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Electrocardiogram signal classification in an IoT environment using an adaptive deep neural networks

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Abstract

IoT is an emerging technology that is rapidly gaining traction throughout the world. With the incredible power and capacity of IoT, anyone may connect to any network or service at any time, from anywhere. IoT-enabled gadgets have transformed the medical industry by granting unprecedented powers such as remote patient monitoring and self-monitoring. Accurate electrocardiogram (ECG) interpretation is critical in the clinical ECG process since it is most often connected with a condition that might create serious difficulties in the body. Cardiologists and medical practitioners frequently utilize ECG to evaluate heart health. The human heart has an electric transmission system that creates regular electrical signals unintentionally and transmits them to the whole heart. Many individuals die as a result of heart disease all around the world. The doctor will be able to provide exceptional treatment to the patients, and the patients will be able to monitor their own health. This research offers an IoT-based ECG monitoring system that uses a heart rate sensor to create data and an intelligent hybrid classification algorithm to classify the data. ECG monitoring has become a widely used method for detecting cardiac problems. The following are the primary contributions of this paper: To begin, this paper introduces WISE (wearable IoT cloud-based health monitoring system), a unique system for real-time personal health monitoring. In order to offer real-time health monitoring, WISE uses the BASN (body area sensor network) infrastructure. Data from the BASN are instantly transferred to the cloud in WISE, and a lightweight wearable LCD may be incorporated to provide rapid access to real-time data. This hybrid model can manage with the problem of class imbalance in the ECG dataset, which will aid in the development of an IoT-based smart and accurate healthcare system. This research uses ADNN, which correctly predicts an abnormal ECG 98.1% of the time. The suggested hybrid model's results are compared to those of other classification models to determine its accuracy and suitability.

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Authors and Affiliations

  1. Department of ECE, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India

    G. Aloy Anuja Mary & B. Sathyasri

  2. Department of ECE, Vijaya Institute of Technology for Women, Vijayawada, India

    K. Murali

  3. Department of CSE, CMR Institute of Technology, Hyderabad, India

    L. Arokia Jesu Prabhu

  4. Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, India

    N. Bharatha Devi

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  1. G. Aloy Anuja Mary
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  4. L. Arokia Jesu Prabhu
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Correspondence to G. Aloy Anuja Mary.

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Mary, G.A.A., Sathyasri, B., Murali, K. et al. Electrocardiogram signal classification in an IoT environment using an adaptive deep neural networks. Neural Comput & Applic 35, 15333–15342 (2023). https://doi.org/10.1007/s00521-023-08534-9

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  • DOI: https://doi.org/10.1007/s00521-023-08534-9

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