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  • ◈ Easy to use toolkit for signal processing and analysis
  • ◈ More of biomedical signal analysis with visualization
  • ◈ Includes basic machine learning models with visualization

Signal Processing Toolkit

Simple and easy to use for signal analysis and predictive analysis

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0.0.9.7 (latest)
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SpKit 0.0.9.7
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  • User Guide
    • 1. Information Theory
    • 2. Signal Filtering
    • 3. Wavelet Analysis
    • 4. Signal Analysis
    • 5. Biomedical Signal Processing
      • 5.1. EEG: Electroencephalography
      • 5.2. ECG: Electrocardiography
      • 5.3. EGM: Electrogram
      • 5.4. MEA: Multi-Electrode Array Processing
    • 6. Machine Learning
    • 7. Statistics/Utilities
    • 8. Data Samples





5. Biomedical Signal Processing¶

  • 5.1. EEG: Electroencephalography
    • 5.1.1. Preprocessing
    • 5.1.2. Working with different files/frameworks
    • 5.1.3. Removing Artifacts
  • 5.2. ECG: Electrocardiography
    • 5.2.1. Filtering (cleaning)
    • 5.2.2. R Peak Detection
  • 5.3. EGM: Electrogram
    • 5.3.1. Filtering
    • 5.3.2. Activation Time
    • 5.3.3. Phase Analysis
    • 5.3.4. Multi-Channel EGMs
  • 5.4. MEA: Multi-Electrode Array Processing
    • 5.4.1. Complete Analysis of a recording
    • 5.4.2. Step-wise Analysis
    • 5.4.3. 1. Read HDF File
    • 5.4.4. 2. Stim Localisation
    • 5.4.5. 3. Alignment of Stim Cycles
    • 5.4.6. 4. Averaging Cycles or Selecting one
    • 5.4.7. 5. Activation Time
    • 5.4.8. 6. Repolarisation Time (optional)
    • 5.4.9. 7. APD (if RT is computed)
    • 5.4.10. 8. Extracting EGM
    • 5.4.11. 9. EGM Feature Extraction
    • 5.4.12. 10. Identifying BAD Channels/electrodes
    • 5.4.13. 11. Creating Feature Matrix
    • 5.4.14. 12. Interpolation
    • 5.4.15. 13. Conduction Velocity
    • 5.4.16. Plots and Figures
    • 5.4.17. Extracting EGM
    • 5.4.18. EGM Processing & Feature Extractions
    • 5.4.19. Conduction and Activation Map
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