用变点检测增强脑电心电数据，提升深度学习模型性能
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates
- 结合变点检测与iAAFT生成新信号，保持原信号时频特性
- 癫痫检测准确率最高提升4.4%，房颤识别F1值提高2.1%
- 适合生理信号少、非平稳性强的医疗数据增强场景
深度学习性能高度依赖训练数据的数量与质量，尤其对于噪声大、样本少的生理时间序列。标准数据增强方法难以处理非平稳信号的动态统计特性。为此，本文提出一种新方法：将离线变点检测与迭代幅值调整傅里叶变换（iAAFT）结合，确保增强过程中保留原始信号的时频特征。在CHB-MIT和Siena头皮脑电图（EEG）数据库上，癫痫检测模型准确率分别提升4.4%和1.9%，精确率提升10%和5.5%，召回率提升3.6%和0.9%，F1值提升4.2%和1.4%。在Computing in Cardiology Challenge 2017心电图数据集上，房颤分类任务准确率提升0.3%，精确率提升2.1%，召回率提升0.8%，F1值提升2.1%。
原文摘要 · Abstract (English)
The performance of deep learning methods critically depends on the quality and quantity of the available training data. This is especially the case for physiological time series, which are both noisy and scarce, which calls for data augmentation to artificially increase the size of datasets. Another issue is that the time-evolving statistical properties of nonstationary signals prevent the use of standard data augmentation techniques. To this end, we introduce a novel method for augmenting nonstationary time series. This is achieved by combining offline changepoint detection with the iterative amplitude-adjusted Fourier transform (iAAFT), which ensures that the time-frequency properties of the original signal are preserved during augmentation. The proposed method is validated through comparisons of the performance of i) a deep learning seizure detection algorithm on both the original and augmented versions of the CHB-MIT and Siena scalp electroencephalography (EEG) databases, and ii) a deep learning atrial fibrillation (AF) detection algorithm on the original and augmented versions of the Computing in Cardiology Challenge 2017 dataset. By virtue of the proposed method, for the CHB-MIT and Siena datasets respectively, accuracy rose by 4.4% and 1.9%, precision by 10% and 5.5%, recall by 3.6% and 0.9%, and F1 by 4.2% and 1.4%. For the AF classification task, accuracy rose by 0.3%, precision by 2.1%, recall by 0.8%, and F1 by 2.1%.
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