Key result
Automated classification algorithm identifies rare morphological arrhythmias with ~90% accuracy for fusion beats.
Why the study?
Manual visual inspection of abnormal heartbeats is tedious, error-prone, and delays care, while real-time automated detection faces challenges from rare heartbeat morphologies underrepresented in datasets.
Does an automated heartbeat classification algorithm based on nonlinear morphological features and a voting scheme improve the detection of rare heartbeat morphologies?
Does an automated heartbeat classification algorithm based on nonlinear morphological features and a voting scheme improve the detection of rare heartbeat morphologies?
Answers are grounded in this paper and cite their sources.
An automated heartbeat classification algorithm using nonlinear morphological features and a voting scheme demonstrates high accuracy in predicting rare heartbeat morphologies, potentially enabling real-time detection on portable devices.
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%.
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Rajesh et al. (2019) studied Cardiac arrhythmias (n=40). Automated heartbeat classification algorithm using ICEEMD, nonlinear morphological features, and a voting scheme vs. Existing state-of-the-art automated classification methods was evaluated on Prediction accuracy for minority heartbeat groups (fusion and unknown classes). The proposed automated heartbeat classification algorithm demonstrated high predictive capability for rare morphological arrhythmias, identifying fusion and unknown classes with 90.4% and 100% accuracy, respectively.
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