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Table of Contents
Intro
Acknowledgment
Contents
1: Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimerś Disease Risk
1.1 Introduction
1.2 Patient Recruitment and Data Availability
1.2.1 Disease Categorization
1.2.2 Cognitive Battery
1.2.3 Mnemonic Strategy Training
1.2.4 Demographics and Neuropsychological Measurements
1.3 Interventions
1.4 Neuropsychological Performance
1.5 Methods
1.5.1 EEG Data Acquisition
1.5.2 EEG Data Source Reconstruction
1.5.3 EEG Data Source Connectivity Analysis
1.5.4 Statistical Filtering: Surrogate EEG Source Connectivity Analysis
1.5.5 Data-Driven Topological Filtering
1.5.6 Graph Diffusion Distance Metric
1.5.7 Quantifying the Contribution of Each Dominant Intrinsic Coupling Mode (DICM)
1.5.8 A Dissimilarity Measure for Dynamical Trajectories Based on the Wald-Wolfowitz (WW) Test
1.5.9 Estimating Time-Delays with Delay Symbolic Transfer Entropy (dSTE)
1.6 Improvements
1.6.1 Improvement of GE-Cost for the MST Group
1.6.2 Improvement of Brain Activity Synchronization Due to MST Intervention Protocol
1.8 Conclusions
References
2: A Sensor-Based Platform for Early-Stage Parkinsonś Disease Monitoring
2.1 Introduction
2.2 he Sensor Perspective
2.3 Sensor Data Acquisition/Processing Unit
2.4 Interface with Peripheral Sensors Platform
2.5 Central Computing Unit and Dashboard
2.6 Conclusions
References
3: Pressure Prediction on Mechanical Ventilation Control Using Bidirectional Long-Short Term Memory Neural Networks
3.1 Introduction
3.2 Background Work
3.2.1 Dynamic Systems
3.2.2 PID Controller
3.2.3 How Mechanical Ventilation Works
3.3 The Dataset
3.3.1 Data Format
3.3.2 Data Preprocessing
3.4 Model
3.4.1 LSTM Nodes
3.4.2 Bidirectional LSTM
3.4.3 Model Architecture and Training
3.5 Results
3.6 Conclusions and Future Work
References
4: Making Pre-screening for Alzheimerś Disease (AD) and Postoperative Delirium Among Post-Acute COVID-19 Syndrome (PACS) a Na...
4.1 Introduction
4.2 PACS Long-Term Cognitive Outcomes
4.3 Postoperative Delirium Long-Term Cognitive Outcomes
4.4 Digital Neuro Signatures of Brain Resilience
References
5: Graph Theory-Based Approach in Brain Connectivity Modeling and Alzheimerś Disease Detection
Acknowledgment
Contents
1: Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimerś Disease Risk
1.1 Introduction
1.2 Patient Recruitment and Data Availability
1.2.1 Disease Categorization
1.2.2 Cognitive Battery
1.2.3 Mnemonic Strategy Training
1.2.4 Demographics and Neuropsychological Measurements
1.3 Interventions
1.4 Neuropsychological Performance
1.5 Methods
1.5.1 EEG Data Acquisition
1.5.2 EEG Data Source Reconstruction
1.5.3 EEG Data Source Connectivity Analysis
1.5.4 Statistical Filtering: Surrogate EEG Source Connectivity Analysis
1.5.5 Data-Driven Topological Filtering
1.5.6 Graph Diffusion Distance Metric
1.5.7 Quantifying the Contribution of Each Dominant Intrinsic Coupling Mode (DICM)
1.5.8 A Dissimilarity Measure for Dynamical Trajectories Based on the Wald-Wolfowitz (WW) Test
1.5.9 Estimating Time-Delays with Delay Symbolic Transfer Entropy (dSTE)
1.6 Improvements
1.6.1 Improvement of GE-Cost for the MST Group
1.6.2 Improvement of Brain Activity Synchronization Due to MST Intervention Protocol
1.8 Conclusions
References
2: A Sensor-Based Platform for Early-Stage Parkinsonś Disease Monitoring
2.1 Introduction
2.2 he Sensor Perspective
2.3 Sensor Data Acquisition/Processing Unit
2.4 Interface with Peripheral Sensors Platform
2.5 Central Computing Unit and Dashboard
2.6 Conclusions
References
3: Pressure Prediction on Mechanical Ventilation Control Using Bidirectional Long-Short Term Memory Neural Networks
3.1 Introduction
3.2 Background Work
3.2.1 Dynamic Systems
3.2.2 PID Controller
3.2.3 How Mechanical Ventilation Works
3.3 The Dataset
3.3.1 Data Format
3.3.2 Data Preprocessing
3.4 Model
3.4.1 LSTM Nodes
3.4.2 Bidirectional LSTM
3.4.3 Model Architecture and Training
3.5 Results
3.6 Conclusions and Future Work
References
4: Making Pre-screening for Alzheimerś Disease (AD) and Postoperative Delirium Among Post-Acute COVID-19 Syndrome (PACS) a Na...
4.1 Introduction
4.2 PACS Long-Term Cognitive Outcomes
4.3 Postoperative Delirium Long-Term Cognitive Outcomes
4.4 Digital Neuro Signatures of Brain Resilience
References
5: Graph Theory-Based Approach in Brain Connectivity Modeling and Alzheimerś Disease Detection