Spectrum Analysis of Heart Rate Variability (HRV)

Heart Rate Variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. High frequency (HF) HRV signals (0.12-0.4 Hz), especially, has been linked to parasympathetic nervous system (PSNS) activity. Activity in this range is associated with the respiratory sinus arrhythmia (RSA). In this thesis, we are interested in the differences between the power of HF-HRV in lying down position and standing up position, using analysis the HRV signal in frequency domain. Four non-parametric window spectrum methods are introduced for estimating the Power Spectra Density (PSD). Then the power of HRV is calculated using a traditional bandwidth [0.12-0.4] as well as a designed new bandwidth [ f0 ± B ], where f0 is the corresponding peak frequency from RSA spectrum. From combing different methods with different bandwidths; the resulting estimated power are compared in two different positions in many ways. In the end, the hypothesis tests are constructed to check if there exists a significant difference in the mean and variance for different methods with for different bandwidths. Moreover, the robust methods are applied to analysis the HRV signal during some Yoga breathing exercises. All data in used are collected from a pre-designed experiment in February that held in IKDC in Lund

Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later

Improved parametrized multiple window spectrogram with application in ship navigation systems

In analyzing non-stationary noisy signals with time-varying frequency content, it's convenient to use distribution methods in joint, time and frequency, domains. Besides different adaptive data-driven time-frequency (TF) representations, the approach with multiple orthogonal and optimally concentrated Hermite window functions is an effective solution to achieve a good trade-off between low variance and minimized stable bias estimates. In this paper, we propose a novel spectrogram method with multiple optimally parameterized Hermite window functions, with parameterization which includes a pair of free parameters to regulate the shape of the window functions. The computation is performed in the optimization process to minimize the variable projection (VP) functional problem. The proposed parametrized distribution method improves TF concentration and instantaneous frequency (IF) estimation accuracy, as shown in experimental results for synthetic signals and real-life ship motion response signals. With the optimization of nonlinear least-squares approximation of the ship response signals, the Hermite spectra are centralized, and only up to 15 basis functions are sufficient for concentration improvement in the TF domain

A multiple window method for estimation of peaked spectra

A multiple window method for estimation of the peaked power density spectrum is designed. The method optimizes a filter function utilizing the Karhunen-Loeve basis functions of a known peaked spectrum as windows to reduce variance and bias in the locality of the frequency peak. For improving performance, a penalty function is used to suppress the sidelobes outside a given bandwidth. The improved windows are obtained as the solution of a generalized eigenvalue problem. The bias at the frequency peak is reduced due to matching windows, while the variance is decreased by averaging uncorrelated periodograms. The method is compared with the Thomson multiple window estimator as well as to a single Hanning window in a simulation