A Machine Learning Approach Towards the Quality Assessment of ECG Signals Collected Using Wearable Devices for Firefighters

This work focuses on assessing the ECG signal quality of data collected with wearable devices specifically tailored for firefighters using machine learning techniques. Firefighters are at a heightened cardiac risk due to their challenging working conditions, making wearable sensors crucial for ongoing health monitoring. However, environmental factors such as the temperature, radiation, and moisture, significantly impact the performance of these sensors and the quality of the collected data. To address these challenges, this work explored supervised learning to classify ECG signals into acceptable and unacceptable segments using only eight cardiac features. Leveraging on the ScientISST MOVE dataset, which contains biosignals during various daily activities, the model achieved promising results, namely 88% accuracy and an 87% F1 score with just eight ECG features. Besides this, a case study was performed on ECG data gathered from firefighters under real-world conditions to further corroborate the proposed method. Such a validation exercise demonstrated how well the model performs for the assessment of signal quality in such dynamic, high-stress scenarios.