Key result
An arrhythmia detection algorithm using GoogLeNet and a Generative Adversarial Network achieved a classification accuracy of approximately 99% on ECG data.
Why the study?
Because symptoms of arrhythmia occur irregularly, the heart requires monitoring over a lengthy period, motivating algorithms that can accurately diagnose arrhythmia from lifelog data over shorter timeframes.
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An algorithm combining GoogLeNet and a GAN for data augmentation achieved 99% accuracy in classifying ECG data for arrhythmia detection, potentially enabling accurate diagnosis from short-term lifelog measurements.
Arrhythmia is a cardiovascular disease with an irregular heartbeat, which can lead to a heart attack if it lasts for an excessive amount of time. Because the symptoms of arrhythmia occur irregularly, the heart needs to be monitored for a lengthy time period. This study suggests an arrhythmia diagnosis algorithm using GoogLeNet and a GAN. Because the algorithm proposed in this study can add to the number of data using a GAN, it can accurately diagnose an arrhythmic occurrence from measured lifelog over a short period of time. The classification of ECG data using GoogLeNet and a GAN showed an accuracy of approximately 99%.
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Shin et al. (2021) studied Arrhythmia. Arrhythmia detection algorithm using GoogLeNet and GAN was evaluated on Classification accuracy. An arrhythmia detection algorithm using GoogLeNet and a Generative Adversarial Network achieved a classification accuracy of approximately 99% on ECG data.
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