A Multitier Deep Learning Model for Arrhythmia Detection
A Multitier Deep Learning Model for Arrhythmia Detection
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DOI：
10.1109/tim.2020.3033072
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发表时间：
2021-01-01
影响因子：
5.6
通讯作者：
Abd El-Latif, Ahmed A.
中科院分区：
文献类型：
--
作者：
Hammad, Mohamed;Iliyasu, Abdullah M.;Abd El-Latif, Ahmed A.
心电图机（ECG）被用作诊断心血管疾病（CVD）的主要工具。ECG信号提供了一个框架来探测潜在的属性，并增强通过传统工具和患者-医生对话获得的初步诊断。尽管其已被证明的实用性，破译大数据集，以确定适当的信息仍然是一个挑战，在基于心电图的心血管疾病的诊断和治疗。我们的研究提出了一种深度神经网络（DNN）策略来改善上述困难。我们的策略包括一个学习阶段，通过一个强大的特征提取协议提高分类精度。其次是使用遗传算法（GA）的过程中，聚合的特征提取和分类的最佳组合。所提出的技术与最先进的方法记录的性能比较，该区域的平均准确度和F1评分分别增加了0.94和0.953。结果表明，所提出的模型可以作为一个分析模块，提醒用户和/或医学专家时，检测到异常。
An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.