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A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM

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Abstract

The Real-time wearable Electrocardiogram (ECG) monitoring device is a perfect choice for assisting in detecting cardiovascular disease. A novel ECG beat classification algorithm is presented for continuous heart monitoring on low-processing-capacity wearable devices. We discuss the different wearable wristwatch monitoring system, which allows for continuous 24-h heart rate monitoring. This paper introduces a novel method for classifying arrhythmias based on deep learning. The method relies on QRS/PT detection, Sigma-Delta Modulation (SDM), One-Dimensional Convolution Neural Networks (1D-CNN) algorithms. The QRS/PT wave detection system is based on the 1D-CNN and SDM framework with local minimum and local maximum point algorithms. We proposed a Long Short-Term Memory (LSTM) recurrent neural network with a blend classifier. The classifier combines two small LSTM networks’ predictions using two different features directly extracted from 1D-CNN and SDM bitstreams. The proposed model is evaluated by detecting QRS/PT waves and classifying arrhythmias using the MIT-BIH Arrhythmia Database. Five different classifications are performed and evaluated by the AAMI standard: N, F, Q, S, and V. The values for accuracy, positive predictivity, sensitivity, and F1-score are 99.56%, 96.5%, 93.87%, and 95.18%, respectively. The proposed algorithm detects QRS/PT in approximately 2050 ms and classifies each heartbeat in approximately 40–60 ms with wearable devices, and it consumes \(1.5\,\upmu \mathrm{w}\) total power. The results indicate that our proposed system outperforms previous research in accuracy and meets both computation time and low power consumption requirements.

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Author notes

  1. Chandra Sekhara Rao Annavarapu and Kishore Kumar Pedapenki have contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, Jharkhand, India

    Meghana Karri & Chandra Sekhara Rao Annavarapu

  2. Electrical and Electronic Engineering, Jain University, Bangalore, 562112, India

    Kishore Kumar Pedapenki

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  1. Meghana Karri
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  2. Chandra Sekhara Rao Annavarapu
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Contributions

MK: Conceptualization, Methodology, Formal analysis, Validation, Resources, Investigation, Data curation, Writing—original draft, Writing—review & editing, Visualization. CSA: Supervision, Investigation, Project administration, Writing—review & editing.

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Correspondence to Meghana Karri.

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Karri, M., Annavarapu, C.S.R. & Pedapenki, K.K. A Real-Time Cardiac Arrhythmia Classification Using Hybrid Combination of Delta Modulation, 1D-CNN and Blended LSTM. Neural Process Lett 55, 1499–1526 (2023). https://doi.org/10.1007/s11063-022-10949-9

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