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May 24, 2026Journal of Multiscale Modelling

AE-3S and DC2SNN model secures ECG signals and detects arrhythmias with ~99% accuracy.

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Why the study?

ECG signals contain sensitive health information, but existing works have not concentrated on securing ECG signal processing in VLSI design.

Population

ECG signals

Key result

The proposed AE-3S and DC2SNN model efficiently secured ECG signals and detected arrhythmias with a high accuracy of 99.15%.

Authors

SBSubramanyam BoyapatiBKBhavya KadiyalaRNRajani Priya Nippatla

Discussion

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Overview

Requires prospective clinical validation before adoption; leaves open whether this model improves real-world arrhythmia detection or data security.

Key Points

  • The aim is to enhance data security during ECG signal processing while improving arrhythmia detection accuracy.
  • Proposed a dual approach using AE-3S for data encryption and DC2SNN for arrhythmia classification.
  • Applied dynamic artifact suppression and feature selection techniques for effective signal preprocessing.
  • Achieved signal enhancement by converting analog ECG signals to digital and detecting peaks.
  • Achieved an accuracy of 99.15% in arrhythmia detection using the combined AE-3S and DC2SNN methods.

Structured PICO

P
Population
ECG signals
I
Intervention
AE-3S encryption and DC2SNN-based irregular heartbeat classification model (incorporating EICFMD for artifact suppression and NGQSO for feature selection)
O
Outcome
Arrhythmia detection accuracy and data securitysurrogate

The proposed AE-3S and DC2SNN model provides a highly accurate (99.15%) method for arrhythmia detection while ensuring ECG data security.

Cite This Study

Boyapati et al. (2026) studied Arrhythmia. AE-3S and DC2SNN was evaluated on Arrhythmia detection accuracy. The proposed AE-3S and DC2SNN model efficiently secured ECG signals and detected arrhythmias with a high accuracy of 99.15%.

synapsesocial.com/papers/6a12968148a0ea1665673535https://doi.org/10.1142/s1756973726400305
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