Code description

Code necassary for testing different feature approaches used with HD computing for epileptioc seizure detection. It compares approaches in three aspects:

1. Prediction performance
2. Memory needed to store all the HD vectors
3. Computational complexity (time needed for execution)

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Python files description

Script_runAllApproaches.py Script that based on parameters set runs training and testing of specific HD features model

• it is possible to choose between two different databases and which patients to use
• it can perform personalized or generalized training
  • personalized is for each subject performing leave one seizure out and training on the rest of seizures
  • generalized requires to define CViterations_testSubj variable - which determines CV iterations and which subjects are in test set (all others are in training)
• outputs prediction labels for each input file with saved original predictions, and predictions after two sets of labels postprocessing (smoothing) and also true labels
  • by analysing and plotting this labes many things can be analized
• plots performances in dependence of performance measures and amount of postprocessing

Script_compareApproaches.py Script that:

• meausres number of seizure per subjects for databases
• compares different approaches in terms of performance and plots comparison
• meausures time for calculation for different approaches
• plots time and memory requirements for different approaches

HdfunctionsLib.py

• library with different functions on HD vectors, uses torch library

VariousFunctionsLib.py

• library including various functions for HD project but not necessarily related to HD vectors