Computer Science and Digital Society Laboratory (LIST3N), Université de Technologie de Troyes, 10300 Troyes, France.
Pôle Santé Publique, Hôpitaux Champagne Sud (HCS), 10000 Troyes, France.
Sensors (Basel). 2022 Nov 26;22(23):9190. doi: 10.3390/s22239190.
Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough oxygen and nutrients to function normally. Early and accurate detection of heart failure is critical for impeding disease progression. An electrocardiogram (ECG) is a test that records the rhythm and electrical activity of the heart and is used to detect HF. It is used to look for irregularities in the heart's rhythm or electrical conduction, as well as a history of heart attacks, ischemia, and other conditions that may initiate HF. However, sometimes, it becomes difficult and time-consuming to interpret the ECG signal, even for a cardiac expert. This paper proposes two models to automatically detect HF from ECG signals: the first one introduces a Convolutional Neural Network (CNN), while the second one suggests an extension of it by integrating a Support Vector Machine (SVM) layer for the classification at the end of the network. The proposed models provide a more accurate automatic HF detection using 2-s ECG fragments. Both models are smaller than previously proposed models in the literature when the architecture is taken into account, reducing both training time and memory consumption. The MIT-BIH and the BIDMC databases are used for training and testing the adopted models. The experimental results demonstrate the effectiveness of the proposed framework by achieving an accuracy, sensitivity, and specificity of over 99% with blindfold cross-validation. The models proposed in this study can provide doctors with reliable references and can be used in portable devices to enable the real-time monitoring of patients.
心力衰竭（HF）是一种严重的疾病，心脏无法为身体提供足够的氧气和营养物质以正常运作。早期和准确地检测心力衰竭对于阻止疾病进展至关重要。心电图（ECG）是一种记录心脏节律和电活动的测试，用于检测心力衰竭。它用于寻找心脏节律或电传导的不规则性，以及心脏病发作、缺血等可能引发心力衰竭的病史。然而，有时即使对于心脏专家来说，解释 ECG 信号也变得困难且耗时。本文提出了两种从 ECG 信号中自动检测 HF 的模型：第一种引入了卷积神经网络（CNN），而第二种通过在网络末端集成支持向量机（SVM）层来对分类进行扩展。所提出的模型使用 2-s ECG 片段提供了更准确的自动 HF 检测。与文献中提出的模型相比，这两种模型在考虑架构时都更小，减少了训练时间和内存消耗。MIT-BIH 和 BIDMC 数据库用于训练和测试所采用的模型。实验结果通过盲交叉验证实现了超过 99%的准确率、灵敏度和特异性，证明了所提出框架的有效性。本研究中提出的模型可以为医生提供可靠的参考，并可以用于便携式设备以实现患者的实时监测。
