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ecg signal enhancement in matlab

Enhancement of ECG Signal
ECG signal is usually corrupted by 50 Hz power line frequency and their first and
second harmonis. Hum noise is created by poor power supplies, transformers or electro
magnetic interference sourced by a main power supply is characterized by a frequency of
50 Hz and its harmonics. If this noise interfers with a desired audio or biomedical signal
(e.g., electrocardiography [ECG]), the desired signal is not useful for diagnosis purpose.
To eliminate these unwanted signal frequencies, it is desired suitable filtering process. In
most practical applications, elimination of the 50 Hz hum frequency with its second and
third harmonics is sufficient. This filtering process can be achieved by cascading with
digital notch filters having notch frequencies of 50 Hz and 100 Hz respectively. Further it
is needed to eliminate DC drift and muscle noise, which may occur at approximately 40
Hz or more. Hence a bandpass filter with pass band frequency 0.25-40 Hz is desired. The
following figure dipicts the functional block diagram.
Functional Block diagram of cascaded Notch filters
(a) Load, display and manipulating of ECG signals
(b) Design notch filters both in FIR and IIR for 50 Hz and 100 Hz respectively.
© Design a band pass filter to eliminate muscle noise.
(d) Plot the spectrum of the designed filters.
(e) Display the corrupted and filtered ECG signals and their spectrum.
Task1: Load an ECG signal. add a 50 Hz, and 100 Hz frequency sinusoidal signals to
this ECG signals. Plot the original ECG signal, corrupted ECG signal and their spectrum.
Task2: Design two notch filters with the following specifications.
Dr. M. Venu Gopala Rao, Professor, Dept. of ECE, KL University, A.P., India.
Notch Filters:
Frequencies to be suppressed : 50 Hz and 100 Hz.
3 dB bandwidth for each filter : 4 Hz.
Sampling rate : 600 Hz
Type of Notch filter : Second order IIR filter
Design methods : (a) FIR and (b) IIR using Pole zero placement.
Bandpass Filter:
Passband frequency range : 0.25 – 40 Hz.
Passband ripple : 0.5 dB
Sampling rate : 600 Hz
Filter type : Chebyshev fourth order
Design method : Bilinear transformation
Step1: Design a second order FIR notch filter.
Step2: Design a second order pole-zero notch filter.
In both cases choose the gain
so that
| | ω 1
ω  0 . Write the mathematical
equations for both transfer function and its difference equations in each case.
Step3: Plot the spectrums of each notch filters and cascaded filters.
Task3: Design a fourth order digital IIR band pass filter using bi-linear transformation
with Chebyshev approximations. The specifications are given above. Plot the original,
corrupted and filtered ECG signals and their spectrum
The current trend of ECG monitoring techniques to be more ambulatory and less annoying usually comes at the expense of the decrease in signal quality. To improve this quality, consecutive ECG complexes can be averaged in the heart beats, taking advantage of the quasi-periodicity of the ECG. However, this averaging constitutes a trade-off between the improvement of the SNR and the loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this document, a sequential average filter is developed which, in essence, adaptively varies the number of complexes included in the average as a function of the characteristics of the ECG signal. The filter is in the form of an adaptive Kalman filter. The adaptive estimation of the process noise and measurement covariance is done by maximizing the Bayesian evidence function of the sequential ECG estimation and exploiting the spatial correlation between several ECG signals recorded simultaneously, respectively. The noise covariance estimates thus obtained allow the filter to be able to attribute more weight to the newly arrived data when these data contain morphological variability, and to reduce this weight in cases of morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To measure the relevance of adaptive noise covariance estimation, the performance of the filter is compared to that of a Kalman filter with optimized noise covariance (a posteriori). This comparison shows that, without using a priori knowledge of the characteristics of the signal, the filter with adaptive noise estimation behaves in a similar way to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where information is not available. a priori or where signal characteristics are expected to fluctuate

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