By Eugene N. Bruce

A biomedical engineering point of view at the idea, tools, and purposes of sign processing. This publication presents a special framework for figuring out sign processing of biomedical indications and what it tells us approximately sign assets and their habit in line with perturbation. utilizing a modeling-based procedure, the writer indicates the best way to practice sign processing through constructing and manipulating a version of the sign resource, delivering a logical, coherent foundation for spotting sign varieties and for tackling the designated demanding situations posed through biomedical signals-including the consequences of noise at the sign, alterations in easy homes, or the truth that those signs comprise huge stochastic parts and will also be fractal or chaotic. every one bankruptcy starts with an in depth biomedical instance, illustrating the tools below dialogue and highlighting the interconnection among the theoretical techniques and functions.

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4 Consider the following signals and decide which components of each are information-bearing biomedical signals or noise. State your reasons for your choices: a. A new biomedical sensor continuously measures the glucose level in the blood. b. A set of ECG electrodes records the electrical activity of the heart and the electrical activity of respiratory muscles of the chest. c. An ultrasound beam is detected by an ultrasound microphone after the beam reflects off a solid tumor. d. A microelectrode implanted in the motor cortex of a monkey records action potentials from many neurons, but especially from one neuron that becomes active during a reaching movement with the right hand.

An intuitive understanding of this property can be obtained by noting that in applying Eq. 26) to z(r) there will be a term involving the product x(t)y(r) which will not appear in either R,(s) or R,,(s). If this term happens to evaluate to zero (and it does in some important cases), then the autocorrelation finction of r(t)will be the sum of the autocorrelation hnctions of x(t) and f i t ) . 5 THE CONCEPT OF AUTOCORRELATION 47 ical signals are not so constrained. 27) For s = 0, the right-hand side of Eq.

The autocorrelation operation is not necessarily linear. That is, if z(t) = x(r) + At),then R&) # R,(s) + Ry(s)necessarily. An intuitive understanding of this property can be obtained by noting that in applying Eq. 26) to z(r) there will be a term involving the product x(t)y(r) which will not appear in either R,(s) or R,,(s). If this term happens to evaluate to zero (and it does in some important cases), then the autocorrelation finction of r(t)will be the sum of the autocorrelation hnctions of x(t) and f i t ) .

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