Synthetic Biometrics in Biomedical Systems

2015 ◽  
pp. 507-526 ◽  
Keyword(s):  
Biomedical Systems

Digital Image Processing Applied to Scintillation Images from Biomedical Systems

1977 ◽  
Vol BME-24 (4) ◽  
pp. 337-347 ◽  
Author(s):  
Panos P. Varoutas ◽  
Louis R. Nardizzi ◽  
Ernest M. Stokely
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Biomedical Systems

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

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