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

Autor: Suryanarayana Gunnam, Paweł Pławiak, Ravindra Dhuli, Gaetano D. Gargiulo, Ganesh R. Naik, Hossein Moeinzadeh, Rajesh N.V.P.S. Kandala
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Scheme (programming language)
Heartbeat
Databases
Factual
Computer science
Association (object-oriented programming)
media_common.quotation_subject
0206 medical engineering
02 engineering and technology
lcsh:Chemical technology
Biochemistry
Article
Analytical Chemistry
Task (project management)
Electrocardiography
Heart Rate
Voting
0202 electrical engineering
electronic engineering
information engineering
Abnormal heart rhythms
Humans
lcsh:TP1-1185
Electrical and Electronic Engineering
electrocardiogram signal
Instrumentation
improved complete ensemble empirical mode decomposition
inter-patient scheme
media_common
computer.programming_language
business.industry
Pattern recognition
Arrhythmias
Cardiac
Signal Processing
Computer-Assisted
020601 biomedical engineering
Atomic and Molecular Physics
and Optics
fpga
Nonlinear Dynamics
classification
nonlinear features
voting
Key (cryptography)
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Algorithms
Zdroj: Sensors, Vol 19, Iss 23, p 5079 (2019)
Sensors
Volume 19
Issue 23
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Popis: 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%.
Databáze: OpenAIRE
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