Next Article in Journal
Disposable Amperometric Immunosensor for the Determination of Human P53 Protein in Cell Lysates Using Magnetic Micro-Carriers
Next Article in Special Issue
Rapid Detection of Bacillus anthracis Spores Using Immunomagnetic Separation and Amperometry
Previous Article in Journal
Wrist Pulse Rate Monitor Using Self-Injection-Locked Radar Technology
Article Menu

Export Article

Open AccessArticle
Biosensors 2016, 6(4), 55; doi:10.3390/bios6040055

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

1
Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, BC V6Z 2K5, Canada
2
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Academic Editor: Chung-Chiun Liu
Received: 16 August 2016 / Revised: 20 October 2016 / Accepted: 25 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Point-of-Care Diagnostics)
View Full-Text   |   Download PDF [712 KB, uploaded 23 November 2016]   |  

Abstract

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) W 2 ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions. View Full-Text
Keywords: trend-following; lagging indicator; crossover; quasi-periodic signals; eventogram transform; mobile health; global health; internet-of-things devices; wearable sensors; point-of-care devices trend-following; lagging indicator; crossover; quasi-periodic signals; eventogram transform; mobile health; global health; internet-of-things devices; wearable sensors; point-of-care devices
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Elgendi, M. TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach. Biosensors 2016, 6, 55.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Biosensors EISSN 2079-6374 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top