Towards real-time heartbeat classification: evaluation of nonlinear morphological features and voting method
Authors:
- Rajesh N V P S Kandala,
- Ravindra Dhuli,
- Paweł Pławiak,
- Ganesh R. Naik,
- Hossein Moeinzadeh,
- Gaetano D. Gargiulo,
- Suryanarayana Gunnam
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%.
- Record ID
- CUT9accc772ecb4401eaa26b95d66b72a08
- Publication categories
- ;
- Author
- Journal series
- Sensors, ISSN , e-ISSN 1424-8220, Biweekly
- Issue year
- 2019
- Vol
- 19
- No
- 23
- Pages
- [1-27]
- Article number
- 5079
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 24-27; Bibliografia (liczba pozycji) - 63; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 19, Iss. 23, Spec. Iss.
- Substantive notes
- Special Issue: Sensors for Monitoring Patient Triage and Management
- Keywords in English
- electrocardiogram signal, nonlinear features, improved complete ensemble empirical mode decomposition, inter-patient scheme, voting, classification, FPGA
- DOI
- DOI:10.3390/s19235079 Opening in a new tab
- URL
- https://www.mdpi.com/1424-8220/19/23/5079 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9accc772ecb4401eaa26b95d66b72a08/
- URN
urn:pkr-prod:CUT9accc772ecb4401eaa26b95d66b72a08
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.