Next Article in Journal
Hyper-Uricemia and Gouty Access in the Adult Population of the Southeast of Gabon: Biochemical Aspects
Next Article in Special Issue
Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms
Previous Article in Journal
Do Nonalcoholic Fatty Liver Disease and Fetuin-A Play Different Roles in Symptomatic Coronary Artery Disease and Peripheral Arterial Disease?
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessFeature PaperShort Note
Diseases 2018, 6(1), 18; https://doi.org/10.3390/diseases6010018

Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis

1
Department of Obstetrics & Gynecology, University of British Columbia and BC Children’s & Women’s Hospital, Vancouver, BC V6H 3N1, Canada
2
School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Received: 10 January 2018 / Revised: 18 February 2018 / Accepted: 23 February 2018 / Published: 24 February 2018
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
Full-Text   |   PDF [164 KB, uploaded 6 March 2018]

Abstract

In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal collection is quite simple and only requires the use of inexpensive sensors, an active Internet connection, and a mobile device that acts as the medium between the sensors and the Internet (e.g., a mobile phone or laptop). Despite the ease and convenience of remote ECG data collection and transmission, the amount of time and energy required for the related remote computational processes remains a major limitation. This short note discusses a biosignal approach that uses fewer biomedical data for screening and diagnosis that is, compared to current data collection methods, equally, if not more, efficient. View Full-Text
Keywords: signal processing; global health; digital health; mobile health; big data; algorithm design; bit-rate reduction signal processing; global health; digital health; mobile health; big data; algorithm design; bit-rate reduction
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

Share & Cite This Article

MDPI and ACS Style

Elgendi, M. Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis. Diseases 2018, 6, 18.

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]
Diseases EISSN 2079-9721 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top