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
Open AccessArticle

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
Biosensors 2016, 6(4), 55; https://doi.org/10.3390/bios6040055
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)
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
Show Figures

Figure 1

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.

Article Access Map by Country/Region

1
Back to TopTop