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Sunday, May 12, 2013

DIGITAL SIGNAL PROCESSING IN BIOMEDICAL ENGINEERING


                    
   DIGITAL SIGNAL PROCESSING IN BIOMEDICAL ENGINEERING                                      


ABSTRACT :-

Digital Signal Processing, a field which has its root in the 17th & 18th century mathematics has become an important modern tool in a multitude diverse field of science & technology. The techniques & applications of this field are as old as Newton & Gauss & as new as digital computers & integrated circuits.
digital Signal Processing means processing signals in digital domain, which includes
# Modifying signal characteristics
# Multiplying two signals ( Modulation, Correlation)
# Filtering
# Averaging
DSP can extract one signal from another. The purpose of such processing may be to estimate characteristic parameter of a signal into a form which is in some sense more desirable. DSP can analyze ECG or EEG to extract some characteristic parameter.

Application of Signal processing in biomedical:
Signal processing in general has a rich history & its important is evident in such diverse fields as       
biomedical engineering.
# Echo cancellation
# Noise cancellation
# Spectrum Analysis
# Detection
# Correlation
# Filters
# Computer Graphics
# Image Processing
# Data Compression
# Machine vision
# Sonar
# Array Processing
# Guidance
# Robotics etc.

Signals:
Signals play an important role in our daily life. Examples of signals that we encounter frequently
are speech, music, picture & video signals.
A signal is a function of independent variable such as time, distance, position, temperature, pressure.
E.g.
1. A black & white picture is a representation of light intensity as a function of two     
      spatial  coordinator.
2. The video signal consists of a sequence of images, called frames and is a function    
      of three variables :two spatial coordinators and time.

Most signals we encounter are generated by natural means. However a signal; can also be generated synthetically or by a computer simulation.
A signal carries information.
The objective of the signal processing is to roughly speaking concerned with the mathematical representation of the signal and the algorithmic operation carried out on it to extract the information present.The representation of signal can be in terms of basis function in the domain of the original independent variable or it can be in terms of basis functions in a transformed domain. Likewise the information extraction process may be carried out in the original domain of the signal or in a transformed domain.

Characterization and classification of signals:
Depending on the nature of the independent variables and the values of the function defining the signal, various types of signal can be defined.
                                               
              Variable ---- 1.Continuous
                                                                     Function of Independent variable                
                                  2.Discrete
                                  
                                  Real Valued
                                                                     Function of Independent variable
                                  Complex valued
              
-       A signal can be generated by a single source or is called Scalar signal.
-       A signal can be generated by multi source or is called Vector signal (multi channel signal.)

Classification:

         Dimension: 1-D, 2-D, 3-D< Multi dimensional

         Amplitude, Waveform, Continuous Time, Discrete time
         Analog, Digital, Sampled Data, Quantized box case
         Deterministic, random.

Typical Signal Processing Operation:
Various types of signal processing operations are employed in practice. In case of analog signals, most signal processing operations are usually carried out in the time domain.
In the case of discrete time signals, both time domain and frequency domain operations are employed. In either case the desired operations are implemented by a combination of some elementary operation. These are also usually implemented in real time or near time, even though in certain application they may be implemented off time.

Elementary Time Domain Operations:
-       Scaling, Amplitude- attenuation
-       Integration
-       Differentiation
-       Delay- advance
-       Addition
-       Product

Filtering:
           One of the most widely used complex signals processing operation is filtering. Filtering is used to pass certain frequency components in a sign through the system without any distortion and to block other frequency components. The system implementing this operation is called as filter.
Pass band: The range of frequency that is blocked by the filter is called as stop band.
Various types of filters can be defined depending upon the nature of filtering operation
-       Low pass- High pass
-       Band pass- Band reject
-       Multi pass- Comb filter
-       Notch filter
-       Interference- Noise removal
Generation of Complex Signals:
     -     Real valued- Real signals
     -     Complex valued –Complex Signals
     -     All naturally generated signals are real valued signals
     -     In some applications it is desirable characteristics. A complex signal from                         
            real signal having more desirable characteristics. A complex signal can be
            generated from a real signal by employing a Hilbert Transformer.
Modulation and Demodulation
Multiplexing and Demultiplexing
Signal Generation
          An equally important part of signal processing is synthetic signal generation.

Examples of Typical Signal in Biomedical engineering:
We will now examine a couple of examples of some typical biomedical signals and their subsequent processing in typical application.

Electroencephalogram (EEG)
               Frequency Range: D.C- 100Hz (0.5 – 60Hz)   
               Signal Range         15- 100 mV

The summation of the electrical activity caused by the random firing of individual neutrons in the brain is represented by the EEG signal. In multiple EEG recordings, electrodes are placed at various position on the scalp with two common electrodes placed on the earlobes, and the potential difference between the various electrodes are recorded. An example of multiple EEG trace is shown in Fig(a)

Both frequency domain and time-domain analysis of the EFG signal have been used for the diagnosis of epilepsy, sleep disorders, psychiatric malfunctions, etc. To this end the EFG spectrum is subdivided into the following five bands.
                                   
                            Range                        Band
                        # Delta                     0.5 to 4 Hz
                        # Theta                    4 to 8 Hz 
                        # Alpha                    8 to 13 Hz
                        # Beta                     13 to 22 Hz
                        # Gamma                22 to 30 Hz

The delta wave is normal in the EFG signals of the children and the sleeping adults. Since it is not common in alert adult, its presence indicates certain brain disease. The theta wave is usually found even though it has been observed in alert adult. The alpha wave is common in all normal humans and is more pronounced in a relaxed and awake subject with closed eyes. Likewise, the beta activity is common in normal adults. The EFG exhibits rapid, low voltage waves, called rapid- eye- movement (REM) waves, in a subject dreaming during sleep. Otherwise, in a sleeping subject, the EFG contains bursts of alpha like waves, called sleep spindles. The EFG of epileptic patient exhibits various types of abnormalities, depending on the type of epilepsy that is caused by uncontrolled neural discharges.

Electrocardiogram ( ECG ) Signal :

            Frequency Range : 0.05 – 100 Hz.
            Signal Range:         10 micro V (Fetal) 5mV(Adult)
The electrical activity of the heart is represented by the ECG signal. A typical ECG signal; trace is shown in figure (b). The ECG trace is essentially a periodic waveform. One such period of ECG waveform is depicted in figure (c) represents one cycle of the blood transfer process from the heart to the arteries. This part of the waveform is generated by an electrical impulse originating at the sinoatrial node in the atrium of the heart. The impulse causes contraction of the atria, forcing the blood in each atrium to squeeze into its corresponding ventricle. The resulting signal is called P wave. The atrioventricular node delays the excitation impulse until the blood transfer from atria to ventricle is completed. The excitation impulse then causes contraction of the ventricle, squeezing the blood into the arteries and generating QRS part of the ECG waveform. During this phase the atria are relaxed and failed with blood. The T-wave of the waveform represents the relaxation of the ventricles. The complete process is repeated periodically generating ECG trace.
             
Each portion of ECG waveform carries various types of information for the physician analyzing a patient’s heart condition. For example, the amplitude and timing of the P and QRS portions indicate the condition of the cardiac muscle mass. Loss of amplitude indicates muscle damage, where as increase amplitude indicates abnormal heart rates. To long a delay in the atrioventricular node is indicated by a very T-R interval.
Likewise, blockage of some or all of the contraction impulses is reflected by intermittent synchronization between P- and QRS- phase. Most of these abnormalities can be treated with various drugs and observing the new ECG waveforms taken after the drug treatment can again monitor the effectiveness of the drug.

In practice, there are various types of externally produced artifacts that appear in the ECG signal. Unless these interference are removed, it is difficult for a physician to make a correct diagnosis. A common source of noise is the 50-Hz power line whose radiated electric and magnetic field induction. Other sources of ECG instruments through capacitive coupling and/or magnetic induction. Other sources of interference are the electromayographic signals that are potentials developed by contracting muscles. These and interference can be removed with careful shielding and processing techniques.

Electromayogram ( EMG)
                       
                        Frequency Range:  10-200 Hz
                        Signal Range:         Function of muscle activity and electrode placement   
Heart Rate:
                        Frequency Range:  45-200 beats/min
Blood pressure:    
                         Frequency Range:  D.C. -200 Hz (D.C.-60 Hz)
                          Signal Range:        40-300 mm of Hg. (arterial)
                                                           0-15mm of Hg (Venous)
Breathing rate:
                        Frequency Range:    12-40 beats/min

Biomedical signals come in all shapes and sizes.


WHY DSP?
Digital signal processing of an analog signal consists basically of three steps:                  
1. Conversion of the analog signals into a digital form.
2. Processing of the digital version.
3. Conversion of the processed digital signal into an analog form. Figure shows the overall scheme in a block diagram form.



 Advantages:
1. Digital circuits are less sensitive to tolerance of the component values and are fairly            
    independent of temperature, aging and most other external parameters.
2. The digital circuits are small in volume quantity and do not require any adjustments
    either during construction and later while in use.
3. Recent advances in VLSI ( Very large Scale Integrated circuits  ) , sophisticated
     integration of complex DSP systems on a single chip – Compactness.
4. Flexibility
5. Resource Sharing
6. Programmability
7. Simple implementation
8. Indefinite storage capability- Size &Time
9. Applicability.

Disadvantages:
1. Increased system complexity because of the need for additional pre and post
    processing devices such as A/D and D/A converters and filters and complex digital
    circuitery.
2. Limited range of frequency available for processing as fs >= 2f
3. Resolution and word length of register effects on the performance and specification of     
    the system.
4. Cost increases and the power consumption also.

However the advantages far outweigh disadvantages in various applications and with the continuing decreases in the cost of digital processor hardware, application of digital signal processing are increasing rapidly.

References:
(1)   Digital signal processing
                 By Sanjit  k. Mitra
      
      (2) Digital signal processing
                 By  Oppenheim &  Schafer
 
      (3) Biomedical digital signal processing
                  By  Wiellis  J.  Tompkins

(4)     www.dspguide.com 






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