Key result
Deep learning detects heart arrhythmias from ECGs with ~92% accuracy, outperforming traditional algorithms.
Why the study?
Heart arrhythmias are common and carry life-threatening risks if left unchecked, creating a need for systems that can detect arrhythmias quickly and effectively using advanced computational methods.
Can deep learning and machine learning algorithms effectively detect heart arrhythmias from electrocardiogram (ECG) reports?
Can deep learning and machine learning algorithms effectively detect heart arrhythmias from electrocardiogram (ECG) reports?
Deep learning and machine learning algorithms are proposed as a method to quickly and effectively detect heart arrhythmias from ECG reports.
Warrants caution before clinical use; leaves open prospective validation of DL arrhythmia detection in practice.
Heart arrhythmia, or irregular heart rhythm, is an extremely common heart affliction experienced by a large percent of the world’s population every day, mostly going unnoticed. However, if left unchecked for an extended period of time, it poses an inherent risk to human life. Advancements in technology have enabled us to leverage the awesome computational power of graphics processing units in parallel in order to derive solutions to real life medical issues, by analyzing tremendous amounts of data in a relatively short amount of time. Owing to their ability to parse huge amounts of data and quickly perform multiple complex computations in parallel, machine learning algorithms have repeatedly and consistently outperformed humans in tasks such as pattern recognition and data analysis. Through this research project, we seek to contribute to the medical field by implementing deep learning technology along with machine learning algorithms into a system which can detect heart arrhythmias from electrocardiogram (ECG) reports quickly and effectively.
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Fernandes et al. (2021) studied Heart arrhythmia (n=420). Deep learning model vs. Traditional machine learning models was evaluated on Classification accuracy. An optimized deep learning model detected heart arrhythmias from ECG signals with an accuracy of 91.52%, outperforming traditional machine learning algorithms.
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