Prediction of instantaneous heart rate using adaptive algorithms

In this paper, adaptive filter based on adaptive algorithms like least mean square (LMS), normalised least mean square (NLMS) and recursive least square (RLS) are used for the prediction of instantaneous heart rate in ECG signal. The adaptive algorithms work on the principle of optimising the least square error by achieving wiener solution. The weights of the filter coefficients are changing, as per the changes in the signal. The performance of adaptive filter is measured by mean square error (MSE) and the prediction accuracy is observed by mean absolute error (MAE). The simulation results show that the adaptive algorithms NLMS and RLS have faster convergence rate with less number of iteration but the forecasting accuracy is higher in LMS compared to NLMS and RLS algorithms.