Next Article in Journal
Application of Phase-Reversal Fresnel Zone Plates for Improving The Elevation Resolution in Ultrasonic Testing with Phased Arrays
Next Article in Special Issue
Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall
Previous Article in Journal
High-Sensitivity, Large Dynamic Range Refractive Index Measurement Using an Optical Microfiber Coupler
Open AccessArticle

Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method

1
Department of ECE, GVPCE (A), Visakhapatnam 530048, India
2
Department of ECE, VIT University, Andhra Pradesh 522237, India
3
Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warsaw 24 st., F-3, 31-155 Krakow, Poland
4
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland
5
The MARCS Institute, Western Sydney University, Milperra, NSW 2214, Australia
6
Department of Electrical Engineering and Information Technology (DIETI), “Federico II” The University of Naples, 80100 Naples, Italy
7
School of Engineering at Western Sydney University, Penrith, NSW 2747, Australia
8
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5079; https://doi.org/10.3390/s19235079
Received: 20 September 2019 / Revised: 10 November 2019 / Accepted: 15 November 2019 / Published: 21 November 2019
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%. View Full-Text
Keywords: electrocardiogram signal; nonlinear features; improved complete ensemble empirical mode decomposition; inter-patient scheme; voting; classification; FPGA electrocardiogram signal; nonlinear features; improved complete ensemble empirical mode decomposition; inter-patient scheme; voting; classification; FPGA
Show Figures

Figure 1

MDPI and ACS Style

Kandala, R.N V P S; Dhuli, R.; Pławiak, P.; Naik, G.R.; Moeinzadeh, H.; Gargiulo, G.D.; Gunnam, S. Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method. Sensors 2019, 19, 5079. https://doi.org/10.3390/s19235079

AMA Style

Kandala RNVPS, Dhuli R, Pławiak P, Naik GR, Moeinzadeh H, Gargiulo GD, Gunnam S. Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method. Sensors. 2019; 19(23):5079. https://doi.org/10.3390/s19235079

Chicago/Turabian Style

Kandala, Rajesh N V P S; Dhuli, Ravindra; Pławiak, Paweł; Naik, Ganesh R.; Moeinzadeh, Hossein; Gargiulo, Gaetano D.; Gunnam, Suryanarayana. 2019. "Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method" Sensors 19, no. 23: 5079. https://doi.org/10.3390/s19235079

Find Other Styles
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
Search more from Scilit
 
Search
Back to TopTop