Details

Subject(s)

• Biomedical engineering [Browse]
• Signal processing [Browse]
• Medical informatics [Browse]

Related name

• Magjarević, Ratko [Browse]
• Petroiu, Gladiola Gabriela [Browse]

Series

• IFMBE Proceedings, 110 [More in this series]
• IFMBE Proceedings, 1433-9277 ; 110 [More in this series]

Summary note

This book gathers the proceedings of the 11th International Conference on E-Health and Bioengineering, EHB2023, held in hybrid form on November 9–10, 2023, in/from Bucharest, Romania. This second volume of a 3-volume set reports on methods for and results from health technology assessment processes, on advances in biosignal processing, medical imaging, informatics and big data in medicine, and current knowledge concerning the design and evaluation of medical devices. It addresses a broad audience of researchers and professionals working at the interface between medicine, informatics, bioengineering, and electrical and mechanical engineering.

Contents

• Intro
• Preface
• Organization
• Contents
• Health Technology Assessment and Economic Issues in Medical Devices
• Value Proposition Concept for a Medical Device - Pilot Study STERLINK (Plasma Sterilizer)
• 1 Introduction
• 2 Database and Method
• 2.1 Clinical Value
• 2.2 Economic Value
• 2.3 User Value
• 3 Results
• 3.1 Clinical Value
• 3.2 Economic Value
• 3.3 User Value
• 4 Conclusions and Discussion
• References
• International Comparison of Cost of Medical Interventions in European Union: A Model Study for Cataract Surgery
• 2 Data and Methods
• 4 Discussion and Conclusions
• Cost and Effects of Treatment of Biceps Long Head Tendon Injury
• 2 Methods
• Health Technology Assessment: Current State of Access to Medical Device Outcomes
• 1.1 Medical Devices Specificities Affecting HTA Methodology
• 2 Database and Methods
• 2.1 State of the Art
• 3.1 Outcomes (i.e. Addressing the Benefits of the MD)
• 3.2 Other Specificities of Medical Device Evaluation
• 4 Conclusions
• Early Stage Health Technology Assessment: Still Unknown Helper of Medical Device Manufacturers
• 3.1 HTA and eHTA
• 3.2 Market Analysis
• 3.3 Economic Evaluation
• 3.4 Stakeholder Analysis
• 3.5 Effects on the Pace of Medical Device Development
• Big Data and Models for Better Health
• Identifying Hidden Patterns from Health Administrative Claims by Means of "HAC2Vec" Embedding
• 2.1 HAC Database
• 2.2 HAC2Vec Method
• 2.3 Machine Learning Modeling
• 3.1 Overall Embedding Space.
• 3.2 Supervised Models to Validate Obtained HAC Embedding
• 4 Conclusion
• Tracing Real-World Patient Pathway by Harnessing Healthcare Administrative Claims
• 2 Practical Framework
• 2.1 Step I: Raw Population
• 2.2 Step II: Dataset Enrichment and Enhancement
• 2.3 Step III: Definition of Index Date
• 2.4 Step IV - Population Narrowing
• 2.5 Step V: Clinical Events and Pathway Milestones
• 2.6 Step VI: Pathway Framework and Analytical Cohort
• 3 Discussion and Conclusion
• Nation-Wide Benchmarking of Healthcare Indicators: From Inferential Statistics to Dashboard Cards
• 2.1 Statistical Methodology for the Development of Quality Performance Indicators
• 2.2 Definition of Basic Terms
• 2.3 Methodology for Calculating Standardized Rh Mortality
• 2.4 Methodology for Developing a Logistic Regression Model for Estimating the Expected Mortality Rate Eh
• 2.5 Methodology for Calculating the 95% Confidence Interval for the Standardized Mortality Rate
• 3.1 Comparison of Individual Providers Based on Stratification
• Cost-Effectiveness Analysis of Multiple Sclerosis Treatment Approaches
• 2.1 Evaluation Perspective
• 2.2 Definition and Selection of the Target Population
• 2.3 Time Horizon
• 2.4 Description and Selection of Compared Interventions
• 2.5 Costs
• 2.6 Effects
• 2.7 Cost-Effectiveness Analysis
• 2.8 Sensitivity Analysis
• Legal and Procedural Health Data Anonymization Framework of the Faculty of Biomedical Engineering, Czech Technical University
• 2 Procedural Data Anonymization Workflow
• 3 Statistical Data Anonymization Workflow.
• 3.1 Step 1: Removal of Direct Identifiers
• 3.2 Step 2: Defining Quasi-Identifiers
• 3.3 Step 3: Determining the Risk of Disclosure
• 3.4 Step 4: Applying Methods to Reduce the Disclosure Risk
• 3.5 Step 5: Data Loss and Disclosure Risk Analysis
• A Burden of Disease Model for Assessing the Economic and Outcomes Impact of Diseases Associated with HPV Infection
• 2.1 Model Description
• 2.2 A Simulated Cohort of Patients
• 2.3 Incidence of Simulated Diseases
• 2.4 Outcomes
• 3.1 Female Model
• 3.2 Male Model
• Biomedical Signal Processing
• Mean Membrane Potential Estimation for Neural Mass Models in EEG Recordings Using a Linear State Observer
• 2 Materials and Methods
• 2.1 Convolution-Based Neural Mass Model
• 2.2 Mathematical Model of the Cortical Column
• 2.3 Observer Design
• Gait Analysis of Electromyographic Spectral Differences in Stroke Survivors and Healthy Controls
• 2.1 Participants
• 2.2 Experimental Protocol
• 2.3 Methodology
• 3.1 Spectrogram Computation
• 3.2 Frequency Domain Analysis
• 4 Discussion
• 4.1 Limitations and Future Work
• 5 Conclusion
• Cardiac Arrhythmia Identification Using Feature Selection and Rule-Based Classifiers
• 2 Dataset and Methods
• 2.1 Dataset
• 2.2 Performance Measures
• 2.3 Classifiers
• 2.4 Feature Selection
• 3 Experimental Design and Results
• 3.1 Experimental Design
• 3.2 Results
• Automatic Detection of High-Quality Fibrillatory Waves Segments from Atrial Fibrillation Electrocardiographic Recordings
• 2.1 Study Population.
• 2.2 Acquisition and Preprocessing of ECG Signals
• 2.3 Optimization of f-Waves Extraction
• 2.4 Performance Assessment
• 2.5 Performance Assessment
• Murmur Separation and Classification from Heart Sound
• 2 Methodology
• 2.1 Separation of Heart Sound and Murmur
• 2.2 Classification of Murmur
• 3 Experiment and Results
• Bio-Signal Based Detection of Hyperarousal as Onset of Post-Traumatic Stress Disorder (PTSD) Episodes
• 2.1 Datasets
• 2.2 Heart Rate Variability (HRV) Data Analysis
• 2.3 Feature Selection and Model Development
• 2.4 Model Performance Metrics
• Heart Rate Variability Machine Learning Models to Facilitate Elevated Blood Pressure Detection
• 2.1 HRV Calculation
• 2.2 Statistical Analysis
• Improved Hypertension Detection Models Utilizing Pulse Rate Variability and Asymmetry
• 2.1 Database and Main Analysis
• 5 Conclusions
• Application of Meta Learning in Quality Assessment of Wearable Electrocardiogram Recordings
• 2 Databases
• 3 Methods
• 3.1 Feature Extraction
• 3.2 Meta-features
• 3.3 Experimental Procedure and Performance Assessment
• 4 Results
• 5 Discussion
• 6 Conclusions
• Heart Rate Variability Analysis on Forcecardiography Signals: A Preliminary Study
• 2.1 Signal Acquisition and Pre-Processing
• 2.2 Inter-Beat Intervals Estimation
• 2.3 Heart Rate Variability Analysis
• 2.4 Statistical Analyses
• 3 Results.
• A Novel Approach to Recognize Valvular Heart Diseases Based on Morphological Similarity of Heartbeats in Seismocardiography Signals
• 2.1 Databases
• 2.2 Signal Processing and Analysis
• Accurate ECG-Free Heartbeats Localization in Long-Lasting SCG Recordings
• 2.1 Database
• 2.2 Signal Pre-processing
• 2.3 Heartbeats Localization and Inter-Beat Intervals Estimation
• 2.4 Statistical Analyses on Inter-Beat Intervals
• Application of Deep Learning to Electrocardiography for Hypertension Detection
• 2.1 Recurrence Plots
• 2.2 Inception-Resnet-V2
• 2.3 Statistical Analysis
• Harnessing Photoplethysmography and Deep Learning in Continuous Blood Pressure Monitoring for Early Hypertension Detection
• 2.1 PPG Preprocessing
• 2.2 Recurrence Plots
• 2.3 Inception-Resnet-V2
• 2.4 Statistical Analysis
• Comparison of Supra and Infrahyoid Muscle Activity in Healthy and Dysphagic Elderly Populations
• 2.1 Database and Experiment Design
• 2.2 Parametrization of the Segmented Signals
• 3 Results and Discussion
• Evaluation of Mental State Based on EEG Signals Using Machine Learning Algorithm
• 2.2 Data Preprocessing
• 3.1 MLP Training on the Depression Rest Database
• 3.2 MLP Training on the MDD vs Control Database.
• 3.3 MLP Training on the Best Features from the Databases Combined.

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ISBN

3-031-62520-X

Doi

• 10.1007/978-3-031-62520-6

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