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

Abstract

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%.

Prediction of heart disease and classifiers’ sensitivity analysis

“…Discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique for the automated detection of arrhythmia detection was employed in [29], and a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset was obtained. An automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies was presented in [30], their algorithm tested on MIT-BIH database, and the simulation results showed the superiority of their proposed method, especially in predicting minority groups with 90.4 and 100% classification. An approach for discovering classification rules of Coronary artery disease (CAD) was proposed by [31], and it was based on the real-world CAD data set and aims at the detection of this disease by producing the accurate and effective rules, and results showed that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD.…”

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

“…All these incidents reveal the negative aspects of biometrics, which have become social issues. Accordingly, major developed regions such as the United States, Europe, and Japan, have been researching and developing biometric systems using bio-signals that exist within the body [ 14 , 15 , 16 , 17 , 18 ].…”

A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

“…In recent years, the machine learning and deep learning network has not only made remarkable achievements in the fields of image processing, audio recognition and many other fields (Wong et al, 2015a(Wong et al, ,b, 2016Kandala et al, 2019;Pławiak et al, 2019, it has also been commonly used in the assisted diagnosis of heart disease based on ECG signals (Zubair et al, 2016;Acharya et al, 2017a,b;Yildirim et al, 2018;Gao et al, 2019;Atal and Singh, 2020;. Pławiak and Acharya (2020) use a deep genetic ensemble of classifiers to classify long-duration ECG signal (10 s).…”