论文标题
深度转交通模型，用于诊断从12铅心电图中诊断出左束分支块
Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms
论文作者
论文摘要
心脏重新同步治疗（CRT）是一种用于补偿心跳不规则的治疗方法。研究表明，这种治疗在心脏束缚块（LBBB）心律不齐的心脏病患者中更有效。因此，识别此心律失常是确定是否使用CRT的重要第一步。另一方面，在心电图（ECG）上检测LBBB的传统方法通常与错误有关。因此，需要一种准确的方法来从心电图数据中诊断出这种心律失常。作为一个新的研究领域，机器学习有助于提高人类系统的性能。作为机器学习的新子领域，深度学习具有分析数据并提高系统准确性的更多力量。这项研究提出了一个深度学习模型，用于从12铅ECG数据中检测LBBB心律失常。该模型由一维扩张的卷积层组成。注意机制还用于识别重要的输入数据特征并更准确地对输入进行分类。使用10倍的交叉验证方法，在包含10344 12铅ECG样品的数据库上训练并验证了该模型。该模型在12铅ECG数据上获得的最终结果如下。精度：98.80+-0.08％，特异性：99.33+-0.11％，F1分数：73.97+-1.8％，以及接收器操作特性曲线（AUC）下的面积（AUC）：0.875+-0.0192。这些结果表明，本研究中提出的模型可以有效地以良好的效率诊断LBBB，如果在医疗中心使用，将极大地帮助诊断这种心律不齐和早期治疗。
Cardiac resynchronization therapy (CRT) is a treatment that is used to compensate for irregularities in the heartbeat. Studies have shown that this treatment is more effective in heart patients with left bundle branch block (LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important initial step in determining whether or not to use CRT. On the other hand, traditional methods for detecting LBBB on electrocardiograms (ECG) are often associated with errors. Thus, there is a need for an accurate method to diagnose this arrhythmia from ECG data. Machine learning, as a new field of study, has helped to increase human systems' performance. Deep learning, as a newer subfield of machine learning, has more power to analyze data and increase systems accuracy. This study presents a deep learning model for the detection of LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated convolutional layers. Attention mechanism has also been used to identify important input data features and classify inputs more accurately. The proposed model is trained and validated on a database containing 10344 12-lead ECG samples using the 10-fold cross-validation method. The final results obtained by the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%, specificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver operating characteristics curve (AUC): 0.875+-0.0192. These results indicate that the proposed model in this study can effectively diagnose LBBB with good efficiency and, if used in medical centers, will greatly help diagnose this arrhythmia and early treatment.
