@inproceedings{f0117dafa1044da380c71452248bf302,
title = "Stockwell Transform Detector for Photoplethysmography Signal Segmentation",
abstract = "Real-time embedded analysis of physiologic waveforms is critical to predict impending pathophysiology. While electrocardiogram (ECG) data is often analyzed to assess cardiovascular disease, there is recent evidence that photoplethysmography (PPG) can track blood loss and thereby alert to hypovolemia. In this work we present a Stockwell transform inspired filter bank to segment PPG waveforms. The Stockwell transform allows for computationally efficient frequency analysis. The proposed Stockwell filter bank utilizes a sparse time-frequency spectrum and is coupled to the Shannon energy envelope to extract PPG peaks. Finally, we demonstrate that the described method is tolerant to the presence of additive Gaussian noise.",
keywords = "Photoplethysmography, Stockwell transform, Wearable devices",
author = "Marks, {Victoria S.} and Felton, {Christopher L.} and Techentin, {Robert W.} and Gilbert, {Barry K.} and Convertino, {Victor A.} and Joyner, {Michael J.} and Curry, {Timothy B.} 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 = "2019",
month = feb,
day = "19",
doi = "10.1109/ACSSC.2018.8645540",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1239--1243",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
}