Xⅇⅈω
X
ω
and
Hⅇⅈω
H
ω
, compute
Yⅇⅈω
Y
ω
Yⅇⅈω=∑n=-∞∞ynⅇ-ⅈωn=∑n=-∞∞∑k=-∞∞xkhn−kⅇ-ⅈωn=∑k=-∞∞xk∑n=-∞∞hn−kⅇ-ⅈωn
Y
ω
n
y
n
ω
n
n
k
x
k
h
n
k
ω
n
k
x
k
n
h
n
k
ω
n
(1)
Let
m=n−k→n=m+k
m
n
k
n
m
k
=∑k=-∞∞xk∑m=-∞∞hmⅇ-ⅈωm+k=∑k=-∞∞xkⅇ-ⅈωk∑m=-∞∞hmⅇ-ⅈωm
k
x
k
m
h
m
ω
m
k
k
x
k
ω
k
m
h
m
ω
m
(2)
Yⅇⅈω=XⅇⅈωHⅇⅈω
Y
ω
X
ω
H
ω
(3)
x*h
↔
DTFT
XH
x h
↔
DTFT
X
H
(4)
ecg
- voltages are very small (
μV
μ
V
!) therefore there is plenty of measurement
noise
- use DSP to reduce noise
- simple processing: low pass filtering
Measurement process takes the
TRUE
SIGNAL
and
corrupts it with additive
NOISE
so that
the actual received signal is
SIGNAL+NOISE
This is
the signal we actually measure. It is "noisy".
Signal is in low frequencies and noise
is spread over all frequencies
We need a lowpass filter to
suppress noise.
Try a simple averaging
filter
h[n]
h[n]
is a moving average
- smooths the data
-
h[n]
h[n]
is a LOWPASS FILTER
We see this behavior in
Hω
H
ω
. Also as
2M+1
2
M
1
increases, the cutoff frequency
ω
1
=π2M+1
ω
1
2
M
1
decreases which leads to more smoothing.
LOWPASS FILTERING = SMOOTHING = AVERAGING
Bottom
Line:
Figure 9
looks much better than
Figure 6
- Interpret the filtering action in terms of
hn
h
n
(time/sample domain) and
Hω
H
ω
(frequency domain)
- What price did we pay?
Can we improve on the frequency
response of the moving average filter?
ECG also picks up plenty of 60Hz
interference
How would you combat this interference using an LTI filter?
- electrocardiogram:
measure electrical
signal given off by the heart.