Advanced Methods in Biomedical Signal Processing and Analysis

Cover image

Title page

Table of Contents

Copyright

Contributors

1: Feature engineering methods

Abstract

1: Machine learning projects development standards and feature engineering

2: Exploratory data analysis

3: Data vs features

4: Feature reduction

5: Feature selection

6: Feature dimensionality reduction

7: Concluding remarks

References

2: Heart rate variability

Abstract

Acknowledgments

1: Introduction

2: Literature review

3: Results

4: Discussion

5: Conclusion

References

3: Understanding the suitability of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers

Abstract

1: Introduction

2: Literature review on cannabis and its legal status

3: Methods

4: Results

5: Discussion

6: Conclusion

References

4: Patient-specific ECG beat classification using EMD and deep learning-based technique

Abstract

1: Introduction

2: Database

3: Proposed methodology

4: Experimental results

5: Conclusions

References

5: Empirical wavelet transform and deep learning-based technique for ECG beat classification

Abstract

1: Introduction

2: Database

3: Proposed methodology

4: Experimental results

5: Conclusions

References

6: Development of an internet of things (IoT)-based pill monitoring device for geriatric patients

Abstract

1: Introduction

2: Literature review

3: Materials and methods

4: Results and discussions

5: Conclusion

Appendix

References

7: Biomedical robotics

Abstract

1: Introduction

2: Challenges and opportunities

References

8: Combating COVID-19 by employing machine learning predictions and projections

Abstract

1: Introduction

2: COVID-19: The 2020 pandemic

3: What is machine learning (ML)?

4: Key application of machine learning with illustrative examples: Fighting COVID-19

5: Concerns

6: Final thoughts

7: Takeaway points

References

9: Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network

Abstract

1: Introduction

2: Deep learning methods

3: Graph neural network

4: Applications of GNNs on neural data

5: Discussion

References

10: Improved extraction of the extreme thermal regions of breast IR images

Abstract

Acknowledgments

1: Introduction

2: Methodology

3: Experimental results and discussion

4: Conclusion

References

11: New metrics to assess the subtle changes of the heart's electromagnetic field

Abstract

Acknowledgments

1: Introduction

2: Information technology of magnetocardiography: Basis, technical means, diagnostic metrics

3: Metrics and information technologies for the analysis of magnetocardiographic data based on two-dimensional visualization of the solution of the inverse problem of magnetostatics

4: New metrics and information technologies based on computerized electrocardiography

5: New metrics and information technologies based on heart rate variability analysis

6: Conclusions

References

Further reading

12: The role of optimal and modified lead systems in electrocardiogram

Abstract

Acknowledgments

1: Introduction

2: Modified and optimal lead systems

3: ECG signal processing

4: Advantages of optimal and modified leads in ECG signal processing

5: Conclusion

References

13: Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy

Abstract

Acknowledgments

1: Introduction

2: The electroencephalogram (EEG) for healthcare

3: Signal acquisition, preprocessing, and features extraction

4: Results

5: Discussion and conclusion

References

14: Development of a novel low-cost multimodal microscope for food and biological applications

Abstract

Acknowledgments

1: Introduction

2: Literature review

3: Materials and methods

4: Results and discussion

5: Conclusion and future scope

References

Index