Towards Real-Time Heart Health Monitoring in Firefighting Using Convolutional Neural Networks

A machine learning-based heart health monitoring model, named H2M, was developed. 24-hour electrocardiogram (ECG) data from 112 professional firefighters was used to train the proposed model. The model used carefully designed multi-layer convolution neural networks with maximum pooling, dropout, global maximum pooling to effectively learn the indicative ECG characteristics.

The contribution of this work is to provide firefighters on-demand, real-time heart health status to enhance their situational awareness and safety and to help reduce firefighters’ deaths and injuries due to sudden cardiac events.