User Guide

See also

User Guide - API

User Guide is in a continues development. It is not a complete guide yet, we are keep updating the contents. For now, please check the API: API Reference, which is the most complete documentation of all the functions and modules.

• 1. Information Theory
  • 1.1. Entropy
    • 1.1.1. Entropy with discreet source
    • 1.1.2. Rényi entropy of order \(\alpha\)
    • 1.1.3. Entropy for real-valued source
    • 1.1.4. Shannon Entropy \(H(X)\)
    • 1.1.5. Mutual Information \(I(X;Y)\)
    • 1.1.6. Joint-Entropy \(H(X;Y)\)
    • 1.1.7. Conditional Entropy \(H(X|Y)\)
    • 1.1.8. Cross Entropy \(H_{xy}(X,Y)\)
    • 1.1.9. Cross Entropy: KL-Divergence \(H_{kl}(X,Y)\)
    • 1.1.10. Approximate Entropy \(H_{ae}(X)\)
    • 1.1.11. Sample Entropy \(H_{se}(X)\)
    • 1.1.12. Spectral Entropy \(H_{f}(X)\)
    • 1.1.13. SVD Entropy \(H_{\Sigma}(X)\)
    • 1.1.14. Permutation Entropy \(H_{\pi}(X)\)
    • 1.1.15. Sample Entropy & Approximate Entropy
    • 1.1.16. Spectral Entropy & Permutation Entropy
  • 1.2. Dispersion and Differential Entropy
    • 1.2.1. Dispersion Entropy \(H_{de}(X)\)
    • 1.2.2. Multi-scale Dispersion Entropy
    • 1.2.3. Dispersion Entropy & Shannon Entropy
    • 1.2.4. Dispersion Entropy & Sample Entropy
    • 1.2.5. Differential Entropy Functions \(H_{\partial}(\cdot)\)
    • 1.2.6. Differential Entropy \(H_{\partial}(X)\)
    • 1.2.7. Self-Conditional Entropy \(H_{\partial}(X_{i+1}|X_{i})\)
    • 1.2.8. Conditional Entropy \(H_{\partial}(X_{i+1}|X_{i},Y_{i})\)
    • 1.2.9. Joint Entropy \(H_{\partial}(X,Y)\)
    • 1.2.10. Self-Mutual Information \(I_{\partial}(X_{i+1}|X_{i})\)
    • 1.2.11. Mutual Information \(I_{\partial}(X,Y)\)
  • 1.3. Applications
    • 1.3.1. Transfer Entropy \(TE_{X \rightarrow Y}\)
    • 1.3.2. Partial Transfer Entropy \(TE_{X \rightarrow Y | Z}\)
    • 1.3.3. Granger Causality \(GC_{X \rightarrow Y}\)
    • 1.3.4. Connectivity Analysis \({X \rightarrow Y}\)
• 2. Signal Filtering
  • 2.1. Drift Removing
    • 2.1.1. IIR Filters of Order 1
    • 2.1.2. Spectral filter - IIR Filters
    • 2.1.3. Savitzky-Golay filter
  • 2.2. Smoothing
    • 2.2.1. Spectral Filters
    • 2.2.2. Savitzky-Golay filter
    • 2.2.3. Gaussian Filter
    • 2.2.4. Mollifier
    • 2.2.5. Wavelet Filtering
  • 2.3. Spectral Filtering
    • 2.3.1. Lowpass filtering
    • 2.3.2. Highpass filtering
    • 2.3.3. Bandpass filtering
    • 2.3.4. Bandreject/bandstop filtering
    • 2.3.5. Custom filtering
  • 2.4. Kernel Filtering
    • 2.4.1. Kernel Filtering
  • 2.5. Filtering Pipeline
    • 2.5.1. Filtering Pipeline
    • 2.5.2. Powerline Interference removal
  • 2.6. Spatial Filtering (3D)
    • 2.6.1. Using Adjacency matrix
    • 2.6.2. Using Proximity (distance)
    • 2.6.3. Using Nearest Neighbours
  • 2.7. Graph Filtering
    • 2.7.1. Using Adjacency matrix
    • 2.7.2. Using Proximity (distance)
    • 2.7.3. Using Nearest Neighbours
• 3. Wavelet Analysis
  • 3.1. Continues Wavelet Transform
    • 3.1.1. Gauss wavelet
    • 3.1.2. Morlet wavelet
    • 3.1.3. Gabor wavelet
    • 3.1.4. Poisson wavelet
    • 3.1.5. Maxican wavelet
    • 3.1.6. Shannon wavelet
  • 3.2. Discreet Wavelet Transform
    • 3.2.1. Wavelet Filtering & Shrinkage
    • 3.2.2. Wavelet Package Analysis
    • 3.2.3. Wavelet decomposed signals
    • 3.2.4. Temporal analysis
• 4. Signal Analysis
  • 4.1. Discrete Fourier Transfer
    • 4.1.1. Analysis
    • 4.1.2. Synthesis
  • 4.2. Short-Time Fourier Transform
    • 4.2.1. Analysis
    • 4.2.2. Synthesis
  • 4.3. Fractional Fourier Transform
    • 4.3.1. Analysis
    • 4.3.2. Synthesis
  • 4.4. Sinusoidal Model
    • 4.4.1. Analysis
    • 4.4.2. Synthesis
  • 4.5. Ramanujan Methods
    • 4.5.1. Period Analysis
  • 4.6. Differentiation
    • 4.6.1. Signal Gradient
  • 4.7. Simplifying a complex signal
    • 4.7.1. Simplifying using Sinasudol Models
    • 4.7.2. Simplifying using DFT
  • 4.8. Signal Temporal Mapping
    • 4.8.1. Linear mapping
    • 4.8.2. Non-linear mapping
• 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
• 6. Machine Learning
  • 6.1. Logistic Regression
    • 6.1.1. Classification
  • 6.2. Naive Bayes
  • 6.3. Decision Trees
    • 6.3.1. Classification Trees
    • 6.3.2. Regression Trees
• 7. Statistics/Utilities
  • 7.1. Statistics
    • 7.1.1. Testing Two Groups
    • 7.1.2. Testing gropus
    • 7.1.3. Testing gropus
  • 7.2. IO Read/Write
    • 7.2.1. Read HDF/EDF file
    • 7.2.2. Read BDF file
    • 7.2.3. Read Surf file
    • 7.2.4. Read VTK file
    • 7.2.5. Write VTK File
  • 7.3. Utilities
• 8. Data Samples
  • 8.1. Real-world-Samples
    • 8.1.1. EEG-Sample
    • 8.1.2. ECG-Sample
    • 8.1.3. Optical Data
    • 8.1.4. PPG: Photoplethysmogram
    • 8.1.5. GSR/EDA: Galvanic Skin Response
  • 8.2. Simulation
    • 8.2.1. 1D- Arbitary signal
    • 8.2.2. 2D-Image Patch
    • 8.2.3. 2D-Data for ML

Under Development - Install

• 1. Installing spkit