I have an ECG signal and I want to separate it 4 second segments. How to do it in MATLAB?
% MATLAB Code
load ('C:\Users\Explorer\Documents\MATLAB\PhysioNet_Database\Malignant_Ventricular_Ectopy_Database\418m.mat');
signal=val(1,:);
plot(signal); xlabel('Time'); ylabel('Voltage');
418m.mat
The signal is downloaded from website of PhysioNet and is of MIT BIH Malignant_Ventricular_Ectopy_Database.
There are two signal in 418m.mat. I just want to separate first one signal.
Also, there is a function buffer() in MATLAB but it is only for equally spaced in time. I don't know whether this signal is equally spaced in time or not.
Related
I created a sinusoid with frequency 550Hz that goes for 1 second
fs=44100;
Duration=1; %second
Len=Duration * fs; %length of sinusoid
t=(0:Len-1)/fs;
x=sin(2*pi*550*t);
for the purpose of exploring and learning, I have decided to take the short time Fourier transform of this signal. I did it as below:
window_len=0.02*fs; %length of the window
hop=window_len/3; %hop size
nfft=2^nextpow2(window_len);
window=hamming(window_len,'periodic');
[S,f,t]=spectrogram(x,window,hop,nfft,fs);
Now I want to plot the real versus imaginary value of S for the frequency equal to 550 and see what happens. First of all, in the frequency vector I didn’t have the exact 550. There was one 516.5 and 559.6. So, I just looked at the spectrogram and chose whichever that was close to it and picked that. When I tried to plot real vs imaginary of S for the frequency I chose (over all time frames), the values all fall in 3 points as it shows in the attached plot. Why three points?
Each STFT window can have a different complex phase depending on how the start (or middle) of the window is synchronized (or not) with the sinusoids period. So the real-complex IQ plot for the peak magnitude DFT result bin can be a circular scatter plot, depending on the number of DFT windows and the ratio between the stepping distance (or length - overlap) and the period of the sinusoid.
The phase of the STFT coefficients for the different windows depends on which data exactly the window "sees". So for your particular choice of window length and hop, it so happens that as you slide through your single-frequency sinusoid, there only three different data chunks that you window "sees". To see what I mean, just plot:
plot(x(1:window_len),'x')
plot(x(1+hop:window_len+hop),'x')
plot(x(1+2*hop:window_len+2*hop),'x')
plot(x(1+3*hop:window_len+3*hop),'x')
.. and if you continue you will see that the pattern repeats itself, i.e., the first plot for instance is the same as the fourth, the second as the fifth etc. Therefore you only have three different real-imaginary part combinations.
Of course, this will change if you change the window length and the hopsize, and you will get more points. For instance, try
window_len =nfft;
hop=ceil(window_len/4)
I hope that helps.
I'm trying to segment the original signal using the DSP toolbox in Matlab Simulink. I'm using the Time scope to plot it. If the original signal has certain characteristics, I plot it. If not, I omit the signal segment. However, when I omit signals, the time scope stops the simulaiton entirely.
What I want to plot is:
original signal: |seg good1|seg bad1|seg bad2|seg good2|...
filtered signal: |seg good1|seg good2|seg good3|...
How to plot the filtered signal?
I want to take a wav. file and plot it in Time Domain and Frequency Domain on MATLAB.
I have this code thus far;
[y,fs]=wavread('FireAlarm');
%wavread=the function that is going to read the wav.file
%y=samples
%fs=sampling frequency
%'FireAlarm'=wav file
t=linespace(0,length(y)/fs,length(y));
%linespace=the function that is going to create the time vector
%0=start time
%length(y)/fs=ending time
%length(y)= number of samples in y
plot(t,y)
Nfft=1024;
%Nfft=the length of fft
f=linespace(0,fs,Nfft);
%f= frequency vector
%0=first frequency
%fs=ending frequency
%nfft=length of the frequency vector
G=abs(fft(y,Nfft));
%G= the fft of the samples y in 1024 points
figure;plot(f(1:Nfft/2),G(1:Nfft/2)
Before I can even finish typing the first section, MATLAB tells there is an undefined function or variable 'wavread'.
Anyone know why this may be?
I would like to use pwelch on a set of signals and I have some questions.
First, let's say that we have 32 (EEG) signals of 30 seconds duration. The sampling frequency is fs=256 samples/sec, and thus each signal has length 7680. I would like to use pwelch in order to estimate the power spectral density (PSD) of those signals.
Question 1:
Based on the pwelch's documentation,
pxx = pwelch(x) returns the power spectral density (PSD) estimate, pxx, of the input signal, x, found using Welch's overlapped segment averaging estimator. When x is a vector, it is treated as a single channel. When x is a matrix, the PSD is computed independently for each column and stored in the corresponding column of pxx.
However, if call pwelch as follows
% ch_signals: 7680x32; one channel signal per each column
[pxx,f] = pwelch(ch_signals);
the resulting pxx is of size 1025x1, not 1025x32 as I would expect, since the documentation states that if x is a matrix the PSD is computed independently for each column and stored in the corresponding column of pxx.
Question 2:
Let's say that I overcome this problem, and I compute the PSD of each signal independently (by applying pwelch to each column of ch_signals), I would like to know what is the best way of doing so. Granted that the signal is a 30-second signal in time with sampling frequency fs=256, how should I call pwelch (with what arguments?) such that the PSD is meaningful?
Question 3: If I need to split each of my 32 signals into windows and apply pwech to each one of those windows, what would be the best approach? Let's say that I would like to split each of my 30-second signals into windows of 3 seconds with an overlap of 2 seconds. How should I call pwelch for each one of those windows?
Here is an example, just like your case,
The results show that the algorithm indicates the signal frequencies just right.
Each column of matrix, y is a sinusoidal to check how it works.
The windows are 3 seconds with 2 seconds of overlapping,
Fs = 256;
T = 1/Fs;
t = (0:30*Fs-1)*T;
y = sin(2 * pi * repmat(linspace(1,100,32)',1,length(t)).*repmat(t,32,1))';
for i = 1 : 32
[pxx(:,i), freq] = pwelch(y(:,i),3*Fs,2*Fs,[],Fs); %#ok
end
plot(freq,pxx);
xlabel('Frequency (Hz)');
ylabel('Spectral Density (Hz^{-1})');
I am trying to find a way to create a graph that shows an audio wave and its time-frequency data (time on x-axis, wave energy and frequency data on y-axis). I have this code that does it on two separate plots:
[audio, fs] = wavread('audio.wav');
subplot(211)
spectrogram(audio,256,200,256,fs,'yaxis')
subplot(212)
wavEnergy=audio(:,1);
time=(1/fs)*length(wavEnergy);
t=linspace(0,time,length(wavEnergy));
plot(t,wavEnergy);
and now I need help with 2 things.
First, how do I get the spectrogram time to be in terms of seconds? Right now it graphs with an x-range of 0-340 (labeled 'time') and I know the clip is about 40s long (the other plot properly displays this).
Second, how do I plot them together? I know I can get a matrix from spectrogram but which array do I get from that matrix and how do I convert its timeframe to seconds?
EDIT:
First problem solved, but the graphs are still doing something strange - they both output about 40s of data, but the ranges of the graphs and the offsets of the data are different. The spectrogram goes from 0s-40s but the first .5s shows no data, and the wave plot goes from 0s-45s and the last 5s shows no data. How can I get the ranges and offsets to be the same?
EDIT 2:
I just needed to use axis tight; on both subplots
Aligning these two plots on the same time-base relies on determining the sampling frequency of your data. Based on the parameter you are passing to spectrogram, the sampling frequency is 1000 Hz. Based on your definition of time = (1/8000)*length(wavEnergy), the sampling frequency is 8000 Hz. These are not consistent. To get the audio sampling frequency from your wav file you can use [audio, fs] = wavread('audio.wav').