School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China.
Comput Biol Med. 2024 Mar;170:107928. doi: 10.1016/j.compbiomed.2024.107928. Epub 2024 Jan 3.
Electrocardiogram (ECG) recordings obtained from wearable devices are susceptible to noise interference that degrades the signal quality. Traditional methods for assessing the quality of electrocardiogram signals (SQA) are mostly supervised and typically rely on limited types of noise in the training data, which imposes limitations in detecting unknown anomalies. The high variability of both ECG signals and noise presents a greater challenge to the generalization of traditional methods. In this paper, we propose a simple and effective unsupervised SQA method by modeling the SQA of ECG as a problem of anomaly detection, in which, a model of pseudo anomalies enhanced deep support vector data description is introduced to learn a more discriminative and generalized hypersphere of the high-quality ECG in a self-supervised manner. Specifically, we propose a series of ECG noise-generation methods to simulate the noise of real scenarios and use the generated noise samples as the pseudo anomalies to correct the hypersphere learned solely by the high-quality ECG samples. Finally, the quality of ECG can be measured based on the distance to the center of the hypersphere. Extensive experimental results on multiple public datasets and our constructed real-world 12-lead dataset demonstrate the effectiveness of the proposed method.
可穿戴设备获取的心电图（ECG）记录容易受到噪声干扰，从而降低信号质量。传统的心电图信号质量评估（SQA）方法大多是监督式的，并且通常依赖于训练数据中有限类型的噪声，这在检测未知异常方面存在局限性。ECG 信号和噪声的高度可变性给传统方法的泛化带来了更大的挑战。在本文中，我们提出了一种简单而有效的无监督 SQA 方法，通过将 ECG 的 SQA 建模为异常检测问题，其中引入了一种伪异常增强深度支持向量数据描述的模型，以自监督的方式学习高质量 ECG 的更具判别力和泛化的超球体。具体来说，我们提出了一系列 ECG 噪声生成方法来模拟真实场景的噪声，并使用生成的噪声样本作为伪异常来纠正仅由高质量 ECG 样本学习到的超球体。最后，可以基于到超球体中心的距离来测量 ECG 的质量。在多个公共数据集和我们构建的真实 12 导联数据集上的广泛实验结果证明了所提出方法的有效性。
