Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Cognitive Computation, Journal Year: 2023, Volume and Issue: 16(1), P. 1 - 44

Published: Nov. 13, 2023

Abstract The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption the domain due typical blackbox nature these models. Therefore, facilitate among practitioners, models' predictions must explainable interpretable. emerging field (XAI) aims justify trustworthiness predictions. This work presents systematic review literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during last decade. Research questions carefully formulated categorise into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations SHAP, Gradient-weighted Class Activation Mapping GradCAM, Layer-wise Relevance Propagation LRP, XAI. categorisation provides broad coverage interpretation spectrum from intrinsic Ante-hoc models) complex patterns Post-hoc taking local explanations global scope. Additionally, forms interpretations providing in-depth insight factors support clinical diagnosis AD are also discussed. Finally, limitations, needs open challenges research outlined possible prospects their usage detection.

Language: Английский

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Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder

Published: Feb. 2, 2023

At present, various electronic devices are used to monitor human heart rates. However, its functions avoid predicting the problems caused by rate variability in advance and analyzing implications. It makes it difficult diagnose variability. A should have an average of 72. same time, newborn's beat between 120 160 beats per minute. baby born with autism spectrum disorder may a lower-than-average rate. Complete blockage at birth is rare. Abnormal leads block. So, there high chance child's death due permanent any time. Most diseases children Autism Spectrum Disorder (ASD) present birth. significant congenital disability hole heart. Many people do not realize that having holes common occurrence. Before born, tiny form muscular wall divides into right left halves. This paper proposed Machine Learning-Based Evaluation identify Heart Rate Variability Response Children Disorder. The reasons for this yet be identified. 70 cent perforations resolve spontaneously before or after Exceptionally, close properly require surgery perforator brace, depending on location size perforation.

Language: Английский

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Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Cognitive Computation, Journal Year: 2023, Volume and Issue: 15(6), P. 1767 - 1812

Published: June 24, 2023

Abstract The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways automate the process make it more objective facilitate needs healthcare industry. Artificial Intelligence (AI) machine learning (ML) emerged as most promising approaches CHA process. In this paper, we background delve into extensive research recently undertaken in domain provide a comprehensive survey state-of-the-art. particular, careful selection significant works published literature is reviewed elaborate range enabling technologies AI/ML techniques used for CHA, including conventional supervised unsupervised learning, deep reinforcement natural language processing, image processing techniques. Furthermore, an overview various means data acquisition benchmark datasets. Finally, discuss open issues challenges using AI ML along with some possible solutions. summary, paper presents tools, lists methods provides technological advancements, usage issues, domain. We hope first-of-its-kind will significantly contribute identifying gaps complex rapidly evolving interdisciplinary mental health field.

Language: Английский

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State-of-the-Art of Stress Prediction from Heart Rate Variability Using Artificial Intelligence

Cognitive Computation, Journal Year: 2023, Volume and Issue: 16(2), P. 455 - 481

Published: Oct. 12, 2023

Abstract Recent advancements in the manufacturing and commercialisation of miniaturised sensors low-cost wearables have enabled an effortless monitoring lifestyle by detecting analysing physiological signals. Heart rate variability (HRV) denotes time interval between consecutive heartbeats.The HRV signal, as detected devices, has been popularly used indicative measure to estimate level stress, depression, anxiety. For years, artificial intelligence (AI)-based learning systems known for their predictive capabilities, recent AI models with deep (DL) architectures successfully applied achieve unprecedented accuracy. In order determine effective methodologies collection, processing, prediction stress from data, this work presents depth analysis 43 studies reporting application various algorithms. The methods are summarised tables thoroughly evaluated ensure completeness findings reported results. To make comprehensive, a detailed review conducted on sensing technologies, pre-processing multi-modal employed models. This is followed critical examination how Machine Learning (ML) models, utilised predicting data. addition, reseults selected carefully analysed identify features that enable perform better. Finally, challenges using predict listed, along some possible mitigation strategies. aims highlight impact AI-based expected aid development more meticulous techniques.

Language: Английский

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Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Brain Informatics, Journal Year: 2023, Volume and Issue: 10(1)

Published: June 21, 2023

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in safe environment, recognise specific triggers and gradually increase their perceived threats. Public-speaking anxiety (PSA) prevalent form of social anxiety, characterised by stressful arousal generated when presenting an audience. In self-guided VRET, participants can tolerance reduce anxiety-induced PSA over time. However, creating such VR environment determining physiological indices or distress open challenge. Environment modelling, character creation animation, psychological state determination the use machine learning (ML) models for stress detection are equally important, multi-disciplinary expertise required. this work, we have explored series ML with publicly available data sets (using electroencephalogram heart rate variability) predict states. If detect arousal, trigger calming activities allow cope overcome distress. Here, discuss means effective selection parameters detection. We propose pipeline model problem different parameter settings context virtual therapy. This be extended other domains interest where crucial. Finally, implemented biofeedback framework VRET successfully provided feedback as brain laterality index from our acquired multimodal anxiety.

Language: Английский

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Effectiveness of immersive VR therapy in reducing stress-associated symptoms in Ukraine

European journal of psychotraumatology, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 22, 2025

Background: The ongoing conflict in Ukraine has led to a rise stress-related symptoms, including anxiety and depression, among veterans, necessitating accessible effective mental health interventions. Traditional rehabilitation resources are often limited, prompting exploration into alternative therapies.Objective: This paper aims assess the effectiveness of immersive 360° video-based Virtual Reality (VR) therapy as an enhancement standard programmes for Ukrainian veterans experiencing depression.Method: A randomised controlled trial (RCT) was conducted with 69 participants, who were randomly assigned either experimental group (n = 34), receiving daily VR sessions alongside rehabilitation, or control 35), following alone. Anxiety depression assessed using Hospital Depression Scale (HADS) both at baseline post-intervention. Additionally, momentary changes mood measured immediately before after each session evaluate immediate effects. intervention designed veteran expert feedback enhance emotional regulation stress resilience, integrating evidence-based psychotherapeutic techniques.Results: Results demonstrate significant rapid improvement reduction session, along reductions (up 14.5%) 12.3%) upon programme completion. Consistent results across all study iterations confirmed reliability scalability 360-VR short-term tool.Conclusions: Immersive presents effective, solution managing psychological impact war, particularly within limitations Ukraine's healthcare system.

Language: Английский

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Distress detection in VR environment using Empatica E4 wristband and Bittium Faros 360

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: March 5, 2025

Distress detection in virtual reality systems offers a wealth of opportunities to improve user experiences and enhance therapeutic practices by catering individual physiological emotional states. This study evaluates the performance two wearable devices, Empatica E4 wristband Faros 360, detecting distress motion-controlled interactive environment. Subjects were exposed baseline measurement VR scenes, one non-interactive interactive, involving problem-solving distractors. Heart rate measurements from both including mean heart rate, root square successive differences, subject-specific thresholds, utilized explore intensity frequency. Both sensors adequately captured signals, with demonstrating higher signal-to-noise ratio consistency. While correlation coefficients moderately positive between data, indicating linear relationship, small absolute error values suggested good agreement measuring rate. Analysis occurrence during scene revealed that devices detect more high- medium-level occurrences compared scene. Device-specific factors emphasized due differences detected events devices.

Language: Английский

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Virtual Reality Exposure Therapy

Published: Jan. 1, 2024

Language: Английский

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A Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism

Communications in computer and information science, Journal Year: 2023, Volume and Issue: unknown, P. 446 - 458

Published: Jan. 1, 2023

Language: Английский

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Augmenting self-guided virtual-reality exposure therapy for social anxiety with biofeedback: a randomised controlled trial

Frontiers in Psychiatry, Journal Year: 2024, Volume and Issue: 15

Published: Nov. 12, 2024

We previously found that self-guided Virtual Reality Exposure Therapy (VRET) improved Public Speaking Anxiety (PSA) and reduced heartrate. Elevated heartrate characterises social anxiety the VRET seemed to reduce Thus, receiving continuous biofeedback about physiological arousal during could help socially anxious individuals manage their anxiety. The present study aimed determine whether enhances responsiveness of VRET. Seventy-two with high self-reported were randomly allocated VRET-plus-biofeedback (n=38; 25 completers) or VRET-alone (n=35; completers). Three hour-long sessions delivered over two consecutive weeks. During each session, participants a 20-minute public speech in front virtual audience. Participants group received on frontal alpha asymmetry (FAA) within environment asked lower accordingly. both groups completed psychometric assessments after session at one-month follow-up. PSA by end treatment overall one month across groups. showed steadier reduction FAA first greater than group. Biofeedback can steady perceived exposure. benefits for are sustained therapy.

Language: Английский

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