Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications.

• A novel deep learning models for arrhythmia detection in IoT healthcare applications are proposed. • It has the independence of the ECG signal quality, as operation is on the spectrograms of the ECG signals. • Ability to work on a single ECG lead, which reduces the complexity of operations. • Applicability of the proposed framework in the smart healthcare platform for real-time operation. • The proposed framework overcomes the overfitting problems and achieves high accuracy on several datasets. In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data. Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. [Display omitted] [ABSTRACT FROM AUTHOR]