ECG monitoring in wearable devices by sparse models

Because of usermovements and activities, heartbeats recorded from wearable devices typically feature a large degree of variability in their morphology. Learning problems, which in ECG monitoring often involve learning a user-specific model to describe the heartbeat morphology, become more challenging. Our study, conducted on ECG tracings acquired from the Pulse Sensor-a wearable device from our industrial partner-shows that dictionaries yielding sparse representations can successfully model heartbeats acquired in typical wearable-device settings. In particular, we show that sparse representations allow to effectively detect heartbeats having an anomalous morphology. Remarkably, the whole ECG monitoring can be executed online on the device, and the dictionary can be conveniently reconfigured at each device positioning, possibly relying on an external host.