ECG data classification with deep learning tools Zhangyuan

ECG data classification with deep learning tools Zhangyuan Wang

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

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

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

Dataset • Acquire data • WFDB App Toolbox Matlab version • Store 2 hdf 5 from caffe/matlab • Preprocessing: median filter…

Method • Run CNN on raw data • Caffe • Windows 10, GTX 765 M • CUDA 7. 5 • Visual Studio 2013

Method • CNN structure • • Adopted from Mnist_demo_Le. Net. prototxt 2*(conv+pooling+Re. Lu)+ip+ip+softmax base_lr: 0. 01 momentum: 0. 9 lr_policy: "inv“ gamma: 0. 0001 power: 0. 75

Method • Train: augment data • 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 HDF 5 Output layer Modify Caffe code

Modify caffe code

Result • Overall accuracy of 92% • Baseline 88%

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

To Do • Tune the network