Evaluation and Development of Methods for Time-frequency Analysis of Heart Rate Variability

Non-stationary signals are very common in nature, consider for example speech, music or heart rate. Using the concept of time-frequency analysis this thesis studies the performance of different time-frequency distributions of both simulated and real non-stationary signals. The signals studied are linear and non-linear frequency modulated (FM) signals. Two methods are studied to increase performance of the signals’ time-frequency distributions. Since lag-independent kernels perform well with slow varying frequency modulated signals both methods use these. One method uses filtering with compact support lag-independent kernels and the other uses a penalty function with multitapers corresponding to lag-independent kernels. These methods are then evaluated using two performance measures and the results are used to improve the timefrequency distributions of heart rate variability signals. The thesis suggests that both of these methods improve the time-frequency distribution of such signals.