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

FAUST, Oliver, KAREEM, Murtadha, SHENFIELD, Alex, ALI, Ali and RAJENDRA ACHARYA, U (2020). Validating the robustness of an internet of things based atrial fibrillation detection system. Pattern Recognition Letters, 133, 55-61.

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Link to published version:: https://doi.org/10.1016/j.patrec.2020.02.005
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    Abstract

    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.

    Item Type: Article
    Additional Information: ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised. **Journal IDs: issn 01678655 **History: issue date 05-02-2020; accepted 04-02-2020
    Identification Number: https://doi.org/10.1016/j.patrec.2020.02.005
    Page Range: 55-61
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 17 Feb 2020 12:03
    Last Modified: 10 Mar 2020 10:45
    URI: http://shura.shu.ac.uk/id/eprint/25812

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