ECG Monitoring in Wearable Devices by Sparse Models

Because of user movements 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 challengi...

Full description

Saved in:
Bibliographic Details
Published inMachine Learning and Knowledge Discovery in Databases pp. 145 - 160
Main Authors Carrera, Diego, Rossi, Beatrice, Zambon, Daniele, Fragneto, Pasqualina, Boracchi, Giacomo
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Anomalous Morphology
External Host
Heartbeat Model
Pulse Sensor
Wearable Devices
Online AccessGet full text

Cover

More Information
Summary:Because of user movements 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.
ISBN:9783319461304
3319461303
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-46131-1_21