. The study found that people with better CVH had a lower hazard of transitioning directly from normal cognition to CIND (HR = 0.76, 95% CI = 0.61-0.95) and dementia (HR = 0.42, 95% CI = 0.21-0.82) in people aged <78 years. In addition, people aged <78 years with better CVH had two to three more years of life living with normal cognition. However, CVH, defined by LS7, was not related to transitions between cognitive states in people aged ≥78 years.
Study II evaluated the associations between OH and dementia. Of the 2532 people who were initially free of dementia, 615 (24.3%) people had OH. People with OH had higher hazards of developing dementia (HR = 1.40, 95% CI = 1.10–1.76) and Alzheimer’s disease (HR = 1.39, 95% CI = 1.04–1.86). In addition, OH was related to a higher hazard of progression from CIND to dementia in people with CIND (HR = 1.54, 95% CI = 1.05–2.25) but not with incident CIND in those without CIND and dementia (HR = 1.15, 95% CI = 0.94–1.40).
Study III investigated the impact of OH on the development of CVDs and dementia in people initially free of CVDs as well as the impact of OH on dementia development in people with CVDs. The study found that in people who were initially free of CVDs, individuals who had OH at baseline had a higher hazard of developing CVDs (HR = 1.33, 95% CI = 1.12-1.59) but not dementia (HR = 1.22, 95% CI = 0.83-1.81) compared to those without OH. Among those with CVDs, persons with OH also had a higher hazard of dementia (HR = 1.54, 95% CI = 1.06-2.23) compared to those without OH.
Study IV assessed the associations of OH, in the presence or absence of frailty, with dementia and mortality. This study found that individuals who had OH at baseline had a higher hazard of dementia in the presence (HR = 2.73, 95% CI = 1.82-4.10) and absence (HR = 2.28, 95% CI = 1.47-3.54) of frailty than robust persons without OH. However, OH was only associated with a higher hazard of death without dementia when accompanied by frailty (HR = 1.56, 95% CI = 1.25-1.96).
Conclusions. Maintaining ideal CVH may protect against cognitive dysfunction and reduce years of life with cognitive dysfunction in younger old age. OH may be a potential modifiable risk factor for dementia, and the intermediate development of CVDs may help explain the association between OH and dementia
Managing risks and harms associated with the use of anabolic steroids
Background: People using AAS may adopt a range of strategies to prevent and treat adverse health conditions potentially associated with the use of these substances (AAS-HC). These strategies include seeking support from physicians, using the needle and syringe exchange programme (NSP) and seeking support from informal sources such as coaches and online forums. The process of identifying risks and harms, adopting and modifying health-related strategies is similar to the methods of risk-management employed in other fields of human activity. This approach recognises the importance of the informal body of knowledge produced by decades of AAS-related folk-pharmacology and seeks to understand harm-reduction from the users’ perspective.Objectives: The primary objective of this thesis is to investigate the strategies adopted by people using AAS to prevent and treat AAS-HC. Secondary objectives include to explore the factors associated with the adoption of health strategies and the occurrence of AAS-HC, as well as the barriers and facilitators experienced by AAS users when accessing health services and other sources of support.Methods: To achieve the objectives above, three work packages (WP) were produced as part of a mixed-methods research design. WP1 is a systematic review and meta-analysis of the prevalence of AAS users seeking support from physicians. WP2 is a cross-sectional online survey that identified AAS-HC, risk factors and health-related strategies adopted by AAS users in the UK. WP3 is a qualitative study based on in-depth interviews to discuss the experiences of AAS users and their risk-management strategies (RMS).Results: The estimated overall prevalence of AAS users seeking support from physicians is 37.1%. Higher prevalence rates were observed in studies from Australia (67.3%) and amongst clients of the NSP (54.1%), whilst the lowest was observed among adolescents (17.3%). The health conditions most commonly reported by the 883 participants of the online survey were insomnia (33.3%) and anxiety (32.2%). Most participants adopted preventive strategies such as having blood tests in the last 12 months (86.2%) and seeking a GP to treat AAS-HC (55.0%). Those who sought a GP for AAS-related information were 76% less likely to report an AAS-HC in the last 12 months. The interviews described AAS users’ RMS as a continuous process of awareness and behavioural changes. Participants described an extensive use of private health services and other sources of support to bypass the barriers experienced by AAS users engaging with the public health system.Conclusion: A large number of AAS users refrain from seeking support from physicians. Health professionals should be trained to recognise and manage the most common AAS-HC and help users improve their RMS. Further studies should investigate the efficacy of AAS-related RMS and the subpopulations of AAS users more likely to experience AAS-HC and less likely to engage with health services.<br/
- …