Rademics Research Institute

Abstract

The emergence of smart biosignal processing systems has revolutionized the landscape of remote health monitoring and diagnostics by enabling real-time, non-invasive, and personalized assessment of physiological states. These systems integrate advanced sensing technologies, signal processing algorithms, and artificial intelligence to capture and interpret complex biosignals such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), and photoplethysmograms (PPG). A critical challenge lies in mitigating the effects of noise, artifacts, and variability inherent in biosignal acquisition, particularly in dynamic, real-world environments. This chapter provides a comprehensive overview of state-of-the-art preprocessing techniques, including time-domain filtering, frequency-domain spectral analysis, and wavelet-based denoising, with a focus on their real-time applicability in edge and wearable devices. The role of machine learning and adaptive filtering in enhancing noise resilience and diagnostic accuracy is discussed, along with the integration of empirical and independent component analysis for artifact rejection. The chapter explores system-level considerations such as edge computing architectures, sensor interoperability, data privacy, and ethical compliance. Case studies and practical insights are presented to demonstrate the utility of these systems in managing chronic diseases, facilitating early diagnosis, and improving healthcare accessibility. By addressing existing technical and translational challenges, this work highlights future directions for building robust, scalable, and clinically reliable smart biosignal systems in the era of digital healthcare.

Introduction

The rapid expansion of digital healthcare technologies has brought transformative changes in how physiological data is collected, processed, and utilized for diagnosis and patient care [1]. Smart biosignal processing systems lie at the core of this evolution, enabling continuous, real-time acquisition and analysis of physiological signals from the human body [2]. These systems leverage wearable and ambient sensors to capture a variety of biosignals such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), and photoplethysmograms (PPG) [3]. These signals provide critical insights into the functioning of cardiac, neurological, muscular, and vascular systems, respectively [4]. With the growing demand for accessible and preventive healthcare, especially in aging populations and resource-constrained regions, smart biosignal processing technologies offer the potential to extend diagnostic capabilities beyond clinical environments and into homes, workplaces, and remote settings [5].

Biosignals, by nature, are prone to various forms of interference and distortion [6]. The quality of acquired signals is often affected by motion artifacts, electromagnetic interference, electrode placement inconsistencies, and physiological variabilities [7]. These noise factors can mask critical diagnostic features, leading to misinterpretation or delayed interventions. Hence, robust preprocessing techniques form the foundation of any smart biosignal system. Time-domain filters are employed to suppress slow drifts and sudden spikes, while frequency-domain approaches isolate and eliminate components such as power-line interference or muscle noise [8]. In addition, wavelet transform-based methods allow multi-resolution analysis, which is particularly effective in handling transient and non-stationary noise patterns [9]. The integration of such techniques ensures that the extracted signal remains reliable and clinically useful, regardless of environmental or operational uncertainties [10].