You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: detect_ecg_beats.m
    path of file: ./repos/ppg-beats/source
    
    the code of the file until where you have to start completion: function [agreed_beats_inds, qual] = detect_ecg_beats(ecg, fs, options, no_signal_vector)
% DETECT_ECG_BEATS  detects beats in ECG.
%   DETECT_ECG_BEATS detects beats in an electrocardiogram (ECG) signal
%   using two beat detectors, and uses the agreement between beat detectors to
%   assess the quality of beat detections.
%   
%   # Inputs
%   
%   * ecg : a vector of ECG values
%   * fs : the sampling frequency of the ECG in Hz
%   * options : (optional) a structure of options which determine the settings used for the analysis:
%    - options.win_durn      
    
function s_filt = elim_vlfs(s, filt_characteristics)
%% Filter pre-processed signal to remove frequencies below resp
% Adapted from RRest

%% Eliminate nans
s.v(isnan(s.v)) = mean(s.v(~isnan(s.v)));

%% Make filter
flag  = 'scale';
[N,Wn,BETA,TYPE] = kaiserord([filt_characteristics.Fstop filt_characteristics.Fpass]/(s.fs/2), [1 0], [filt_characteristics.Dstop filt_characteristics.Dpass]);
b  = fir1(N, Wn, TYPE, kaiser(N+1, BETA), flag);
AMfilter = dfilt.dffir(b);

%% Check frequency response
% % Gives a -3 dB cutoff at ? Hz, using:
% freqz(AMfilter.Numerator)
% norm_cutoff_freq = 4.435e-3;    % insert freq here from plot
% cutoff_freq = norm_cutoff_freq*(s.fs/2);

if length(s.v) > (length(AMfilter.numerator)-1)*3
    % - Tukey Window to avoid edge effects
    win_edge_durn = 0.5; % in secs
    prop_win = win_edge_durn*2/((length(s.v)-1)/s.fs);
    tw = tukeywin(length(s.v),prop_win); 
    s_filt.v = filtfilt(AMfilter.numerator, 1, s.v.*tw);
    s_filt.v = s.v-s_filt.v;
else
    s_filt.v = s.v;
end