Biomedical Signal Processing
Biomedical Signal Processing - Related Videos
09:37
Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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Cited by 5 •
2015
Attention control comprises enhancement of target signals and attenuation of distractor signals. We describe an approach to measure separately but concurrently, the neurophysiology of attending and ignoring in sustained intermodal attention, utilizing a passive control condition during which neither process is continuously engaged.
10:18
Fragmenting Bulk Hydrogels and Processing into Granular Hydrogels for Biomedical Applications
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Cited by 23 •
2022
This work describes straightforward, adaptable, and low-cost methods to fabricate microgels with extrusion fragmentation, process the microgels into injectable granular hydrogels, and apply the granular hydrogels as extrusion printing inks for biomedical applications.
05:07
Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
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Cited by 20 •
2018
The practical application and performance of methods for generation of representative tissue samples of porcine animal models for a broad spectrum of downstream analyses in biobank projects are demonstrated, including volumetry, systematic random sampling, and differential processing of tissue samples for qualitative and quantitative morphologic and molecular analyses types.
09:56
Fabrication of Mechanically Tunable and Bioactive Metal Scaffolds for Biomedical Applications
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Cited by 11 •
2015
Bioactive and mechanically reliable metal scaffolds have been fabricated through a method which consists of two processes, dynamic freeze casting for the fabrication of porous Ti, and coating and densification of the Ti scaffolds. The densification process is simple, effective and applicable to the fabrication of functionally graded scaffolds.
09:22
Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
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Cited by 4 •
2015
Two- and three-dimensional superhydrophobic polymeric materials are prepared by electrospinning or electrospraying biodegradable polymers blended with a lower surface energy polymer of similar composition.
FAQs
Filtering separates unwanted noise from signal components that carry physiologically relevant information. Processing must reduce interference without removing patterns needed for interpretation, because the resulting signal supports later feature extraction, classification, or event detection. This makes preprocessing a central engineering step before researchers draw conclusions from biomedical measurements.
Time-domain analysis examines how a signal changes over time, whereas frequency-domain analysis characterizes its frequency components. Using either representation, or both, helps analysts identify patterns that may be difficult to recognize in a raw digital recording. The chosen representation affects which aspects of physiological activity become visible for interpretation, classification, or quantitative study.
Feature extraction converts processed signals into measurable characteristics that summarize relevant patterns. Classification assigns those patterns to categories, while event detection identifies occurrences within a recording. These operations serve different purposes: features describe signal content, classification organizes it, and detection locates notable events. Together, they support interpretation and decision-making in biomedical research and healthcare.
A typical workflow begins by sensing physiological activity and converting it into digital data. Processing then reduces noise, isolates relevant patterns, and applies time- or frequency-domain analysis. The workflow may continue with feature extraction, classification, or event detection, depending on the objective. Its outputs support interpretation, measurement, or healthcare decision-making rather than merely storing the signal.
In electrocardiography and electroencephalography, processing helps turn recorded physiological signals into analyzable information. In patient monitoring, the same general approach supports ongoing measurement and interpretation of body activity. The signal source differs across these applications, but each depends on acquiring digital data, reducing noise, and identifying patterns that help characterize physiological function or inform healthcare decisions.
Engineering provides the computational and mathematical framework for designing how biomedical measurements are acquired, cleaned, analyzed, and interpreted. This perspective is important in medical-device development, where signal handling is integrated into a measurement system, and in quantitative physiology, where processed data support systematic study of bodily function. The approach connects sensor output with usable biomedical information.