CinC/Physionet PCG/ECG challenge 2016

[breznak]

CinC challenge

https://physionet.org/challenge/2016/

A prestigious challenge/conference with nice data!

:fire: UPDATE: game's still ON! :guitar:

Looking for hackers to help me set someting up, if it's feasible. The there will be whole summer to tune the app.

Blocked by: Add encoders #22

Plan of attack

• [ ] audio
  • [x] for now use wav2vect from Matlab
  • [ ] implement wavEncoder - IN PROGRESS #26
  • [ ] evaluate if functionality of the WAVEncoder (internal scipy) is the same as matlab's
  • [ ] try Cochlea encoder
  • [ ] implement sound encoders for nupic.audio #22
• [ ] training
  • records are Normal/Anomaly/Unknown
  • [ ] aggregate all NORMAL records to a 2 column file (reset, PCG)
    • [x] how radical subsampling? bcs nupic is too slow to process whole dataset: only down to 1000(from 2000),bcs of Sampling Theorem (Fs>=2*F)
  • [ ] commit the training data files (bcs the preprocessing takes long)
  • [ ] train a HTM model + serialize it
  • [ ] try param swarming
• [ ] evaluation
  • [ ] load the model, disable learning
  • [ ] 2 tasks description.py?, OR other way to train/load/eval a model on datasets
  • [ ] compute average anomaly score for all datapoints of a record
  • [ ] implement the anomaly metric in nupic
  • [ ] create a model (for nupic?) that does this classification based on avg. anomaly scores?
  • [ ] threshold to Normal/Anomaly/Unknown
• [ ] submission
  • [ ] modify examples sample2016*
  • [ ] nupic is installed, so setup will just source a virtualenv
  • [ ] each evaluation in next will call matlab (wav2csv), python(writes anomaly scores to CSV), matlab again(loads anomalies and decides classification)
  • this is problematic, better go full-python if possible!
• [ ] improvements:
  • [ ] try bag (multi model) voting
    • [ ] model trained on full normal data
  • [ ] model on FHS parts
  • [ ] model on anomalous data
  • [ ] model pretrained on ECG data from other sources! https://github.com/breznak/nupic.biodat

Working plan to get some validation results ASAP:

• [ ] training data
  • will train only on Normal data and select (FHS) subsequences of it
  • data extracted from Matlab @breznak will do that
• [ ] train HTM model
  • on the provided data
  • just one HTM model (with RDSE? encoder, what best settings? probably no time to swarm)
  • able to serialize the model and load to run on eval. data (learning off)
    • the approach with OPF is not reliably working, can someone post code to do that? (@rhyolight or someone..?)
• [ ] write simple classification function: classify(anScores[])
  • should decide classification from the anomaly scores for the whole sequence/sample
  • can be sth like avg and Normal iff <0.4; UNKNOWN iff [0.4...0.7]; Anomal iff > 0.7; ETA ~10mins
• [ ] score
  • process validation data (@breznak will commit a file)
  • classify & compute score -> submit! :pray: