Affective Computing in Healthcare : Applications Based on Biosignals and Artificial Intelligence / edited by M. Murugappan.

Format:

Book

Contributor:

Murugappan, M., editor.

Series:

IPEM-IOP series in physics and engineering in medicine and biology.

IPEM-IOP Series in Physics and Engineering in Medicine and Biology Series

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Human-computer interaction.

Medicine--Data processing.

Medicine.

Physical Description:

1 online resource (225 pages)

Edition:

First edition.

Place of Publication:

Bristol, England : IOP Publishing, [2023]

Summary:

The healthcare applications of affective computing are gaining traction as biomedical signals are being increasingly used in system design and development. Affective computing and its applications in healthcare are presented in this book during a time of great advancement in machine learning and deep learning algorithms.

Contents:

Intro

Preface

Editor biography

M Murugappan

List of contributors

Chapter 1 Anxiety recognition using a new EEG signal analysis approach based on sample density in a Chebyshev chaotic map

1.1 Introduction

1.2 Material and methods

1.2.1 EEG DASPS database

1.2.2 Normalisation

1.2.3 Chebyshev chaotic map

1.2.4 Feature selection

1.2.5 Classification

1.3 Results

1.4 Discussions

1.5 Conclusion

Acknowledgments

Compliance with ethical standards

Funding

Conflict of interest

Statements of ethical approval

References

Chapter 2 Evaluating cognitive load during lexical decision tasks for monolinguals and bilinguals using EEG

2.1 Introduction

2.2 Methodology

2.2.1 Block diagram

2.2.2 Participant selection

2.2.3 Lexical decision task

2.2.4 Data acquisition

2.2.5 Experiment protocol

2.2.6 EEG data processing

2.2.7 Statistical analysis

2.3 Results

2.3.1 Task performance analysis

2.3.2 Reaction-time analysis

2.3.3 Event-related changes within frequency bands

2.4 Discussion

2.5 Conclusion

Chapter 3 Detection of psychological stress using principal component analysis of phonocardiography signals

3.1 Introduction

3.2 Methodology

3.2.1 Signal acquisition

3.2.2 Inter-beat interval signal formation

3.2.3 Empirical mode decomposition

3.2.4 Principal component analysis

3.2.5 Classifiers

3.2.6 Performance metrics

3.3 Results and discussions

3.4 Conclusions

Acknowledgements

Chapter 4 Affective computational advertising based on perceptual metrics

4.1 Introduction

4.2 Related work

4.2.1 Advertisements as affective stimuli

4.2.2 Advertising and healthcare

4.2.3 Programme influence on ad perception

4.2.4 Strategic video-in-video advertising.

4.2.5 Inference summary and research questions

4.3 Materials and methods

4.3.1 Materials

4.3.2 Participants

4.3.3 Procedure

4.3.4 User data analyses

4.3.5 Inferences and Affective Computational ADvertising design rules

4.4 Affective Computational ADvertising formulation

4.4.1 Brute-force Affective Computational ADvertising algorithm

Algorithm 4.1: Brute-force ACAD implementation

4.4.2 Computational affective advertising versus Affective Computational ADvertising scheduling

4.5 Validational study and hypotheses

4.5.1 Materials and methods

4.5.2 Results

4.6 Summary and conclusions

Chapter 5 Machine-learning-based emotion recognition in arousal-valence space using photoplethysmogram signals

5.1 Introduction and background

5.2 Materials and methods

5.2.1 Dataset description

5.2.2 Data pre-processing

5.2.3 Feature extraction

5.2.4 Feature reduction

5.2.5 Data balancing

5.2.6 Machine-learning models

5.2.7 Evaluation criteria

5.3 Results and discussion

5.4 Conclusion

Chapter 6 EEG-based human emotion classification from channel-wise feature extraction and feature selection

6.1 Introduction

6.2 Related literature

6.2.1 Affective computing

6.2.2 Brain-computer interface devices

6.2.3 Elicitation of human emotions

6.2.4 Pre-processing methods

6.2.5 Feature extraction

6.2.6 Feature selection

6.2.7 Machine-learning classification

6.3 Methodology

6.3.1 The experiment and experimental data

6.3.2 Pre-processing

6.3.3 Feature extraction

6.3.4 Feature selection

6.3.5 Machine-learning classification

6.4 Results and analysis

6.4.1 Experimental results

6.4.2 SEED-IV dataset

6.4.3 Application to healthcare

6.5 Discussion and conclusions

6.6 Challenges and future works

References.

Chapter 7 Detection of physiological body movements in affective disorder patients using EEG signals and deep neural networks

7.1 Introduction

7.1.1 Chapter organisation

7.2 Literature survey

7.3 Proposed system

7.3.1 Data collection

7.3.2 Pre-processing

7.3.3 Data training

7.3.4 Classification by neural networks

7.4 Results

7.4.1 Summary of the utilised datasets

7.4.2 Comparison of different hyperparameters

7.4.3 Statistical parameter comparison of different datasets

7.4.4 Comparative analysis

7.5 Conclusion

7.6 Limitations and future work

Chapter 8 Voice-enabled real-time affective framework for negative emotion monitoring

8.1 Introduction

8.1.1 Wearables for emotion monitoring

8.1.2 Real-time stress detection

8.1.3 Cognitive behavioural therapy

8.1.4 Privacy in health-monitoring systems

8.2 Methods

8.2.1 Real-time emotion detection

8.2.2 Use of cognitive behavioural therapy in real time

8.3 Experimental results

8.3.1 Emotion-detection accuracy

8.3.2 False positives and audio sample size

8.3.3 Voice types and unknown users

8.3.4 Error patterns

8.3.5 Employing cognitive behavioural therapy

8.3.6 Conclusion for deployment

8.4 Conclusion

Chapter 9 Differential diagnosis tool in healthcare application using respiratory sounds and convolutional neural network

9.1 Introduction

9.1.1 Literature review

9.2 Methodology

9.2.1 Dataset

9.2.2 Pre-processing

9.2.3 Classification using deep-learning algorithms

9.3 Results and discussion

9.4 Conclusions

Authors' contributions

Ethical approval

Chapter 10 Virtual reality and augmented reality based affective computing applications in healthcare, challenges, and its future direction

10.1 Introduction.

10.2 Generic applications of augmented reality/virtual reality

10.3 Design of affective augmented reality/virtual reality applications for the medical domain

10.4 Affective augmented reality/virtual reality applications in healthcare

10.4.1 Mental health disorders

10.4.2 Rehabilitation related to traumatic brain injuries

10.4.3 Rehabilitation and physiotherapy

10.5 Assessment of affective responses in augmented reality/virtual reality applications

10.6 Design challenges of affective applications using augmented reality/virtual reality technology in healthcare

10.7 Conclusion

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9780750351843

0750351845