The document discusses several medical applications of digital signal processing (DSP) including hearing aids, electroencephalograms (EEGs), and acquiring blood pressure signals. DSP techniques such as sampling, filtering, frequency analysis, and spectral estimation are used to process analog signals from the body, like brain waves or sound, into digital signals. This allows signals to be filtered and analyzed to extract clinically useful information for diagnosing conditions and monitoring patients.

1. MEDICAL APPLICATIONS OF
DSP
Made by-
Digisha Singhal 13bec026
Kinjal Aggrawal 13bec054
Guided by-
Dr. Mehul Naik

2. 1. Introduction
2. Applications
3. Hearing aid
4. Brain signals – electroencephalogram
5. The acquisition of a blood pressure signal
6. References

3.  Most natural phenomena occur continuously in time, such as the
variations of temperature of the human body, forces exerted by
muscles, or electrical potentials generated on the surface of the scalp.
 By sampling these analog signal, digital signals are formed.
 Major applications of DSP in the field of medicine are widely applied
in patient monitoring, diagnosis, as well as physiological investigation
and in some therapeutic settings (e.g. Muscle and sensory stimulation,
hearing aids).

4.  Hearing aid
 Brain signals – electroencephalogram
 The acquisition of a blood pressure signal

5.  The sound is picked up by the microphone, is converted into an
electric signal, and then digitized.
 The digital signal is then filtered to selectively amplify those
frequency bands in which the patient shows the most severe hearing
loss.
 Amplitude compression, in which the system gain is reduced when
the amplitude exceeds some pre-defined threshold values, in order
to avoid excessive loudness to the ear is also used.
 Finally the processed digital signal is converted back to analog form
in the digital-to-analog (D/A) converter, and delivered to the ear via
the earphone.

6. In a further stage of signal processing in hearing aids, the amplitude-range
may be compressed. Hence, the gain of the system is progressively reduced
when the sound-volume exceeds a specified level, as is the case near the
end of this recording. A small delay in adapting the gain to the signal-levels
may also be added, as the average of the most recent amplitudes drives the
gain control.

7.  The two basic, non-invasive, brain signals are the EEG.
 Both find use in routine neuro-physiological evaluation, for e.g. diagnosis
and following the development of brain diseases, and in monitoring
anaesthetised patients during medical operations.
 The EEG is the electrical activity of the brain and may contain diagnostic
information for a variety of neurological conditions, such as sleep disorders,
and coma.
 They are often described by the frequency band in which they lie ( e.g alpha
rhythm, beta activity, delta waves), and by their amplitude, and the pattern
of distribution on the scalp.

8. 4 second excerpt from as EEG

9.  The characteristics of the EEG vary considerably with the state of the
patient, e.g whether the patient's eyes are open or closed, whether
the patient is drowsy, soundly asleep, asleep and dreaming.
 The EEG lends itself to time and frequency analysis.

10. Segment of the original EEG signal (solid line), and the same signal
reconstructed from only its DFT components in the α frequency band
(dotted line). The oscillation in the frequency band of 8-13 Hz is
enhanced. The slow change during the artefact in the EEG signal around
29.5 s is eliminated, as are some of the faster variations (higher
frequency beta activity, and noise ) seen in the raw data.

11.  Spectral estimation: Spectral estimation helps in finding the pulse
rhythms present in the EEG signal. The short segment of EEG data is
analyzed for spectral parameters such as location and amount of
spectral energy. Wave-shaping filters are extensively used in this
technique. Wave shaping filters produce desired output signal for
given input signal.
 ARMA (autoregressive moving average) method: This model is
suggested for modelling signals with sharp peaks and valleys in their
frequency content and also signals with severe background noise.

12. Frequency response |f(H)| of a second-order Butterworth band-pass
filter, with cut-off frequencies at 8 and 13 Hz. This filter will preserve
activity in the alpha frequency band, while attenuating other frequency
bands.

13. Using and IIR filter with coefficients a=[1 -7.43 24.44 -46.33 55.38 -42.75
20.82 -5.84 0.72] and b =10-4 [0.12 0 -0.48 0 0.720 -0.48 0 0.12], very rapid
change and slow variations are eliminated, and only the signal
components in the alpha frequency band are maintained.
An IIR filter is used to
enhance the alpha activity in
an EEG signal that contains
mains interference (50 Hz),
and noise.

14.  A blood pressure (BP) signal contains clinically relevant components
up to about 20 Hz.
 The signal is contaminated by noise at the mains frequency (50 Hz)
and other noise (mainly below 50 Hz) may also be present
 Thus the sampling rate has to be above 100Hz, as the highest
frequency present is 50 Hz.
 If the noise (mains interference) is removed prior to sampling, by a
low-pass (anti-alias) filter with a cut-off frequency at say 20 Hz, the
sampling rate could be reduced to a value above 40 Hz, without
significant aliasing occurring.

15. The power spectrum of this signal, sampled at 67 Hz. Without the anti-alias filter
(dotted lines), the 50 Hz noise is aliased, and appears as a very large peak at 17 Hz (67-
50 Hz). With the anti-alias filter (solid line), this peak is very much reduced (though not
eliminated, due to the imperfection of the filter), and can be considered insignificant.

16.  H. Nyquist (1928) Certain Topics in Telegraph Transmission
Theory. Trans. AIEE, vol. 47, pp. 617-644.
 A. Antoniou. (1993) Digital Filters: Analysis, Design and
Applications. New York, NY: McGraw-Hill, 2nd Edition.
 L. B. Jackson. (1996) Digital Filters and Signal Processing. Boston,
MA: Kluwer, 3rd Edition.
 A. V. Oppenheim, and R. W. Schafer (1989). Discrete-Time Signal
Processing. Englewood Cliffs, NJ: Prentice Hall.
 S. K. Mitra. (2001) Digital Signal Processing: A Computer-Based
Approach. New York, NY: McGraw-Hill, 2nd Edition.