未经证实:心房颤动(AF)是最常见的心血管疾病之一,其无症状趋势使房颤检测具有挑战性。机器和深度学习方法通常用于AF检测。
UNASSIGNED:这项研究的目的是评估卷积神经网络(CNN)和随机森林(RF)机器学习模型提供的信息,以进行AF分类。
UNASSIGNED:我们手动提取了166个时频域以及线性和非线性特征,将单导联心电图(ECG)分类为正常,AF,other,或嘈杂的窦性心律。我们使用射频模型中使用的遗传算法选择了56个鲁棒特征的子集。在另一项研究中,一维,在原始ECG节律上设计了12层CNN。来自CNN的输出层的四个特征和来自完全连接层的128个特征被独立地探索用于分类。这些模型在8,528个ECG上进行了训练和内部验证,并在包含3,658个ECG的隐藏数据集上进行了外部验证。接下来,我们分析了工程和CNN学习特征之间的相关性.
UNASSIGNED:使用56个工程特征训练的RF分类器对于正常,F1得分为0.91、0.78和0.72,AF,和其他节奏,分别。然而,支持向量机和CNN模型的集合分别导致F1得分为0.92、0.87和0.80。
UNASSIGNED:我们探索了各种功能和机器学习模型,以使用短(9-61秒)单导联ECG记录来识别AF节律。我们的结果表明,提出的CNN模型为AF分类提取了独特的特征。
UNASSIGNED: Atrial fibrillation (AF) is one of the most common cardiovascular problems, and its asymptomatic tendency makes AF detection challenging. Machine and deep learning methods are commonly used in AF detection.
UNASSIGNED: The purpose of this study was to evaluate the information provided by convolutional neural network (CNN) and random forest (RF) machine learning models for AF classification.
UNASSIGNED: We manually extracted 166 time-frequency domains and linear and nonlinear features to classify single-lead electrocardiograms (ECGs) as normal, AF, other, or noisy sinus rhythms. We selected a subset of 56 robust features using a genetic algorithm that was used in the RF model. In a separate study, a 1-dimensional, 12-layer CNN was designed on the raw ECG rhythms. Four features from the output layer and 128 features from the fully connected layer of CNN were explored independently for classification. The models were trained and internally validated on 8,528 ECGs and externally validated on a hidden dataset containing 3,658 ECGs. Next,we analyzed the correlation between engineered and CNN-learned features.
UNASSIGNED: An RF classifier trained with 56-engineered features resulted in an F1 score of 0.91, 0.78, and 0.72 for normal, AF, and other rhythms, respectively. However, an ensemble of support vector machine and the CNN model resulted in an F1 score of 0.92, 0.87, and 0.80, respectively.
UNASSIGNED: We explored various features and machine learning models to identify AF rhythms using short (9-61 seconds) single-lead ECG recordings. Our results showed that the proposed CNN model abstracted distinctive features for AF classification.