This is a UCI Computer Science course project which aims to build explainable AI models to classify pathological events in electrocardiograms (ECG).
- Python 3.6
- Keras==2.2.4
- tensorflow-gpu==1.12.0
- Setup Python virtual environment
virtualenv ENV, where ENV is a directory to place the new virtual environment - Install requirments
pip install -r requirements.txt
[DEFAULT]
loglevel = DEBUG
logdir = log
experiments_dir = experiments
[nsrdb]
; Change this path to the folder which you store nsrdb
dataset_path = C:\.....\Dataset\nsrdb
[mitdb]
; Change this path to the folder which you store mitdb
dataset_path = C:\.....\Dataset\mitdb
[preprocessing]
NSR_DB_TAG = 0
MIT_DB_TAG = 1
; Take 2 records from MIT_DB/NSR_DB to build dev set
dev_record_each = 2
; Take 5 records from MIT_DB/NSR_DB to build test set
test_record_each = 5
batch_size = 512
sequence_length = 1300
overlap_percent = 5
augmentation = True
random_time_scale_percent = 20
dilation_factor = 1
awgn_rms_percent = 2
[RNN-train]
rnn_output_features = 32
l2_regularization = 0
dropout = 0.2
initial_lr = 0.001
initial_weights = None
model_output = trained_model/model.h5
tensorboard_dir = tensorboard
epochs = 100
patientce_reduce_lr = 2
early_stop = True
verbosity = 1
[RNN-test]
test_set = experiments/7Xa9GShsje_0122_112200/test.pickle
model_json = experiments/7Xa9GShsje_0122_112200/model.json
weights = experiments/7Xa9GShsje_0122_112200/final_weights.h5
batch_size = 32The authors of this software package would like to thank following authors and their efforts in setting up long-term S-T database.
Franc Jager, Alessandro Taddei, George B. Moody, Michele Emdin, Gorazd Antolic, Roman Dorn, Ales Smrdel, Carlo Marchesi, and Roger G. Mark. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Medical & Biological Engineering & Computing 41(2):172-183 (2003).
And Physionet for hosting this dataset:
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 http://circ.ahajournals.org/content/101/23/e215.full; 2000 (June 13).
Copyright 2019 Tao-Yi Lee, Kenneth Stewart and Saehanseul Yi
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