Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals
<italic>Goal</italic>: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. <italic>Methods</italic>: We propose Boosted-SpringDTW, a probabilistic f...
Saved in:
Main Authors: | Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi, Roozbeh Jafari |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Open Journal of Engineering in Medicine and Biology |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9774024/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Smartwatch or Just a Watch? A Validation Study of the Smartwatch KC08 for Measuring Blood Pressure
by: Susana López-Ortiz, et al.
Published: (2025-06-01) -
Physically Meaningful Surrogate Data for COPD
by: Harry J. Davies, et al.
Published: (2024-01-01) -
Follow That Tune – Adaptive Approach to DTW-based Query-by-Humming System
by: Bartłomiej STASIAK
Published: (2015-01-01) -
Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
by: Sicong Huang, et al.
Published: (2024-01-01) -
Experimental Analysis on the Effect of Contact Pressure and Activity Level as Influencing Factors in PPG Sensor Performance
by: Francesco Scardulla, et al.
Published: (2025-07-01)