Towards real-time heartbeat classification: Evaluation of nonlinear morphological features and voting method

Rajesh N.V.P.S. Kandala, Ravindra Dhuli, Paweł Pławiak, Ganesh R. Naik, Hossein Moeinzadeh, Gaetano D. Gargiulo, Suryanarayana Gunnam

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

Original languageEnglish
Article number5079
Number of pages27
JournalSensors (Switzerland)
Volume19
Issue number23
DOIs
Publication statusPublished - 1 Dec 2019
Externally publishedYes

Bibliographical note

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Keywords

  • Classification
  • Electrocardiogram signal
  • FPGA
  • Improved complete ensemble empirical mode decomposition
  • Inter-patient scheme
  • Nonlinear features
  • Voting

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