Generating Counterfactual Explanations for Electrocardiography Classification with Native Guide

Inhaltsverzeichnis (Table of Contents)

• Introduction
  • Topic and Related Work
  • Research Approach: Design Science
    • Aims and Objectives
    • Research Questions
  • Thesis Outline and Research Methods
• Background
  • Time Series Data
  • Basic Understanding of Artificial Intelligence, Machine Learning, and Classification
  • Convolutional Neural Network (CNN)
  • Explainable Artificial Intelligence (XAI)
  • Counterfactual Explanations
  • ECG Signal Data
  • Openly-accessible ECG Datasets
    • ECG200
    • ECG5000
    • PTB
    • PTB-XL
• Native Guide: A Counterfactual Explanation Technique
  • Reference Method
    • Learn or Load Classifier
    • Class-Activation-Map (CAM)
    • Finding the Native Guide
    • Perturbation
  • Investigation and Observation of the Method
    • Comparison of Classifiers
    • ECG Signal Strength and Wavelength
    • Swapped Subsequence-Length
    • Data Quantity, Length and Variety
    • Different Decision Boundaries
  • Experimental Approaches for Optimization
    • Normalization and Synchronization
    • Swapping Points instead of Subsequences
    • Shifted Decision Boundary
• Evaluation: Expert Interview
  • Goal, Structure and Approach
  • Expert Background
  • Interview Results
    • Usage of ECG
    • ECG Data Quality
    • General Attitude towards Counterfactuals
    • Plausibility of Counterfactuals
    • Improvement Ideas
    • Possible Use-Cases
• Discussion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to investigate different aspects of Native Guide, a generative counterfactual explanation method for time series data classification. It explores the method's functionality, examines its strengths and weaknesses, and proposes optimizations for improving counterfactual explanations, especially in the context of electrocardiogram (ECG) classification.

• Counterfactual Explainability for Time Series Data
• Native Guide Method and its Application to ECG Classification
• Evaluation of Counterfactual Explanations from a User Perspective
• Optimization Techniques for Improving Counterfactual Plausibility
• Potential Use Cases of Counterfactuals in Cardiology

Zusammenfassung der Kapitel (Chapter Summaries)

• Introduction: Introduces the topic of explainable AI in the context of ECG classification. It outlines the research approach, objectives, and questions addressed in the thesis.
• Background: Provides a comprehensive overview of key concepts related to the research, including time series data, machine learning, convolutional neural networks, explainable AI, counterfactual explanations, and ECG signal data. It also presents a summary of existing ECG datasets.
• Native Guide: A Counterfactual Explanation Technique: Presents the Native Guide method, explaining its technical steps for generating counterfactual explanations. It investigates different aspects of the method, such as the influence of various classifiers and data characteristics.
• Evaluation: Expert Interview: Describes the expert interviews conducted with cardiologists to evaluate the plausibility of counterfactuals generated by Native Guide. It presents insights gained from the experts regarding the practical use of ECG, their opinions on the method, and potential use cases.
• Discussion: Discusses the limitations and challenges of Native Guide, highlighting the need for further research and improvement in areas like data quality, synchronization, and perturbation methods.

Schlüsselwörter (Keywords)

This work focuses on explainable AI, counterfactual explanations, time series data classification, ECG signal data, Native Guide, and expert evaluation. It explores the potential of counterfactuals to enhance the understanding and trust in AI-driven ECG classification systems.