Modelling and Analysis of Active Biopotential Signals in Healthcare

PRELIMS.pdf
Preface
Acknowledgements
Editor biographies
Varun Bajaj
G R Sinha
Contributor list
CH001.pdf
Chapter 1 Classification of schizophrenia patients through empirical wavelet transformation using electroencephalogram signals
1.1 Introduction
1.2 Methodology
1.2.1 Dataset
1.2.2 Empirical wavelet transform
1.2.3 Feature extraction
1.2.4 Classification techniques
1.2.5 Performance parameters
1.3 Results and discussion
1.4 Conclusion
References
CH002.pdf
Chapter 2 Fuzzy scale invariant feature transform phase locking value and its application to PTSD EEG data
2.1 Introduction
2.2 Method
2.2.1 FSIFT-PLV
2.2.2 Functional connectivity graph indices
2.3 Data
2.3.1 Synthetic data
2.3.2 EEG data
2.4 Results
2.4.1 Synthetic EEG data
2.4.2 Real EEG data
2.5 Conclusion
Acknowledgments
References
CH003.pdf
Chapter 3 Weighted complex network based framework for epilepsy detection from EEG signals
3.1 Introduction
3.2 Weighted complex network based framework
3.2.1 Conversion of EEG signals into the WCN
3.2.2 Statistical feature extraction from the WCN
3.2.3 Evaluation of the AWD using classifiers
3.2.4 Evaluation of performance
3.3 Experimental results and discussion
3.3.1 Experimental data
3.3.2 Results and discussion
3.4 Conclusion
References
CH004.pdf
Chapter 4 Epileptic seizure prediction and onset zone localization using intracranial and scalp electroencephalographic and magnetoencephalographic signals
4.1 Epileptic seizure prediction
4.2 Seizure onset zone identification
4.3 Performance indices
4.4 Conclusion and future scope
Acknowledgments
References
CH005.pdf
Chapter 5 Automatic drowsiness detection based on variational non-linear chirp mode decomposition using electroencephalogram signals
5.1 Introduction
5.2 Methodology
5.2.1 Dataset
5.2.2 Variational non-linear chirp mode decomposition (VNCMD)
5.2.3 Feature extraction
5.2.4 Classifiers
5.3 Results and discussion
5.4 Conclusion
References
CH006.pdf
Chapter 6 Noise removal and classification of EEG signals using the Fourier decomposition method
6.1 Introduction
6.2 Related work
6.3 Proposed work
6.3.1 Dataset
6.3.2 The Fourier decomposition method
6.4 Classification
6.5 Experimental results and discussion
6.6 Conclusion and proposed future scope
References
CH007.pdf
Chapter 7 Reliable and accurate information extraction from surface electromyographic signals
7.1 Surface electromyography
7.2 Surface EMG applications
7.3 Challenges in sEMG recording
7.4 Detection of atypical signals in HD-sEMG
7.4.1 Feature extraction
7.4.2 Detection methods
7.5 Myoelectric prosthesis control, a hot topic
7.6 Conclusion and future scope
Acknowledgments
References
CH008.pdf
Chapter 8 Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns
8.1 Introduction
8.2 Materials and methods
8.2.1 The wavelet transform
8.2.2 The one-dimensional LBP
8.2.3 The support vector machine classifier
8.2.4 The decision tree classifier
8.2.5 The ensemble bagging classifier
8.2.6 The ensemble boosting classifier
8.2.7 The k-nearest neighbor classifier
8.2.8 The linear discriminant classifier
8.3 Experimental work and results
8.4 Conclusion
References
CH009.pdf
Chapter 9 Empirical wavelet transform based classification of surface electromyogram signals for hand movements
9.1 Introduction
9.2 Dataset
9.3 Overview of empirical wavelet transform
9.4 The proposed method
9.4.1 EWT based decomposition
9.4.2 Feature computation
9.4.3 Feature ranking
9.4.4 Classification
9.5 Simulation results
9.6 Discussion
9.7 Conclusion and future scope
References
CH010.pdf
Chapter 10 Analysis of the muscular activity pattern of recurring physical action
10.1 Introduction
10.2 Analytical expressions of joint moments
10.2.1 Data collection and joint moment analysis
10.3 Myoelectric signals during recursive work
10.3.1 The major muscles of the lower extremity
10.3.2 Data collection and subjects
10.3.3 The myoelectrical signal, electrodes and recording
10.3.4 Crosstalk and muscle movement artefacts
10.3.5 The measured electromyogram
10.3.6 Muscle activity pattern
10.4 Joint force estimation
10.4.1 Joint moment pattern and performance measurement
10.5 Conclusions
References
CH011.pdf
Chapter 11 Cloud-based cardiac health monitoring using event-driven ECG processing and ensemble classification techniques
11.1 Introduction
11.2 Background and literature review
11.3 ECG in healthcare
11.4 The proposed approach
11.4.1 Dataset
11.4.2 The event-driven acquisition
11.4.3 The event-driven segmentation
11.4.4 The adaptive rate resampling and denoising
11.4.5 Extraction of features
11.4.6 Machine learning methods
11.5 The performance evaluation measures
11.5.1 Compression ratio
11.5.2 Computational complexity
11.5.3 Classification accuracy
11.6 Experimental results and discussion
11.6.1 Experimental results
11.6.2 Discussion
11.7 Conclusion
Acknowledgments
References
CH012.pdf
Chapter 12 Electrocardiogram beat classification using deep convolutional neural network techniques
12.1 Introduction
12.2 Material and methods
12.2.1 The MIT-BIH database
12.2.2 Producing ECG beat images
12.2.3 Convolutional neural networks (CNNs)
12.2.4 Deep transfer learning (DTL)
12.2.5 Support vector machines
12.2.6 Performance metrics
12.3 Experimental work and results
12.4 Discussion
12.5 Conclusion
References
CH013.pdf
Chapter 13 ECG signal watermarking to enhance the security of telecardiology
13.1 Introduction
13.2 Preliminaries
13.3 Prediction error expansion
13.4 Prediction scheme and ECG database
13.4.1 Deep neural network
13.4.2 ECG database
13.5 Training and embedding
13.5.1 Training
13.5.2 Embedding scheme 1
13.5.3 Embedding scheme 2
13.5.4 Embedding scheme 3
13.6 Improved embedding scheme
13.6.1 The effect of ECG abnormalities
13.6.2 Performance on the ECG-ID database
13.7 Conclusion
References
CH014.pdf
Chapter 14 Statistical measures and analysis in electrocardiogram (ECG) signal processing
14.1 Introduction
14.2 The electrocardiogram (ECG) signal and its characteristics
14.2.1 ECG signal generation
14.2.2 ECG signal characteristics
14.3 Statistical measures and analysis
14.4 Statistical analysis in ECG signal processing
14.5 Conclusion
References
CH015.pdf
Chapter 15 The impact of regional atrophy on Alzheimer’s disease and its identification using 3D texture analysis
15.1 Introduction
15.2 Regional atrophy and Alzheimer’s disease
15.3 Related works
15.3.1 VBM based methods
15.3.2 Texture analysis based methods
15.3.3 Shape analysis based methods
15.3.4 Other methods
15.4 Materials and methods
15.4.1 Dataset
15.4.2 The proposed methodology
15.5 Experiments and results
15.5.1 Experiment 1: Voxel as features (VAF) obtained from GM and WM regions
15.5.2 Experiment 2: Volumetric features evaluated on the 3D-DWT sub-bands obtained from all 116 regions
15.5.3 Experiment 3: Volumetric features evaluated on 3D-DWT sub-bands obtained from the top five regions
15.5.4 Experiment 4: Features obtained after applying feature selection on the features of the top five selected regions
15.5.5 Performance comparison with state-of-art methods
15.6 Conclusions
Acknowledgments
References