Background and objective

Cardiac arrhythmia, which is an abnormal heart rhythm, is a common clinical problem in cardiology. Detection of arrhythmia on an extended duration electrocardiogram (ECG) is done based on initial algorithmic software screening, with final visual validation by cardiologists. It is a time consuming and subjective process. Therefore, fully automated computer-assisted detection systems with a high degree of accuracy have an essential role in this task. In this study, we proposed an effective deep neural network (DNN) model to detect different rhythm classes from a new ECG database.

Methods

Our DNN model was designed for high performance on all ECG leads. The proposed model, which included both representation learning and sequence learning tasks, showed promising results on all 12-lead inputs. Convolutional layers and sub-sampling layers were used in the representation learning phase. The sequence learning part involved a long short-term memory (LSTM) unit after representation of learning layers.

Results

We performed two different class scenarios, including reduced rhythms (seven rhythm types) and merged rhythms (four rhythm types) according to the records from the database. Our trained DNN model achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively.

Conclusion

Recently, deep learning algorithms have been found to be useful because of their high performance. The main challenge is the scarcity of appropriate training and testing resources because model performance is dependent on the quality and quantity of case samples. In this study, we used a new public arrhythmia database comprising more than 10,000 records. We constructed an efficient DNN model for automated detection of arrhythmia using these records.

中文翻译：

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精确的深度神经网络模型可在10,000多个个体ECG记录上检测出心律不齐。

背景和目标

心脏心律不齐是一种异常的心律，是心脏病学中常见的临床问题。基于最初的算法软件筛选，并由心脏病专家进行最终的视觉验证，可以在延长的心电图（ECG）上检测心律不齐。这是一个耗时且主观的过程。因此，具有高度准确性的全自动计算机辅助检测系统在此任务中起着至关重要的作用。在这项研究中，我们提出了一种有效的深度神经网络（DNN）模型，可以从新的ECG数据库中检测不同的节奏类别。

方法

我们的DNN模型旨在为所有ECG导线提供高性能。提议的模型包括表示学习和序列学习任务，在所有12导联输入上都显示出令人鼓舞的结果。在表示学习阶段中使用了卷积层和子采样层。在学习层表示之后，序列学习部分涉及一个长短期记忆（LSTM）单元。

结果

根据数据库中的记录，我们执行了两种不同的课堂方案，包括降低节奏（七个节奏类型）和合并节奏（四种节奏类型）。我们训练有素的DNN模型在减少和合并的节奏类上分别达到92.24％和96.13％的准确性。

结论

最近，由于深度学习算法的高性能而被发现是有用的。主要的挑战是缺乏适当的培训和测试资源，因为模型的性能取决于案例样本的质量和数量。在这项研究中，我们使用了一个新的公共心律失常数据库，该数据库包含10,000多个记录。我们使用这些记录构建了用于自动检测心律不齐的有效DNN模型。