Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning

TL;DR

This study evaluates hybrid machine learning models, including CNN, ResNet, and Transformer-based networks, for detecting tampering in wireless ECG signals and verifying identity, achieving over 99.5% accuracy in complex scenarios.

Contribution

It introduces a hybrid CNN-Transformer approach for ECG tamper detection and identity verification, demonstrating superior performance over traditional models.

Findings

CNN and ResNet models achieve over 99.5% accuracy in detecting complex tampering.

The hybrid CNN-Transformer Siamese network attains 100% accuracy in identity verification.

Models perform reliably across various tampering strategies and real-world activities.

Abstract

With the proliferation of wireless electrocardiogram (ECG) systems for health monitoring and authentication, protecting signal integrity against tampering is becoming increasingly important. This paper analyzes the performance of CNN, ResNet, and hybrid Transformer-CNN models for tamper detection. It also evaluates the performance of a Siamese network for ECG based identity verification. Six tampering strategies, including structured segment substitutions and random insertions, are emulated to mimic real world attacks. The one-dimensional ECG signals are transformed into a two dimensional representation in the time frequency domain using the continuous wavelet transform (CWT). The models are trained and evaluated using ECG data from 54 subjects recorded in four sessions 2019 to 2025 outside of clinical settings while the subjects performed seven different daily activities. Experimental results show that in highly fragmented manipulation scenarios, CNN, FeatCNN-TranCNN, FeatCNN-Tran and ResNet models achieved an accuracy exceeding 99.5 percent . Similarly, for subtle manipulations (for example, 50 percent from A and 50 percent from B and, 75 percent from A and 25 percent from B substitutions) our FeatCNN-TranCNN model demonstrated consistently reliable performance, achieving an average accuracy of 98 percent . For identity verification, the pure Transformer-Siamese network achieved an average accuracy of 98.30 percent . In contrast, the hybrid CNN-Transformer Siamese model delivered perfect verification performance with 100 percent accuracy.