Biotechnology & Biomedical EngineeringBio-Medical Signal and Image Processing Lab →List Of Experiments

Bio-Medical Signal Processing Toolkit

It is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.

The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.

Statistical Parameters

To calculate the statistical parameters of any function or signal, first of all we need to acquire the signal. Here, we have generated a sine wave using the signal generator VI. The sine wave generated can be written as:
f(x) = sin(80πx)

The minimum and maximum values of the function are 1 and -1 respectively.
The mean of this signal over the time interval of 0.01 second is zero as the average value of sine wave over one complete cycle is zero. Hence, the average value over four cycles has to be zero.
The rms value of a sine wave is given by peak/√2. The peak value of the signal acquired here is . hence the rms value must be one divided by square root of 2 which is 0.707.
Since all the values ranging from -1 to 1 are present in the sine wave in one cycle, so the central value must be equal to zero.

Since a sine wave is symmetric in full cycle, the skewness is zero. It can also be observed that third moment of the sine wave is equal to zero in a full cycle and hence the skewness is equal to zero.
The fourth moment of the given function about the mean is equal to 0.375.
Hence, the kurtosis is given by fourth moment divided by square of variance which is equal to 1.5.

Note: There is a slight difference between the values obtained by mathematical calculation and those obtained by LabVIEW. This is because LabVIEW calculates the statistical parameters by taking discrete points in the signal, while mathematical calculation are done using equations of waveforms generated which are continuous in nature.

Sampling & Aliasing

While sampling of a data, the problem of aliasing can occur if the Nyquist-Shanon criterion is not fulfilled. In other words, if the sampling frequency is not more than or equal to twice of the signal frequency, the problem of aliasing may occur.

Consider for example: the signal frequency is 15 Hz while the sampling frequency is 20 Hz. Hence the ration of sampling to signal frequencies becomes 1.333. The problem of aliasing occurs which is shown by the fourier transform of original signal and that of the sampled signal, which don’t overlap
Consider for example: the signal frequency is 10 Hz while the sampling frequency is 50 Hz which makes the ratio of sampling to signal frequency equal to 5. Hence, the problem of aliasing doesn’t occur, which is shown by the overlap of fourier transform of the original signal and that of the sampled signal.

Convolution & Correlation

Consider for example: we have taken two signals whose convolution has to be performed. The two signals are:

f(t) = 5 sin (10πt)    0 ≤ t ≤ 1

g(t) = sin (10πt)      0 ≤ t ≤ 1

To perform convolution, we reverse the signal g(t) and replace the variable t by 𝜏 and shift the function g(𝜏) by t.

Thus, the convolution of f and g is written as:

The limits of integration will have four cases:
1. t < 0
in this case, the two signals don’t overlap and hence the result of convolution is zero.
2. 0 ≤ t ≤ 1
in this case, the two signals overlap and the limits of integration vary from 0 to t.
3. 1 ≤ t ≤ 2
in this case, the two signals overlap and the limits of integration vary from t-1 to 1.
4. t > 2
in this case, the two signals don’t overlap and hence the result of convolution is zero. Now, taking the two integrals and putting the limits according to the above discussion, we get: (f*g)(t) = 0 at t=0 (f*g)(t) = 1.25 at t= 1.5 s (f*g)(t) = -2.5 at t= 1 s (f*g)(t) = 1.25 at t= 1.5 s (f*g)(t) = 0 at t = 2 s .

Power Spectrum

For a given signal, the power spectrum gives a plot of the portion of a signal's power (energy per unit time) falling within given frequency bins. The most common way of generating a power spectrum is by using a discrete Fourier transform, but other techniques such as the  maximum entropy method can also be used.


Histograms are used to plot density of data, and often for density estimation: estimating the  probability density function of the underlying variable. The total area of a histogram used for probability density is always normalized to 1. If the length of the intervals on the x-axis are all 1, then a histogram is identical to a relative frequency plot.

Fast Fourier Transform:

Since the fourier transform gives the information about the frequency component of any signal, it is used for finding out what are the frequencies present in the signal if the signal is stationary i.e. the frequency component of the signal is not changing with time.

Discrete cosine transform

A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, from lossy compression of audio and images (where small high-frequency components can be discarded), to spectral methods for the numerical solution of  partial differential equations. The use of cosine rather than sine functions is critical in these applications: for compression, it turns out that cosine functions are much more efficient (as explained below, fewer are needed to approximate a typical signal), whereas for differential equations the cosines express a particular choice of boundary conditions.


Hilbert Transform

In mathematics and in signal processing, the Hilbert transform is a linear operator which takes a function, u(t), and produces a function, H(u)(t), with the same domain. The Hilbert transform is named after David Hilbert, who first introduced the operator in order to solve a special case of the Riemann–Hilbert problem for holomorphic functions. It is a basic tool in Fourier analysis, and provides a concrete means for realizing the conjugate of a given function or Fourier series. Furthermore, in harmonic analysis, it is an example of a singular integral operator, and of a Fourier multiplier. The Hilbert transform is also important in the field of signal processing where it is used to derive the analytic representation of a signal u(t).

The Hilbert transform was originally defined for periodic functions, or equivalently for functions on the circle, in which case it is given by convolution with the Hilbert kernel. More commonly, however, the Hilbert transform refers to a convolution with the Cauchy kernel, for functions defined on the real line R (the boundary of the upper half-plane). The Hilbert transform is closely related to the Paley–Wiener theorem, another result relating holomorphic functions in the upper half-plane and Fourier transforms of functions on the real line.


In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time).


Infinite impulse response (IIR) is a property of signal processing systems. Systems with this property are known as IIR systems or, when dealing with filter systems, as IIR filters. IIR systems have an impulse response function that is non-zero over an infinite length of time.
This is in contrast to finite impulse response (FIR) filters, which have fixed-duration impulse responses. The simplest analog IIR filter is an RC filter made up of a single resistor (R) feeding into a node shared with a single capacitor (C). This filter has an exponential impulse response characterized by an RC time constant.

IIR filters may be implemented as either analog or digital filters. In digital IIR filters, the output feedback is immediately apparent in the equations defining the output. Note that unlike FIR filters, in designing IIR filters it is necessary to carefully consider the "time zero" case in which the outputs of the filter have not yet been clearly defined.


To implement the lowpass FIR filter, we choose Lowpass from the ‘Filter Type’ menu. The sampling frequency and lower cut off frequency are set in accordance with the Nyquist criterion.


A low-pass filter is a filter that passes low-frequency signals but attenuates (reduces the amplitude of) signals with frequencies higher than the cut-off frequency. The actual amount of attenuation for each frequency varies from filter to filter. It is sometimes called a high-cut filter, or treble cut filter when used in audio applications. A low-pass filter is the opposite of a high-pass filter, and a band-pass filter is a combination of a low-pass and a high-pass.


A high-pass filter, or HPF, is an LTI filter that passes high frequencies well but attenuates (i.e., reduces the amplitude of) frequencies lower than the filter's cut-off frequency. The actual amount of attenuation for each frequency is a design parameter of the filter. It is sometimes called a low-cut filter or bass-cut filter.


A band-pass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outside that range. An example of an analogue electronic band-pass filter is an RLC circuit (a resistor–inductor–capacitor circuit). These filters can also be created by combining a low- pass filter with a high-pass filter.


In signal processing, a band-stop filter or band-rejection filter is a filter that passes most frequencies unaltered, but attenuates those in a specific range to very low levels. It is the opposite of a band- pass filter.

Median Filter

In signal processing, it is often desirable to be able to perform some kind of noise reduction on an image or signal. The median filter is a nonlinear digital filtering technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing because under certain conditions, it preserves edges while removing noise
Consider an input sine wave of frequency 30 Hz and amplitude 5V is given to a median filter. The filtered output comes out to be a modified sine wave whose peak has been clipped. Thus, the median filter removes the sharp peak of the signal making it a smooth signal.

Data Compression

Consider for example: An input sine wave of frequency 30 Hz and amplitude 5V is given to a median filter. Let the reduction factor be 25. This means at a time, 25 data points are taken and the average is taken. Again, this point is plotted and the next 25 data points are considered and the average is taken. This process goes on repeating itself to get the compressed data.
The value -1.00978 specifies the instantaneous mean of the current reduction factor size.