Adaptive Hybrid Signal Processing for Real-Time Patient Monitoring and Diagnosis
AUTHORS
Sabah Mohammed,Professor of Computer Science, Lakehead University, Canada
ABSTRACT
Advances in digital health have underscored the importance of accurate and efficient biomedical signal processing to support patient monitoring and early diagnosis. Conventional methods, while effective in controlled settings, often face limitations when confronted with noisy, dynamic, and heterogeneous physiological data encountered in real-world healthcare environments. This study introduces an adaptive hybrid signal processing framework designed to meet the demands of real-time patient monitoring and clinical decision support. The proposed approach integrates traditional signal analysis techniques with intelligent machine learning models, enabling robust noise reduction, feature extraction, and diagnostic classification. The framework leverages multi-level preprocessing for artifact suppression, followed by hybrid feature extraction that combines domain-specific methods with data-driven learning to capture both physiological relevance and statistical significance. Adaptive feedback mechanisms continuously refine classification models, ensuring reliable performance across varying patient conditions. Experimental evaluations were conducted using publicly available biomedical datasets, with additional validation on prototype wearable healthcare devices. Results indicate that the hybrid framework consistently improves diagnostic accuracy, reduces false alarm rates, and enhances real-time responsiveness compared to baseline signal processing methods. The system demonstrates strong applicability for mobile health solutions, telemedicine platforms, and clinical monitoring systems, where efficiency and reliability are critical. By bridging traditional signal processing with adaptive computational intelligence, this work advances the development of patient-centered healthcare technologies. The findings suggest that hybrid methodologies offer a promising pathway for scalable, resource-efficient, and clinically meaningful innovation in digital health.
KEYWORDS
Hybrid signal processing, Real-time patient monitoring, Biomedical signal analysis, Adaptive algorithms, Digital healthcare systems
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