Difference between revisions of "Kaggle"
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− | + | Noisebridge Kaggle team!!!!!!!!!!!!!!!!!!!!!!!!!!!!! | |
+ | We use this wiki to archive information. We use this google group to communicate with each other: | ||
+ | https://groups.google.com/forum/#!forum/nbkaggle | ||
+ | |||
+ | The Noisebridge neuro hacking dream team has a lot of useful stuff on their reading list: | ||
+ | https://noisebridge.net/wiki/DreamTeam/Reading#Seizure_Detection | ||
+ | |||
+ | Here is a link to the competition: | ||
https://www.kaggle.com/c/melbourne-university-seizure-prediction/data | https://www.kaggle.com/c/melbourne-university-seizure-prediction/data | ||
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Ali Shoeb, John Guttag Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139 | Ali Shoeb, John Guttag Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139 | ||
− | + | https://drive.google.com/open?id=0ByjOj5sb0Oj_SGduRjduNVdfX1k | |
+ | |||
+ | EEG-based neonatal seizure detection with Support Vector Machines | ||
+ | |||
+ | A. Temko a,*, E. Thomas a, W. Marnane a,b, G. Lightbody a,b, G. Boylan a,c | ||
+ | a Neonatal Brain Research Group, University College Cork, Ireland | ||
+ | b Department of Electrical and Electronic Engineering, University College Cork, Ireland c Department of Paediatrics and Child Health, University College Cork, Ireland | ||
+ | |||
+ | https://drive.google.com/open?id=0ByjOj5sb0Oj_UzVxdGpkcTNPV0E | ||
+ | |||
+ | |||
+ | = Code = | ||
+ | |||
+ | https://github.com/cowlicks/kaggle-seizure-prediction | ||
+ | |||
+ | == reading the data == | ||
+ | Here is a python function to load a file from the matplotlib file format. | ||
+ | |||
+ | <nowiki> | ||
+ | |||
+ | from scipy.io import loadmat | ||
+ | |||
+ | def load(fn): | ||
+ | return loadmat(fn, struct_as_record=False)['dataStruct'][0, 0].data | ||
+ | |||
+ | </nowiki> | ||
+ | |||
+ | <nowiki> | ||
+ | import pandas as pd | ||
+ | from scipy.io import loadmat | ||
+ | |||
+ | def mat_to_pandas(path): | ||
+ | mat = loadmat(path) | ||
+ | names = mat['dataStruct'].dtype.names | ||
+ | ndata = {n: mat['dataStruct'][n][0, 0] for n in names} | ||
+ | sequence = -1 | ||
+ | if 'sequence' in names: | ||
+ | sequence = mat['dataStruct']['sequence'] | ||
+ | return pd.DataFrame(ndata['data'], columns=ndata['channelIndices'][0]), sequence | ||
+ | |||
+ | </nowiki> | ||
+ | via https://www.kaggle.com/zfturbo/melbourne-university-seizure-prediction/seizure-boost-0-6-lb/code |
Latest revision as of 00:43, 22 September 2016
Noisebridge Kaggle team!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
We use this wiki to archive information. We use this google group to communicate with each other: https://groups.google.com/forum/#!forum/nbkaggle
The Noisebridge neuro hacking dream team has a lot of useful stuff on their reading list: https://noisebridge.net/wiki/DreamTeam/Reading#Seizure_Detection
Here is a link to the competition: https://www.kaggle.com/c/melbourne-university-seizure-prediction/data
Papers[edit]
Random papers from google searching "machine learning seizure detection"
Application of Machine Learning To Epileptic Seizure Detection
Ali Shoeb, John Guttag Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139
https://drive.google.com/open?id=0ByjOj5sb0Oj_SGduRjduNVdfX1k
EEG-based neonatal seizure detection with Support Vector Machines
A. Temko a,*, E. Thomas a, W. Marnane a,b, G. Lightbody a,b, G. Boylan a,c a Neonatal Brain Research Group, University College Cork, Ireland b Department of Electrical and Electronic Engineering, University College Cork, Ireland c Department of Paediatrics and Child Health, University College Cork, Ireland
https://drive.google.com/open?id=0ByjOj5sb0Oj_UzVxdGpkcTNPV0E
Code[edit]
https://github.com/cowlicks/kaggle-seizure-prediction
reading the data[edit]
Here is a python function to load a file from the matplotlib file format.
from scipy.io import loadmat def load(fn): return loadmat(fn, struct_as_record=False)['dataStruct'][0, 0].data
import pandas as pd from scipy.io import loadmat def mat_to_pandas(path): mat = loadmat(path) names = mat['dataStruct'].dtype.names ndata = {n: mat['dataStruct'][n][0, 0] for n in names} sequence = -1 if 'sequence' in names: sequence = mat['dataStruct']['sequence'] return pd.DataFrame(ndata['data'], columns=ndata['channelIndices'][0]), sequence
via https://www.kaggle.com/zfturbo/melbourne-university-seizure-prediction/seizure-boost-0-6-lb/code