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<p>In biosignal telemetry, signal integrity must be maintained against noise, artifacts, and transmission disturbances, especially in real-time health applications. In this relation, the present paper proposes the Machine Learning-Based Adaptive Optimization Algorithm (MLAOA) that ensures Lipschitz continuity across the various biosignal types, namely ECG, EEG, and EMG, by providing dynamic adjustment of signal processing parameters. The Algorithm incorporates supervised learning for signal behavior modeling with reinforcement learning for real-time adaptation. Experimental results on benchmark datasets show that MLAOA reduces the mean Lipschitz constant deviation to 2.7% (compared to 7.8% offered by more traditional methodologies), increases feature extraction accuracy by a margin of 14.3% and enhances signal-to-noise ratio significantly by an average of 9.6&#xa0;dB. Concurrently, classification accuracy has been improved from 85.1 to 93.4%, whereas anomaly detection precision has been upgraded from 81.7 to 90.2%. With an average latency within 35&#xa0;ms, MLAOA presents a lightweight and practical solution for real-time wearable and remote health systems, emphasizing the significance of incorporating machine learning with continuity principles for robust biosignal analysis.</p>

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Machine learning-based adaptive optimization algorithm for Lipschitz continuity in biosignal telemetry

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • J. Noorul Ameen,
  • M. Irshad Ahamed,
  • P. C. Senthil Mahesh,
  • Saahira Banu Ahamed

摘要

In biosignal telemetry, signal integrity must be maintained against noise, artifacts, and transmission disturbances, especially in real-time health applications. In this relation, the present paper proposes the Machine Learning-Based Adaptive Optimization Algorithm (MLAOA) that ensures Lipschitz continuity across the various biosignal types, namely ECG, EEG, and EMG, by providing dynamic adjustment of signal processing parameters. The Algorithm incorporates supervised learning for signal behavior modeling with reinforcement learning for real-time adaptation. Experimental results on benchmark datasets show that MLAOA reduces the mean Lipschitz constant deviation to 2.7% (compared to 7.8% offered by more traditional methodologies), increases feature extraction accuracy by a margin of 14.3% and enhances signal-to-noise ratio significantly by an average of 9.6 dB. Concurrently, classification accuracy has been improved from 85.1 to 93.4%, whereas anomaly detection precision has been upgraded from 81.7 to 90.2%. With an average latency within 35 ms, MLAOA presents a lightweight and practical solution for real-time wearable and remote health systems, emphasizing the significance of incorporating machine learning with continuity principles for robust biosignal analysis.