Adaptive Signal Processing Algorithms in Biomedical Devices for Remote Monitoring
DOI:
https://doi.org/10.64137/XXXXXXXX/IJETET-V1I1P102Keywords:
Adaptive signal processing, Biomedical devices, Remote monitoring, LMS algorithm, RLS filter, Kalman filter, IoT, Wearable biosensors, Signal-to-Noise Ratio (SNR), Healthcare technologyAbstract
Adaptive Signal Processing (ASP) algorithms have revolutionized remote health monitoring systems through integration into the biomedical device. With the pressure on global healthcare systems to deliver scalable, patient-centric monitoring frameworks, ASP algorithms can help transform how healthcare services are provided now and in the future. The algorithms adapt to different physiological conditions, suppress noise while extracting critical features in real-time and improve the accuracy and reliability of biomedical diagnostics. In this paper, current advancements and applications of ASP within remote biomedical monitoring are presented, and several adaptive filtering techniques (Least Mean Square (LMS), Recursive Least Squares (RLS), Kalman filters) are described. Wearable biosensors, IoT and ASP work in tandem to augment the capabilities of e-health systems to process physiological data continuously and in real time. A number of case studies (including ECG monitoring, EEG-based brain computer interfaces) are described, which demonstrate the practical utility of ASP in diagnosing cardiovascular anomalies, detecting epileptic seizures and monitoring of respiratory irregularities. This paper also assesses algorithmic performance in terms of convergence rate, efficiency via computational cost and signal-to-noise ratio (SNR). These discussions are supported by a literature review including recent results in medical engineering and signal processing. An experimental setup is proposed by the methodology section that uses simulated biomedical signals, the prototyping of hardware using Arduino and MATLAB-based signal analysis. Our results demonstrate significant improvement in noise suppression and anomaly detection over traditional signal processing techniques. The results support the protean promise of ASP in telemedicine and personalized medicine
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