MEDICAL APPLICATIONS OF DSP

Made by-Digisha Singhal 13bec026Kinjal Aggrawal 13bec054

Guided by- Dr. Mehul Naik

Index

1. Introduction2. Applications3. Hearing aid4. Brain signals – electroencephalogram5. The acquisition of a blood pressure signal6. References

Introduction

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).

Applications

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

Hearing Aid

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.

Hearing Aid

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.

Brain signals – Electroencephalogram

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.

Brain signals – electroencephalogram

4 second excerpt from as EEG

Brain signals – electroencephalogram

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.

Brain signals – electroencephalogram

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.

Brain signals – electroencephalogram

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.

Brain signals – electroencephalogram

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.

Brain signals – electroencephalogram

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.

The acquisition of a blood pressure signal

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.

The acquisition of a blood pressure signal

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.

References

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.