Morphological Classification of Heartbeats in Compressed ECG

The number of connected medical devices that are able to acquire, analyze, or transmit health data is continuously increasing. This has allowed the rise of Internet of Medical Things (IoMT). IoMT-systems often need to process a massive amount of data. On the one hand, the colossal amount of data available allows the adoption of machine learning techniques to provide automatic diagnosis. On the other hand, it represents a problem in terms of data storage, data transmission, computational cost, and power consumption. To mitigate such problems, modern IoMT systems are adopting machine learning techniques with compressed sensing methods. Following this line of research, we propose a novel heartbeat morphology classifier, called RENEE, that works on compressed ECG signals. The ECG signal compression is realized by means of 1-bit quantization. We used several machine learning techniques to classify the heartbeats from compressed ECG signals. The obtained results demonstrate that RENEE exhibits comparable results with respect to state-of-the-art methods that achieve the same goal on uncompressed ECG signals.