Use of Convolutional Neural Networks in electrocardiogram readings to identify heart diseases and predict heart attacks

I will be creating a machine learning model (convolutional neural network) which will analyze and detect features from electro cardiogram readings to identify and predict heart attacks and diseases.
Owen Kuang
Grade 11

Problem

Currently, heart disease is the leading cause of death in the United States of America, with a heart attack occuring every 40 seconds. In Canada, this problem is also prevalent with 14 deaths attributed to heart disease every hour.

Method

Data Preprocessing: 

- Take the electrocardiogram patient data and convert to Spectogram / frequency domain 

Convolutional Neural Network 

- Define the architechture (Input --> Convolutional --> Pooling --> Convolutional --> Pooling --> fully connected --> fully connected --> classification 

Validation / Training 

- five fold cross validation 

- Accruacy is the average of all of those values 

 

Analysis

The machine learning model was able to achieve a training accuracy of 95% and a validation accuracy of 83%. 

I also analysed the behaviour of the machine learning model at various different learning rates. 

Learning Rate 

Training Accuracy

Validation Accuracy

1 * 10^-3 

82% 

76% 

1 * 10^-4

95% 

83% 

1 * 10^-5 

78% 

80% 

Conclusion

Although my model was not able to perform to the same accuracy as other convolutional neural networks using time frequency analysis, it was able to achieve an accuracy of 83% with limited data. However, my model was able to succeed in obtaining an accuracy similar / replicate what a physician is able to achieve in ECG reeadings. This is because my model accuracy was roughly 80% and the range of physician accuracy ranges from 49 - 92 percent. 

Citations

(these are the sources referenced in my poster however I had many more sources for background research) 

[1] “Heart disease facts,” Centers for Disease Control and Prevention, Heart Disease Facts | cdc.gov.  (accessed Mar. 1, 2024).

[2] P. H. A. of Canada, “Government of Canada,” Canada.ca, Heart Disease in Canada  (accessed Mar. 1, 2024).

[3] Wu, Ziqian, Tianjie Lan, Cuiwei Yang, and Zhenning Nie. 2019. “A Novel Method to Detect Multiple Arrhythmias Based on Time-Frequency Analysis and Convolutional Neural Networks.” IEEE Access: Practical Innovations, Open Solutions 7: 170820–30 (accessed Mar. 1, 2024). 

[4] “ECG (electrocardiogram),” Cancer Research UK, https://www.cancerresearchuk.org/about-cancer/tests-and-scans/ecg#:~:text=You%20might%20get%20your%20results,your%20doctor%20needs%20the%20results  (accessed Mar. 1, 2024). 

 

Acknowledgement

I would like to acknowledge my applied science teacher, Dr. Garcia and my applied science mentor, Dr. Pieper.