@inproceedings{ecdabb22e33247e1815a3d0b5a5157b3,
title = "On the Design of a Physiological Signal Feature Extraction and Segmentation Digital Subsystem",
abstract = "Real-time and compact algorithms are desired for wearable physiologic monitoring devices. We present a QRS waveform detection algorithm, inspired by the Stockwell time-frequency transform and targeted to embedded architectures. The proposed QRS detector uses a complex filter for feature extraction and efficient estimations to achieve minimal computations and limited digital hardware resources. The QRS detector algorithm is demonstrated to reduce the computational cost and maintain accuracy.",
keywords = "Electrocardiogram, Photoplethysmography, QRS, Wearable",
author = "Felton, {Christopher L.} and Gilbert, {Barry K.} and Holmes, {David R.} and Haider, {Clifton R.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ACSSC.2018.8645139",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "208--212",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
}