title: Diagnostic grade wireless ECG monitoring.
abstract: In remote monitoring of Electrocardiogram (ECG), it is very important to ensure that the diagnostic integrity of signals is not compromised by sensing artifacts and channel errors. It is also important for the sensors to be extremely power efficient to enable wearable form factors and long battery life. We present an application of Compressive Sensing (CS) as an error mitigation scheme at the application layer for wearable, wireless sensors in diagnostic grade remote monitoring of ECG. In our previous work, we described an approach to mitigate errors due to packet losses by projecting ECG data to a random space and recovering a faithful representation using sparse reconstruction methods. Our contributions in this work are twofold. First, we present an efficient hardware implementation of random projection at the sensor. Second, we validate the diagnostic integrity of the reconstructed ECG after packet loss mitigation. We validate our approach on MIT and AHA databases comprising more than 250,000 normal and abnormal beats using EC57 protocols adopted by the Food and Drug Administration (FDA). We show that sensitivity and positive predictivity of a state-of-the-art ECG arrhythmia classifier is essentially invariant under CS based packet loss mitigation for both normal and abnormal beats even at high packet loss rates. In contrast, the performance degrades significantly in the absence of any error mitigation scheme, particularly for abnormal beats such as Ventricular Ectopic Beats (VEB).
authors.affiliation: Qualcomm Research Center, 5775 Morehouse Dr, San Diego, CA 92121, USA. hgarudad@qualcomm.com
authors.forename: Harinath
authors.initials: H
authors.lastname: Garudadri
authors.forename: Yuejie
authors.initials: Y
authors.lastname: Chi
authors.forename: Steve
authors.initials: S
authors.lastname: Baker
authors.forename: Somdeb
authors.initials: S
authors.lastname: Majumdar
authors.forename: Pawan K
authors.initials: PK
authors.lastname: Baheti
authors.forename: Dan
authors.initials: D
authors.lastname: Ballard
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D016477:Artifacts; D015716:Electrocardiography, Ambulatory / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D017216:Telemedicine / Q000295:instrumentation*; D013686:Telemetry / Q000295:instrumentation*