Abstract
One of the key aspects of biomedical signal processing is feature extraction. In this chapter, feature extraction techniques are categorized into time domain, frequency domain, time and frequency domain, and signal decomposition approaches. Brief introductions to some advanced signal analysis techniques such as nonnegative matrix factorization, sparsity, and compressive sensing techniques are covered in this chapter. The role of these advanced techniques becomes crucial in understanding continuous long-term biomedical signals gathered using wearable devices. Handcrafted features will help in designing explainable machine learning and also edge AI applications in a connected healthcare context.
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