Secure healthcare monitoring of arrythmias in internet of things with deep learning and elgamal encryption

• Published in: Journal of intelligent & fuzzy systems 2024-01, Vol.46 (1), p.1697-1712
• Main Authors: Sumathi, S., Balaji Ganesh, A.
• Format: Article
• Language: English
• Subjects: Arrhythmia; Cardiac arrhythmia; Classification; Continuous wavelet transform; Cryptography; Curves; Data collection; Data transmission; Deep learning; Electrocardiography; Encryption; Feature extraction; Formability; Health care; Internet of Things; Machine learning; Signal transmission; Wavelet transforms
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-235885

Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient’s irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients’ heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient’s ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes.