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murugappan m biomedical signals computer aided diagnosis 2022

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Textbook in PDF format Biomedical signals provide unprecedented insight into abnormal or anomalous neurological conditions. The computer-aided diagnosis (CAD) system plays a key role in detecting neurological abnormalities and improving diagnosis and treatment consistency in medicine. This book covers different aspects of biomedical signals-based systems used in the automatic detection/identification of neurological disorders. Several biomedical signals are introduced and analyzed, including electroencephalogram (EEG), electrocardiogram (ECG), heart rate (HR), magnetoencephalogram (MEG), and electromyogram (EMG). It explains the role of the CAD system in processing biomedical signals and the application to neurological disorder diagnosis. The book provides the basics of biomedical signal processing, optimization methods, and machine learning/deep learning techniques used in designing CAD systems for neurological disorders. Preface Acknowledgements Abnormal EEG Detection Using Time-Frequency Images and Convolutional Neural Network Introduction Related Studies Materials and Methods Data Preparation Time-Frequency Representation-Based Abnormal EEG Detection Convolutional Neural Networks Inception-ResNet-V DenseNet SeizureNet Extreme Learning Machine Training and Validation Results Discussion Conclusion References Physical Action Categorization Pertaining to Certain Neurological Disorders Using Machine Learning-Based Signal Analysis Introduction Related Work Materials and Methods Acquisition Band Data Acquisition Methodology Pre-Processing and Segmentation Feature Vector Generation Time Domain Features Inter-Channel Statistics Maximum Similarity Index Maximum Covariance Index Power Spectral Density (PSD) Log Moments of Fourier Spectra (LMF) Feature Normalization Classifier Experimental Results and Discussion Dataset Experimental Results Discussion Conclusions References A Comparative Study on EEG Features for Neonatal Seizure Detection Introduction Methodology Database Pre-processing Feature Extraction Feature Ranking and Classification Results Discussions Effect of Number of Channels Effect of Feature Extraction Methods Effect of Feature Ranking Methods Effect of Classifiers Effect of Performance Metrics Limitations and Future Directions Conclusion References Hilbert Huang Transform (HHT) Analysis of Heart Rate Variability (HRV) in Recognition of Emotion in Children with Autism Spect Introduction Methodology Protocol Design ECG Data Acquisition Using Wearable Sensor Data Pre-processing Feature Extraction Hilbert-Huang Transform (HHT) Algorithm Feature Classification Results Pre-processing Extraction of Features Discussion and Conclusion References Detection of Tonic-Clonic Seizures Using Scalp EEG of Spectral Moments Introduction Methods Feature Extraction Rectangular Window Hanning Window Hamming Window Flattop Window Spectral Moments Classification Results and Discussion Conclusion References Investigation of the Brain Activation Pattern of Stroke Patients and Healthy Individuals During Happiness and Sadness Introduction Materials and Methods Subjects´ Background EEG Device Signal Pre-processing Feature Analysis Emotion Classification Results Average Values of Hjorth Parameters Distribution of Hjorth Parameters in Different Frequency Sub-bands Statistical Analysis of Hjorth Parameters Between Happiness and Sadness Average Distribution over Frontal Regions of Left and Right Hemispheres Emotion Classification Using KNN Classifier Discussion Emotion Impairment Analyzed Using Hjorth Parameters Region of Emotional Activation in the Brain The Involvement of Frontal Region in Emotion Processing Conclusion References A Novel Parametric Nonstationary Signal Model for EEG Signals and Its Application in Epileptic Seizure Detection Introduction Signal Modeling and Parametric Estimation Segmentation and AM-FM Demodulation Phase Smoothing and Instantaneous Frequency (IF) Estimation Simulation Results Applications Conclusion and Discussion References Biomedical Signal Analysis Using Entropy Measures: A Case Study of Motor Imaginary BCI in End Users with Disability Introduction Entropy History Entropy and Information Theory Mathematical Formulation Shannon Entropy Approximate Entropy Neural Network Entropy Numerical Results Channel Selection Conclusion and Further Work References Automatic Detection of Epilepsy Using CNN-GRU Hybrid Model Introduction Methodology Dataset Scalogram Architecture Training the Models Results Two-Class Performance Case : AB-CDE Case : ABCD-E Three-Class Performance Five-Class Performance Discussion Conclusion References Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic Systems Introduction Methodology Research Questions Search Strategy Selection Criteria Data Categorization Quality Assessment Data Extraction Review Conduction Metadata Analysis Data Analysis C: Acquisition Stage C: Acquisition Interface C: Analog-to-Digital Conversion C: Amplification C: Data Compression C: Transmission Protocol C: Preprocessing Stage C: Detrending/Filtering C: Feature Extraction C: Processing Stage C: Seizure Detection C: Emotion/Vigilance Classification C: Autism/Anomaly Detection C: Intention/Imagery/Gesture/Speech Recognition C: Sleep/Hypnosis-Level Classification Results RQ: What Hardware Accelerators Exist? RQ: Which Algorithms Are Implemented? RQ: Which Technology/Platform Is Preferred? RQ: Which Stages Are Hardware Accelerated? RQ: What Are the Advantages and Disadvantages? RQ: What Are the Metrics for the Assessment? Discussion Conclusion References Wearable Real-Time Epileptic Seizure Detection and Warning System Introduction Materials and Methods Block Diagram of the System Wearable Bio-signal Acquisition Subsystem Electrodermal Activity (EDA) Sensor Accelerometer (ACC) Module Wireless Communication over BLE Intelligent Epilepsy Detection and Alerting Subsystem Database Description Analysis Data Preprocessing Feature Extraction Training, Validation, and Testing Results and Discussion Performance Evaluation of EDA and ACC Sensor Reliability of the BLE Transmission Power Consumption of the Wearable Module Classification Results for ACC Data Alone EDA and ACC Data from Empatica and Prototype System Classification of Seizure Using EDA Alone, ACC Alone, and Fused EDA-ACC Conclusions References Analysis of Intramuscular Coherence of Lower Limb Muscle Activities Using Magnitude Squared Coherence Introduction Methods Magnitude Squared Coherence Results Discussion Conclusion References Index

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murugappan m biomedical signals computer aided diagnosis 2022