Commentary - (2025) Volume 12, Issue 1
Received: 02-Jan-2025, Manuscript No. bset-25-168432;
Editor assigned: 04-Feb-2025, Pre QC No. P-168432;
Reviewed: 18-Jan-2025, QC No. Q-168432;
Revised: 23-Jan-2025, Manuscript No. R-168432;
Published:
30-Jan-2025
, DOI: 10.37421/2952-8526.2025.12.237
Citation: Petrova, Anya. "Advanced Signal Processing Techniques in Wearable Biomedical Sensor Devices." J Biomed Syst Emerg Technol 12 (2025): 237.
Copyright: © 2025 Petrova A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
One of the most fundamental aspects of signal processing in wearable devices is noise reduction. Physiological signals such as Electrocardiograms (ECG), Electromyograms (EMG), and Photoplethysmograms (PPG) are often contaminated by motion artifacts, power-line interference, and ambient light fluctuations. To address this, various filtering techniques are employed, including low-pass, high-pass, band-pass, and notch filters, often designed using Finite Impulse Response (FIR) or Infinite Impulse Response (IIR) methods. More advanced techniques such as adaptive filtering, which adjusts filter coefficients dynamically based on signal characteristics, are particularly useful in wearable applications where the noise profile can change rapidly. For example, adaptive least mean squares (LMS) filters can effectively remove muscle noise from ECG signals in real time, enhancing signal clarity without distorting the underlying biological data.
Beyond basic filtering, time-frequency analysis techniques such as the Short-Time Fourier Transform (STFT), Wavelet Transform, and Empirical Mode Decomposition (EMD) allow for the examination of non-stationary biomedical signals. These methods are critical in detecting transient events like arrhythmias, epileptic spikes, or sleep stages. Wavelet Transform, in particular, is highly favored in wearable ECG and EEG devices due to its ability to localize features in both time and frequency domains. EMD, on the other hand, decomposes signals into intrinsic mode functions, providing an adaptive representation without requiring a predefined basis function. These techniques facilitate feature extraction for machine learning classifiers and anomaly detection algorithms, thereby supporting intelligent diagnostic and monitoring functions in wearable systems [2].
Another pivotal area is data compression and efficient transmission, especially in scenarios involving continuous, high-frequency data streams like EEG or multi-channel ECG. Wearable devices often operate under constraints of power, bandwidth, and storage. Hence, lossless and lossy compression techniques such as Huffman coding, Run-Length Encoding (RLE), and transform-based compression (e.g., Discrete Cosine Transform, DCT) are used to reduce data volume without significant loss of diagnostic information. Additionally, compressed sensing a technique that reconstructs signals from fewer samples than traditional methods has gained popularity in wearable systems. It allows energy-efficient data acquisition by reducing the sampling rate and computational load, which is particularly useful for long-term monitoring and real-time applications.
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