Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications
Graphical abstract
Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications.
Section snippets
INTRODUCTION
A heart rhythm disorder (arrhythmia) is any unusual, or out-of-time pulse in a person's heart rhythm. An arrhythmia may be accompanied by abnormal heart rhythms (a rapid pulse called tachyarrhythmia, or a slow pulse called slow-disorder-bradyarrhythmia) [1]. There are different types of arrhythmia:
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Premature atrial contraction: This is an excessive additional contraction, caused by premature atrial contraction. This condition is simple and does not cause clinical complications [2].
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Premature
Proposed framework for smart healthcare platform
The motivation for the design of the proposed framework is to use modern classification tools to incorporate arrhythmia detection into the existing e-health frameworks. In this section, an overview of the related research and some of the existing frameworks as well as challenges in such frameworks are investigated as a precursor to the adoption of the proposed framework for improved arrhythmia detection performance.
Smart wearable devices, such as smart bands and smartwatches, have become more
Proposed Strategy
The strategy of the proposed framework is to use a DLM with specified parameters to create a hierarchal designed file (hdf). The purpose of obtaining this file is to burn it into a hardware facility, such as raspberry pi or Arduino for real-life applications. In addition, it can be involved in a mobile application for smartphones or smart watches. Fig. 2 shows the data flow and architecture of the proposed strategy. As shown in Fig. 2, the proposed strategy consists of three main phases as
Simulation Results
The proposed DLMs are implemented on different datasets including MIT-BIH, PhysioNet 2016 and PhysioNet 2018. In addition, these DLMs are carried out at different scenarios (binary classes and Multiole classes). This variety of datasets and scenarios provides an accurate and fair evaluation of the proposed models. Furthermore, the strategy of the simulation experiments is to select an optimum model with a certain number of epochs. This optimum DLM is evaluated by the accuracy of detection.
Result Discussion
From the previous results, the proposed DLMs reveal high performance in the detection of arrhythmia. The strategy of evaluation of the proposed models is based on multi-class evaluation. In this strategy, the proposed models are carried out on different datasets with several scenarios. The proposed DLMs are carried out on the MIT-BIH with both spectrogram and compressed forms in addition to PhysioNet 2016 and PhysioNet 2018. These datasets are provided in both two-class and multi-classes
Conclusion
The main objective of this study was to develop a new model for automatic detection of arrhythmia. The main contribution is the design of novel CNN and ConvLSTM-based DLMs to classify the input ECG signals to normal or arrhythmia. We worked on three publicly available datasets from PhysioNet: MIT-BIH database, PhysioNet 2016 and PhysioNet 2018. Our model achieved accuracies of 97%, 98%, 94% and 91% on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016, PhysioNet 2018
Declaration of Competing Interest
The author declares that no conflict of interest exists and if accepted, the article will not be published elsewhere in the same form, in any language, without the written consent of the publisher.
Acknowledgments
This work was supported by the EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
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