MATLAB: How to calculate an exponentially weighted moving average on a conditional basis

Hi, I am using MATLAB R2020a on a MacOS. I have a signal 'cycle_periods' consisting of the cycle periods of an ECG signal on which I would like to perform an exponentially weighted mean, such that older values are less weighted than newer ones. However, I would like this to be done on an element-by-element basis such that a given element is only included in the overall weighted mean if the weighted mean with the current sample does not exceed 1.5 times or go below 0.5 times the weighted mean without the element.

I have used the dsp.MovingAverage function as shown below to calculate the weighted mean, but I am really unsure as to how to manipulate the function to include my conditions.

% Exponentially weighted moving mean for stable cycle periods
    movavgExp = dsp.MovingAverage('Method', 'Exponential weighting', 'ForgettingFactor', 0.1);
    mean_cycle_period_exp = movavgExp(cycle_periods);

I would very much appreciate any help regarding this matter, thanks in advance.

%Let t be a 1-D array of data points lambda = 0.1; %forgetting factor prevWeightFactor = 0; %initialise weight factor and moving average prevAvg = 0; for i = 1 : length(t) presentWeightFactor = lambda * prevWeightFactor + 1; presentAvg = (1 - (1/presentWeightFactor)) * prevAvg + (1/presentWeightFactor) * t(i); if (presentAvg < 0.5 * prevAvg) || (presentAvg > 1.5 * prevAvg) presentAvg = prevAvg; %ignore this input, you might want to skip this step for the first sample else %accept this input in the moving average prevWeightFactor = presentWeightFactor; prevAvg = presentAvg; end end

Best Answer

Hi Cai,

In order to accomodate your custom conditions, you may think about creating a small script to mimic the algorithm used by the dsp.MovingAverage system object (exponential wieghing method). A very detailed explanation about the same is given in this link. I have created a dummy code for reference:

I hope this helps!