Key result
A novel real-time cardiac arrhythmia classifier using a two-stage machine learning algorithm achieved 97.55% accuracy for abnormality identification and 97.1% accuracy for 15 arrhythmia classes.
Why the study?
Cardiovascular diseases have increased drastically worldwide, motivating algorithms for continuous monitoring, identification, and classification of cardiac arrhythmias from ECG data.
Does a novel two-stage machine learning algorithm accurately identify and classify cardiac arrhythmias from ECG data?
Does a novel two-stage machine learning algorithm accurately identify and classify cardiac arrhythmias from ECG data?
A novel two-stage machine learning algorithm demonstrates high accuracy in classifying cardiac arrhythmias from ECG data and achieves real-time performance via GPU acceleration.
Promising ML accuracy for real-time ECG arrhythmia detection; leaves open prospective clinical validation before adoption.
Cardiovascular diseases (CVD) have increased drastically among Non-Communicable diseases, which have peaked over the past recent years. In 2018, around 17.9 million which is an estimated 31% of the people have died worldwide due to CVDs. A novel machine learning algorithm for continuous monitoring, identification and classification of cardiac arrhythmias from Electrocardiogram (ECG) data is presented here. The proposed solution has two stages where the first stage is a rule based cardiac abnormality identification which has an individual 97.55% ± 0.3% of accuracy (Acc) for a dataset of 705,000 and the second stage is a Neural Network (NN) based classification model which is trained and tested to identify 15 different classes recommended by ANSI/AAMI standard [1], and has 97.1% of individual accuracy for MIT-BIH Arrhythmia dataset [2] of 96265 beat samples. The combined real-time cardiac arrhythmia classifier is parallelized with CUDA in order to utilize the GPU and increase the execution speed by 4.86 times.
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Abayaratne et al. (2019) studied Cardiac arrhythmias. Real-time cardiac arrhythmia classifier was evaluated on Accuracy of cardiac abnormality identification and classification. A novel real-time cardiac arrhythmia classifier using a two-stage machine learning algorithm achieved 97.55% accuracy for abnormality identification and 97.1% accuracy for 15 arrhythmia classes.
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