02041nas a2200229   4500000000100000000000100001008004100002260001200043100001900055700001900074700001800093700001600111700002300127700002100150700002500171245011300196856004600309300000900355490000700364520142600371022001401797       2019                            d  c11/20191 aRajesh Kandala1 aRavindra Dhuli1 aPawel Plawiak1 aGanesh Naik1 aHossein Moeinzadeh1 aGaetano Gargiulo1 aSuryanarayana Gunnam00aTowards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method  uhttps://www.mdpi.com/1424-8220/19/23/5079  a50790 v233 a<p>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%.</p>
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