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: peak_det_advanced_pan_tompkins.m
    path of file: ./repos/OSET/under-development
    
    the code of the file until where you have to start completion: function [peaks, peak_indexes, R_prominence] = peak_det_advanced_pan_tompkins(data, fs, varargin)
%
% peak_det_modified_pan_tompkins - R-peak detector based on modified
%   Pan-Tompkins method. The filters and post-detection R-peak selection
%   logic differ from the original algorithm
%
%   [peaks, peak_indexes, width] = peak_det_modified_pan_tompkins(data, fs, wlen, fp1, fp2, th, ksigma, flag)
%
%   Inputs:
%       data: Vector of input data
%       fs: Sampling rate
%       wlen: Optional. Moving average window length (default = 150ms)
%       fp1: Optional. Lower cut-off frequency (default = 10Hz)
%       fp2: Optional. Upper cut-off frequency (default = 33.3Hz)
%       th: Optional. Detection threshold (default = 0.2)
%       ksigma: Optional. Saturates peaks at ksigma x STD of the signal after energy envelope calculation (default = 5)
%       flag: Optional. Search for positive (flag=1) or negative (flag=0) peaks.
%             By default, the maximum absolute value of the signal determines the peak sign.
%
%   Outputs:
%       peaks: Vector of R-peak impulse train
%       peak_indexes: Vector of R-peak indexes

    
function [peaks, peak_indexes, R_prominence] = peak_det_advanced_pan_tompkins(data, fs, varargin)
%
% peak_det_modified_pan_tompkins - R-peak detector based on modified
%   Pan-Tompkins method. The filters and post-detection R-peak selection
%   logic differ from the original algorithm
%
%   [peaks, peak_indexes, width] = peak_det_modified_pan_tompkins(data, fs, wlen, fp1, fp2, th, ksigma, flag)
%
%   Inputs:
%       data: Vector of input data
%       fs: Sampling rate
%       wlen: Optional. Moving average window length (default = 150ms)
%       fp1: Optional. Lower cut-off frequency (default = 10Hz)
%       fp2: Optional. Upper cut-off frequency (default = 33.3Hz)
%       th: Optional. Detection threshold (default = 0.2)
%       ksigma: Optional. Saturates peaks at ksigma x STD of the signal after energy envelope calculation (default = 5)
%       flag: Optional. Search for positive (flag=1) or negative (flag=0) peaks.
%             By default, the maximum absolute value of the signal determines the peak sign.
%
%   Outputs:
%       peaks: Vector of R-peak impulse train
%       peak_indexes: Vector of R-peak indexes
%
%   Revision History:
%       2023: First release
%
%   Reference:
%       Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans
%       Biomed Eng. 1985;32(3):230-236. doi:10.1109/TBME.1985.325532
%
%   Reza Sameni, 2008-2023
%   The Open-Source Electrophysiological Toolbox
%   https://github.com/alphanumericslab/OSET

% Set default values for optional arguments
if nargin > 2 && ~isempty(varargin{1})
    wlen = varargin{1};
else
    wlen = 0.02; % moving average window length 150ms
end