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Contents

Master

Thesis Structure

  • Introduction
  • Background
    • The Human Heart
    • Deep Learning
    • Time-Series Signals
    • Classifiers And Regressors
    • Signal Processing Theory
    • Available Data
  • Experiment 1: Domain Analysis
    • Idea
    • Data preparation
      • Time feature independent data set
      • Time feature dependent data set
    • Implementation
    • Results
  • Experiment 2: Classifying Cardiac Heart Functions
    • Idea
    • Preparations
    • Implementation
    • Results
  • Experiment 3: Image Classification
    • Idea
    • Preparations
    • Implementation
    • Results
  • Relation To Previous Research
  • Future Work
  • Conclusion

Questions

Is it possible to use motion data to predict the condition of the human heart? If so, can Deep Learning be used, and is it effective?

Do we need an invasive method for classification (accelerometer), or is it sufficient with a non-invasive method (ECG)?

Experiments

Experiment 1

Idea

Given an input signal represented in a specific domain, an output signal represented in another domain, and a neural network that transforms the input signal to the output signal. The more features the input-signal contains than the output-signal, the better the transformation should be if both signals represent the same real-world features.

Implementation

One of the first regressors going form accelerometer data to ECG data. This dense regressor is predicting an ECG signal when provided an ACC signal. The data is re-sampled to 250 Hz to reduce training time: 450px

Experiment 2

Experiment 3

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