Improved Neural Network Arrhythmia Classification Through Integrated Data Augmentation
- Resource Type
- Conference
- Authors
- Cayce, Garrett I.; Depoian, Arthur C.; Bailey, Colleen P.; Guturu, Parthasarathy
- Source
- 2022 IEEE MetroCon MetroCon, 2022 IEEE. :1-3 Nov, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Transportation
Heart beat
Arrhythmia
Neural networks
Training data
Machine learning
Electrocardiography
Data models
Data Augmentation
Convolutional Neural Network
Medical Classification
- Language
This work investigates an evolution of verified recent advances to machine learning applied to electrocardiogram (ECG) data. The successful inference of heartbeat arrhythmia has long been a goal yet achieved, the techniques presented advance the worthy endeavor. The mutation of the training data through amplitude and time inversion creates artificial information leading to a more robust and accurate model in comparison to the current state of the art. Over a 5% reduction in accuracy error is reached with the proposed techniques in comparison to that of the base model.