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首页> 外文会议>IEEE International Conference on Data Science and Advanced Analytics >Anomaly detection in ECG time signals via deep long short-term memory networks
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Anomaly detection in ECG time signals via deep long short-term memory networks

机译：通过深层长期短期记忆网络对ECG时间信号进行异常检测

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Electrocardiography (ECG) signals are widely used to gauge the health of the human heart, and the resulting time series signal is often analyzed manually by a medical professional to detect any arrhythmia that the patient may have suffered. Much work has been done to automate the process of analyzing ECG signals, but most of the research involves extensive preprocessing of the ECG data to derive vectorized features and subsequently designing a classifier to discriminate between healthy ECG signals and those indicative of an Arrhythmia. This approach requires knowledge and data of the different types of Arrhythmia for training. However, the heart is a complex organ and there are many different and new types of Arrhythmia that can occur which were not part of the original training set. Thus, it may be more prudent to adopt an anomaly detection approach towards analyzing ECG signals. In this paper, we utilize a deep recurrent neural network architecture with Long Short Term Memory (LSTM) units to develop a predictive model for healthy ECG signals. We further utilize the probability distribution of the prediction errors from these recurrent models to indicate normal or abnormal behavior. An added advantage of using LSTM networks is that the ECG signal can be directly fed into the network without any elaborate preprocessing as required by other techniques. Also, no prior information about abnormal signals is needed by the networks as they were trained only on normal data. We have used the MIT-BIH Arrhythmia Database to obtain ECG time series data for both normal periods and for periods during four different types of Arrhythmias, namely Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC), Paced Beats (PB) and Ventricular Couplet (VC). Results are promising and indicate that Deep LSTM models may be viable for detecting anomalies in ECG signals.
机译：心电图（ECG）信号被广泛用于评估人类心脏的健康状况，并且通常由医疗专业人员手动分析所得的时间序列信号，以检测患者可能遭受的任何心律不齐。已经完成了许多工作来自动化分析心电信号的过程，但是大多数研究涉及对心电数据进行广泛的预处理，以得出矢量化特征，随后设计分类器来区分健康的心电信号和心律失常的信号。这种方法需要不同类型的心律失常的知识和数据来进行训练。但是，心脏是一个复杂的器官，可能会发生许多不同的新型心律失常，而这并不是原始训练集的一部分。因此，采用异常检测方法来分析ECG信号可能更为谨慎。在本文中，我们利用具有长期短期记忆（LSTM）单元的深度递归神经网络架构来开发健康ECG信号的预测模型。我们进一步利用来自这些递归模型的预测误差的概率分布来指示正常或异常行为。使用LSTM网络的另一个好处是，无需像其他技术一样进行任何复杂的预处理，就可以将ECG信号直接馈入网络。同样，网络不需要关于异常信号的先验信息，因为它们仅在正常数据上进行了训练。我们已经使用MIT-BIH心律失常数据库来获取正常时段和四种不同类型的心律失常期间的心电图时间序列数据，即室性早搏（PVC），房性早搏（APC），节律性搏动（PB）和心律失常室联（VC）。结果令人鼓舞，表明Deep LSTM模型对于检测ECG信号异常可能是可行的。

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