This paper describes the validation of a deep learning model for Internet of Things (IoT) based health care applications. As such, the deep learning model was created to detect episodes of Atrial Fibrillation (AF) using Heart Rate (HR) signals. The initial Long Short-Term Memory (LSTM) model was developed using 20 data sets, from distinct subjects, obtained from the AFDB database on PhysioNet. This model achieved an AF detection accuracy of 98.51% with ten fold cross validation. In this study, we validated the initial results by testing the developed deep learning model with unknown data. To be specific, we fed the data from 82 subjects to the deep learning system and compared the classification results with the diagnosis results indicated by human practitioners. The validation results show 94% accuracy with an area under the Receiver Operating Characteristic (ROC) curve of 96.58. These results indicate that the LSTM model is able to extract the feature maps from the unknown data and hence detect the AF periods accurately. With this blindfold validation testing we violated a well known design rule for learning systems which states that more data should be used for training than for testing. By doing so, we have established that our deep learning system is fit for practical deployment, because in a practical situation the diagnosis support system must apply the knowledge, extracted from a limited training data set, to a HR trace from a patient.

本文介绍了基于物联网（IoT）的医疗保健应用程序的深度学习模型的验证。因此，创建了深度学习模型以使用心率（HR）信号检测心房颤动（AF）发作。最初的长期短期记忆（LSTM）模型是使用从PhysioNet上AFDB数据库获得的，来自不同主题的20个数据集开发的。通过十次交叉验证，该模型实现了98.51％的AF检测精度。在本研究中，我们通过使用未知数据测试开发的深度学习模型来验证初始结果。具体来说，我们将来自82个受试者的数据馈入了深度学习系统，并将分类结果与从业人员指示的诊断结果进行了比较。验证结果显示94％的准确性，并且接收器工作特征（ROC）曲线下的面积为96.58。这些结果表明，LSTM模型能够从未知数据中提取特征图，从而准确地检测AF周期。通过这种眼罩验证测试，我们违反了学习系统的众所周知的设计规则，该规则规定，培训应使用比测试更多的数据。通过这样做，我们已经确定我们的深度学习系统适合实际部署，因为在实际情况下，诊断支持系统必须将从有限的训练数据集中提取的知识应用于患者的HR跟踪。这些结果表明，LSTM模型能够从未知数据中提取特征图，从而准确地检测AF周期。通过这种眼罩验证测试，我们违反了学习系统的众所周知的设计规则，该规则规定，培训应使用比测试更多的数据。通过这样做，我们已经确定我们的深度学习系统适合实际部署，因为在实际情况下，诊断支持系统必须将从有限的训练数据集中提取的知识应用于患者的HR跟踪。这些结果表明，LSTM模型能够从未知数据中提取特征图，从而准确地检测AF周期。通过这种眼罩验证测试，我们违反了学习系统的众所周知的设计规则，该规则规定，培训应使用比测试更多的数据。通过这样做，我们已经确定我们的深度学习系统适合实际部署，因为在实际情况下，诊断支持系统必须将从有限的训练数据集中提取的知识应用于患者的HR跟踪。