Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1300-1305. doi: 10.1109/EMBC48229.2022.9871518.
Automatic classification of cardiac abnormalities is becoming increasingly popular with the prevalence of ECG recordings. Many signal processing and machine learning algorithms have shown the potential to identify cardiac ab-normalities accurately. However, most of these methods heavily rely on a large amount of relatively homogeneous datasets. In real life, chances are that there is not enough data for a specific category, and regular deep learning may fail in this scenario. A straightforward intuition is to use the knowledge learned from previous data to solve the problem. This idea leads to learning-to-learn: extrapolating the knowledge accumulated from the old dataset and using it in a different but somewhat related dataset. In this way, we do not need to have considerable data to learn the new task because the underlying features of the old and new datasets resemble one another. In this paper, a novel machine learning method is introduced to solve the ECG arrhythmia detection problem with a limited amount of data. The proposed method combines the popular techniques of meta-learning and transfer learning. It is shown that our method achieves much higher accuracy in ECG arrhythmia classification with a few data and learns the new task faster than regular deep learning.
随着心电图记录的普及，自动分类心脏异常变得越来越流行。许多信号处理和机器学习算法已经显示出准确识别心脏异常的潜力。然而，这些方法大多严重依赖大量相对同质的数据集。在现实生活中，特定类别的数据可能不足，常规的深度学习在这种情况下可能会失败。一个直接的直觉是利用从前的数据中学习到的知识来解决问题。这个想法导致了学习如何学习：从旧数据集积累的知识中推断出来，并将其应用于不同但有些相关的数据集。通过这种方式，我们不需要大量的数据来学习新任务，因为新旧数据集的底层特征彼此相似。在本文中，引入了一种新的机器学习方法来解决有限数据下的心电图心律失常检测问题。所提出的方法结合了元学习和迁移学习的流行技术。结果表明，我们的方法在使用少量数据进行心电图心律失常分类时达到了更高的准确性，并且比常规深度学习更快地学习新任务。
