Herraiz Álvaro Huerta, Martínez-Rodrigo Arturo, Bertomeu-González Vicente, Quesada Aurelio, Rieta José J, Alcaraz Raúl
Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Clinical Medicine Department, Miguel Hernandez University, 03202 Elche, Spain.
Entropy (Basel). 2020 Jul 1;22(7):733. doi: 10.3390/e22070733.
Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.
心房颤动（AF）是临床实践中最常见的心律失常。它通常始于无症状且非常短暂的发作，若不对患者的心电图（ECG）进行长期监测，极难检测到这些发作。尽管近期的便携式和可穿戴设备在这种情况下可能会非常有用，但它们记录的ECG信号常常被噪声和伪迹严重干扰。这损害了后续的自动化分析，而这种分析只有通过自动识别高质量ECG间期的前期阶段才能可靠地进行。到目前为止，已经提出了多种用于ECG质量评估的技术，但据报道，在AF患者的记录上表现不佳。这项工作引入了一种基于深度学习的新颖算法，以在单导联记录交替出现窦性心律、AF发作和其他心律的具有挑战性的环境中稳健地识别高质量的ECG片段。该方法基于卷积神经网络的高学习能力，该网络已使用将ECG信号转换为小波尺度图时获得的二维图像进行训练。为了验证该方法，在500次学习 - 测试迭代中分析了来自三个不同数据库的近100,000个ECG片段，总共分析了超过320,000份ECG。获得的结果显示，检测高质量和丢弃低质量ECG片段的判别能力约为93%，仅将约5%的干净AF片段误分类为有噪声的片段。此外，该方法还能够处理原始ECG记录，无需像之前的阶段那样进行信号预处理或特征提取。因此，它特别适合嵌入便携式和可穿戴设备，通过可靠地提供高质量的ECG片段以供进一步处理阶段使用，促进AF以及其他自动化诊断工具的早期检测。
