Biomedical Signal Processing

Biomedical signals are observations of physiological activities of organisms, ranging from gene and protein sequences, to neural and cardiac rhythms, to tissue and organ images. Biomedical signal processing aims at extracting significant information from biomedical signals. With the aid of biomedical signal processing, biologists can discover new biology and physicians can monitor distinct illnesses. Decades ago, the primary focus of biomedical signal processing was on filtering signals to remove noise [1]–[6]. Sources of noise arise from imprecision of instruments to interference of power lines. Other sources are due to the biological systems themselves under study. Organisms are complex systems whose subsystems interact, so the measured signals of a biological subsystem usually contain the signals of other subsystems. Removing unwanted signal components can then underlie subsequent biomedicine discoveries. A fundamental method for noise cancelation analyzes the signal spectra and suppresses undesired frequency components. Another analysis framework derives from statistical signal processing. This framework treats the data as random signals; the processing, e.g. Wiener filtering [6] or Kalman filtering [7], [8], utilizes statistical characterizations of the signals to extract desired signal components. While these denoising techniques are well established, the field of biomedical signal processing continues to expand thanks to the development of various novel biomedical instruments. The advancement of medical imaging modalities such as ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET), enables radiologists to visualize the structure and function of human organs; for example, segmentation of organ structures quantifies organ dimensions [9]. Cellular imaging such as fluorescence tagging and cellular MRI assists biologists