1. ECG data classification with deep learning tools
Zhangyuan Wang

2. Motivation ECG data classification to assist health monitoring.
E.g. in emergency room
Challenge for current algorithm
High false alarm rate
Cannot tackle noisy data

3. Dataset MIT-BIH Arrhythmia Database 44 patients in total
30 mins of ECG data sampled at 360Hz for each patient

4. Dataset Input: Label for beat
Extract 200 points around the peak of each beat
Label for beat
following AAMI to 5 labels: N, S, V, F, Q

5. Dataset Acquire data WFDB App Toolbox Matlab version
Store2hdf5 from caffe/matlab
Preprocessing: median filter…

6. Method Run CNN on raw data Caffe Windows 10, GTX 765M CUDA 7.5
Visual Studio 2013

7. Method CNN structure Adopted from Mnist_demo_LeNet.prototxt
2*(conv+pooling+ReLu)+ip+ip+softmax
base_lr: 0.01momentum: 0.9
lr_policy: "inv“ gamma: power: 0.75

8. Method Train: augment data Test: report within class accuracy
Use full training set vs part of training set
8/10 of the N type
Add noise to abnormal type
Test: report within class accuracy
Python wrapper
Native C code
Matlab wrapper
HDF5Output layer
Modify Caffe code

9. Modify caffe code

10. Result
Overall accuracy of 92%
Baseline 88%

11. Contribution Setup caffe on windows
Modify code to output probability of each sample
Prove the effectiveness of CNN

12. To Do
Tune the network