Purpose

According to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.

Design/methodology/approach

In this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.

Findings

Among these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.

Originality/value

In this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

中文翻译：

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使用深度学习模型集合对心电图信号进行分类

目的

据世界卫生组织称，心律失常是全球死亡的主要原因之一。为了降低死亡率，应正确识别心血管疾病，并立即为患者提供适当的治疗。本文的目的是实现一个更好的心跳分类模型，该模型将比其他已实现的心跳分类方法工作得更好。

设计/方法/方法

在本文中，提出了两种深度学习模型的集成，以根据 ANSI-AAMI 标准将 MIT-BIH 心律失常数据库分为四个不同的类别。首先，使用卷积神经网络 (CNN) 模型对原始数据集上的心跳进行分类。其次，从数据集中提取四个特征（小波、RR 间隔、形态学和高阶统计量），然后应用于长短期记忆（LSTM）模型对心跳进行分类。最后，使用具有求和规则、乘积规则和多数投票的 CNN 和 LSTM 模型的集成来识别心跳类别。

发现

其中，使用具有乘积规则的集成方法获得的最高准确率为98.58%。结果表明，与本研究中讨论的其他技术相比，CNN 和 BLSTM 的集成提供了令人满意的性能。

原创性/价值

在这项研究中，我们开发了两种深度学习模型的新组合，以使用输入 ECG 信号的分割来提高心律失常分类的性能。本研究的贡献如下：首先，构建了一个深度 CNN 模型，使用原始数据集对 ECG 心跳进行分类。其次，从原始数据集中提取四类特征（RR间期、HOS、形态学和小波），然后应用于双向LSTM模型对ECG心跳进行分类。第三，测试组合规则（求和规则、乘积规则和多数投票规则）以确保 CNN 和 LSTM 模型的累积概率。