Online peak detection in photoplethysmogram signals using sequential learning algorithm

B. N. Sumukha, R. Chandan Kumar, Skanda S. Bharadwaj, Koshy George

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Photoplethysmogram signals are becoming increasingly important for the detection of abnormalities in patients. Peak detection plays a significant role in diagnosis and monitoring using PPG signals. Although a copious number of methods are available for peak detection, none of them consider an online processing of the signal. In this paper we propose an online peak detection algorithm that tries to mimic the human cognitive model using a three-layered feedforward neural network trained using online sequential learning algorithm. The signals are processed sequentially without pre-processing or feature extraction, and result in an almost instantaneous detection of peaks.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1313-1320
Number of pages8
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 30 Jun 2017
Externally publishedYes
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States
CityAnchorage
Period14/05/1719/05/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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