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



Jiajia Li, Christopher U. Brown, Dillon Dzikowicz, Mary Carey, Wai Cheong Tam, Michael Xuelin Huang


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. H2M was benchmarked against three existing state-of-the-art machine learning models. Results showed the proposed model was robust and had an overall accuracy of approximately 94.3 %. A parametric study was conducted to demonstrate the effectiveness of key model components. An additional data study was also carried out and it was shown that using non-firefighters' ECG data to train the H2M model led to a substantial error of 40 %. 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.
Fire Safety Journal


Abnormal beat detection, machine learning, on-duty ECG signals, sudden cardiac death prevention, smart firefighting


Li, J. , Brown, C. , Dzikowicz, D. , Carey, M. , Tam, W. and Huang, M. (2023), Towards Real-Time Heart Health Monitoring in Firefighting Using Convolutional Neural Networks, Fire Safety Journal, [online], (Accessed July 21, 2024)


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Created June 28, 2023, Updated July 2, 2023