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Enhancing Biomedical Sensing in Ambulances using Neural Networks and Internet of Things

Ambulances may be installed with a wide range of biomedical equipment that aid in emergency treatment and patient care. Electrocardiography is an important diagnostic process to detect cardiac abnormalities. Due to the presence of many noise sources, the electrocardiogram reading must be denoised and presented in a clear waveform. While considering its usage in ambulances, vehicular motion-induced artifacts pose a serious issue by contaminating the signal. The proposed work presents a possibility of denoising the ECG signals, leveraging technologies such as deep learning and recurrent neural networks. The approach is architected using a local computing node that retrieves a trained model from the cloud and does the signal processing locally. This setup can be deployed as a standalone device in an ambulance to improve the quality and reliability of recorded signals.