As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field. The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CIas importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CIas potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.... also known as the gain of the circuit because it measures the amplification or attenuation of the output signal with respect to the input. ... 1997) that negative poles on the left-hand side of the s-plane give rise to a stable system or circuit, hence the RC circuit is stable and ... FIGURE 2.8 Circuit diagram for RC low-pass filter.
|Title||:||Computational Intelligence in Biomedical Engineering|
|Author||:||Rezaul Begg, Daniel T.H. Lai, Marimuthu Palaniswami|
|Publisher||:||CRC Press - 2007-12-04|