1. Detecting Myocardial Infarctions (Heart Attack) using Neural Network
Meital Lian Rannon, Fall 2018

2. Executive Summary Terminology
MI - Myocardial Infarctions (Heart Attack)
ECG – Electrocardiogram
Facts
Every year ~735K Americans experience MI.
One major mean of MI diagnosis is the ECG.

3. Executive Summary The project’s tasks: Get 12 channels of the ECG
Handle the Data
Train and test 12 NN
Use committee classifier
Combine the results
The network was compared to a CNN
The NN met the expectations when using channel i
Did not perform as well when using all 12 channels

4. Approaches: 01
Get ECG files from PhysioNet
Database: PTB diagnostic ECG database from PhysioNet.
Network: 2 layers, 10 neurons at the hidden layer, 1 at the output (binary).
Platform: Laptop
Get ECG 02
Cut ECG to frames- two methods:
1. Arbitrarily
2. Peak-to-peak .
Framing 03 06
Confusion matrix
Downsample
Downsample to 192 samples
Database contains 207 ECG files: 80 files of healthy patients and 127 filed of MI patients.
Combine the confusion matrices of all 12 networks
Committee classifier
Train and test NN
Create final labels using majority voting
NN was created by Matlab’s NN train tool 05 04

5. Youden’s J-statistic =
Results:
Statistics:
Sensitivity = 𝑇𝑃 𝑇𝑃+𝐹𝑁
Specificity = 𝑇𝑁 𝑇𝑁+𝐹𝑃
Youden’s J-statistic =
𝑆𝑒𝑛𝑠+𝑆𝑝𝑒𝑐−1
12 ECG channels: Channel I only:
J-stat.
Sens.
Spec.
Fully connected shallow NN 1st framing method
0.401
0.733
0.669
Fully connected shallow NN 2nd framing method
0.661
0.847
0.814
Fully convolutional architecture
0.827
0.933
0.897
J-stat.
Sens.
Spec.
Fully connected shallow NN 2nd framing method
0.715
0.767
0.947
Fully convolutional architecture
0.703
0.875
0.828

6. Discussion: Main conclusions:
The Peak-to-Peak framing method is preferred.
There are differences between the performance of the NN for each channel.
Main conclusions:
For shallow NN - framing method that doesn’t change the ECG signal position over the feature vector.
Only channels with high performance for classification.
More complex NN to achieve better results.

7. Thank you!