Convolutional Neural Network Fused With Recurrent Network for ECG-Based Detection of Hypertrophic Cardiomyopathy.

Yogalakshmi V, Manikandan T
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Abstract

Background: Hypertrophic Cardiomyopathy (HCM) affects the left ventricle of the heart, leading to thickening of the ventricular wall and potentially life-threatening conditions, such as atrial fibrillation, cardiac failure, and sudden death. Early and accurate detection of HCM from Electrocardiogram (ECG) signals is critical for reducing mortality risk. However, most existing methods fail to simultaneously capture spatial and temporal patterns in ECG data, resulting in reduced diagnostic reliability.

Method: This paper proposes a hybrid Deep Learning (DL) network for detecting HCM using the ECG. Initially, input ECG signals are forwarded for pre-processing by Kalman filter. Then, processed signal is fed to feature extraction phase for extracting Empirical Mode Decomposition (EMD), statistical and medical features, which is followed by feature fusion, wherein the optimal feature is merged by Deep Belief Network (DBN) with Jensen-Shannon distance. Moreover, Convolutional Neural Network Fused with Recurrent Network (CNNFRN) performs HCM detection and final detected output is effectively achieved. The proposed CNNFRN combines Kalman Neural Network (CNN) and Recurrent Neural Network (RNN) based on regression modelling. Finally, the model is trained under a supervised framework using the Adam optimizer.

Results: The proposed model is validated using the PTB Diagnostic ECG Database and Shaoxing and Ningbo Hospital ECG Database. The results show that the proposed CNNFRN model achieved an accuracy of 0.940, sensitivity of 1.000, specificity of 0.913, and an F1-score of 0.956. These findings confirm the model's effectiveness in robust and early detection of HCM, offering significant clinical value.

Conclusion: The proposed model accurately detects HCM by combining advanced feature extraction and a hybrid deep learning approach that captures both spatial and temporal ECG patterns. It also shows strong performance and reliability across multiple databases, making it valuable for early and effective clinical diagnosis.

卷积神经网络与循环神经网络融合用于ecg检测肥厚性心肌病。
背景:肥厚性心肌病(HCM)影响心脏左心室,导致心室壁增厚和潜在的危及生命的疾病,如心房颤动、心力衰竭和猝死。从心电图(ECG)信号中早期准确检测HCM对于降低死亡风险至关重要。然而,大多数现有方法无法同时捕获心电数据中的空间和时间模式,导致诊断可靠性降低。方法:本文提出了一种混合深度学习(DL)网络,用于利用ECG检测HCM。首先,将输入的心电信号转发给卡尔曼滤波进行预处理。然后,将处理后的信号送入特征提取阶段,提取EMD、统计特征和医学特征,进行特征融合,其中最优特征通过深度信念网络(Deep Belief Network, DBN)与Jensen-Shannon距离进行融合。此外,卷积神经网络与递归网络(CNNFRN)融合进行HCM检测,有效地实现了最终检测输出。提出的CNNFRN结合了卡尔曼神经网络(CNN)和基于回归建模的递归神经网络(RNN)。最后,使用Adam优化器在监督框架下对模型进行训练。结果:采用PTB诊断心电数据库和绍兴宁波医院心电数据库对该模型进行了验证。结果表明,本文提出的CNNFRN模型准确率为0.940,灵敏度为1.000,特异性为0.913,f1评分为0.956。这些发现证实了该模型在HCM的稳健和早期检测方面的有效性,具有重要的临床价值。结论:该模型通过结合先进的特征提取和混合深度学习方法来准确检测HCM,同时捕获空间和时间的ECG模式。它还显示出跨多个数据库的强大性能和可靠性,使其在早期和有效的临床诊断中具有价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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