Validating the robustness of an internet of things based atrial fibrillation detection system

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.