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: qt_estimator_sum_of_gaussians.m
    path of file: ./repos/OSET/matlab/tools/ecg/qt-interval
    
    the code of the file until where you have to start completion: function fiducials = qt_estimator_sum_of_gaussians(ecg, fs, varargin)
% qt_estimator_sum_of_gaussians - Estimates Q-T interval and related parameters using ECG data.
%
% Syntax:
%   fiducials = qt_estimator_sum_of_gaussians(ecg, fs)
%   fiducials = qt_estimator_sum_of_gaussians(ecg, fs, params)
%

    
function fiducials = qt_estimator_sum_of_gaussians(ecg, fs, varargin)
% qt_estimator_sum_of_gaussians - Estimates Q-T interval and related parameters using ECG data.
%
% Syntax:
%   fiducials = qt_estimator_sum_of_gaussians(ecg, fs)
%   fiducials = qt_estimator_sum_of_gaussians(ecg, fs, params)
%
% Inputs:
%   ecg: Matrix of multichannel ECG data, where each row corresponds to a channel.
%   fs: Sampling frequency of the ECG data.
%   params (optional): A structure containing parameter settings:
%       w1: Window length of the first median filter used in baseline removal. Default: 0.75
%       w2: Window length of the second median filter used in baseline removal. Default: 0.9
%       f_hr: Heart rate factor for peak detection. Default: 1.2
%       amp_th: Amplitude threshold for peak detection. Default: 0.6
%       polarity_detection_mode: Polarity detection mode for peak detection. Default: 2
%       initial_q: Initial values for Q wave segments of interest. Default: [-0.080; -0.020]
%       initial_t: Initial values for T wave segments of interest. Default: [0.1; 0.5]
%       BayesianEstimation: perform Bayesian estimation or not (true/false).
%           Saves processing load if only ML estimation is required.
%           Default: true
%       snr: Signal-to-noise ratio for Bayesian approach. Default: 100
%       beta: Constant coefficient multiplied by the Q/T wave widths for
%           Q-T wave width determination. Default: 3. Can be a scalar such as
%           beta = 3 or a construct with beta.q = 2.1 and beta.t = 2.8
%       widest_qt: widest QT-interval. QT estimates beyond this threshold
%           are set to nan. Default: .6 seconds
%       earliest_q_onset: earliest Q-wave onset relative to the R-peak.
%           Q-wave onsets and offsets are both set to nan, if the Q onset
%           or offsets are estimated to be earlier. Default: 0.15 seconds
%       latest_t_offset: latest T-wave offset relative to the R-peak.
%           T-wave onsets and offsets are both set to nan, if the T onset
%           or offsets are estimated to be later. Default: 0.4 seconds
%       plot_results: Whether to plot results. Default: false
%
% Outputs:
%   fiducials: A structure containing various fiducial points and intervals
%       estimated using both ML and Bayesian approaches. The fiducials
%       structure contains the following fields, which are vectors of the
%       same length as the number of detected R-peaks:
%       - R_peaks: Detected R peak positions for each channel.
%       - QT_ML: QT intervals estimated using the Maximum Likelihood approach for each channel.
%       - Q_onsets_ML: Q wave onset positions estimated using the Maximum Likelihood approach for each channel.
%       - Q_offsets_ML: Q wave offset positions estimated using the Maximum Likelihood approach for each channel.
%       - T_onsets_ML: T wave onset positions estimated using the Maximum Likelihood approach for each channel.
%       - T_offsets_ML: T wave offset positions estimated using the Maximum Likelihood approach for each channel.
%       - QT_BYS: QT intervals estimated using the Bayesian approach for each channel.
%       - Q_onsets_BYS: Q wave onset positions estimated using the Bayesian approach for each channel.
%       - Q_offsets_BYS: Q wave offset positions estimated using the Bayesian approach for each channel.
%       - T_onsets_BYS: T wave onset positions estimated using the Bayesian approach for each channel.
%       - T_offsets_BYS: T wave offset positions estimated using the Bayesian approach for each channel.
%
% Reference:
%   Fattahi, Davood, and Reza Sameni. "CramÃ©r-Rao Lower Bounds of
%   Model-Based Electrocardiogram Parameter Estimation." IEEE Transactions on
%   Signal Processing 70 (2022): 3181-3192.
%
% Revision History:
%   2023: First release
%
% Davood Fattahi and Reza Sameni 2023
% The Open-Source Electrophysiological Toolbox
% https://github.com/alphanumericslab/OSET

% Handle optional input arguments
if nargin > 2 && ~isempty(varargin{1})
    params = varargin{1};
else
    params = struct;
end