|Tytuł||Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method|
|Publication Type||Journal Article|
|Autorzy||Kandala RNVPS, Dhuli R, Pławiak P, Naik GR, Moeinzadeh H, Gargiulo GD, Gunnam S|
|Słowa kluczowe||Classification, electrocardiogram signal, FPGA, improved complete ensemble empirical mode decomposition, inter-patient scheme, nonlinear features, voting|
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%.