There are several ways to design filters in MATLAB.
This design may do what you want:
Fs = 1E+4; % Sampling Frequency
Fn = Fs/2; % Nyquist Frequency
Wp = [0.1 0.5 100 110];
fsamp = Fs;
fcuts = Wp;
mags = [0 1 0];
devs = [0.05 0.01 0.05];
[n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,fsamp);
n = n + rem(n,2);
hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'scale');
figure(1)
freqz(hh, 1, 2^nextpow2(length(hh)), Fs)
figure(2)
freqz(hh, 1, 2^nextpow2(length(hh)), Fs)
set(subplot(2,1,1), 'XLim', [0 150]) % Zoom Frequency-Axis
set(subplot(2,1,2), 'XLim', [0 150]) % Zoom Frequency-Axis
figure(3)
freqz(hh, 1, 2^nextpow2(length(hh)), Fs)
set(subplot(2,1,1), 'XLim', [0 10]) % Zoom Frequency-Axis
set(subplot(2,1,2), 'XLim', [0 10]) % Zoom Frequency-Axis
The usual frequency content of a EKG is <=100 Hz, so a bandpass filter to eliminate high-frequency noise is a better choice than a highpass filter. You can also use a high-frequency cutoff of about 50 Hz for a normal EKG, but arrhythmias can increase the bandwidth to a maximum of about 100 Hz.
