CinC/Physionet PCG/ECG challenge 2016

[breznak]

CinC challenge

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

A prestigious challenge/conference with nice data!

🔥 UPDATE: game's still ON! 🎸

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
  • for now use wav2vect from Matlab
  • implement wavEncoder - IN PROGRESS Wav encoder #26
  • evaluate if functionality of the WAVEncoder (internal scipy) is the same as matlab's
  • try Cochlea encoder
  • implement sound encoders for nupic.audio Create sound encoders #22
• training
  • records are Normal/Anomaly/Unknown
  • aggregate all NORMAL records to a 2 column file (reset, PCG)
    • 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! 🙏