Digital Spectral Analysis With Applications Marple Pdf Download PORTABLE

Digital Spectral Analysis With Applications Marple Pdf Download

therefore, the objectives of this paper were (1) to generate 3d df maps using different autoregressive (ar) spectral estimation methods [with emphasis on selection of appropriate sampling rate and ar model order to estimate the df for each of those 2048 simultaneous unipolar noncontact intracardiac atrium electrograms (aegs)], (2) to compare the df between those ar spectral estimation techniques and (3) to compare them with the maps produced using fourier-based approach [ 15 ].

the qrs wave detection is an important step in heart rhythm analysis. it is usually based on the comparison of the qrs complex waveform with predefined reference waveforms. some of the most frequently used reference waveforms are the classic fiducial points or combinations of them. in this paper, we propose a new approach to detect the qrs complex based on a high-order statistics (hos)-based template matching method.

the ssa was then implemented to estimate the df from ncm signals. the ssa technique was validated and implemented in several previous studies including in the context of sinus rhythm [ 7, 13, 14, 15, 20, 24 ], af [ 7, 14, 15, 20, 24 ], and ventricular arrhythmias [ 21 ]. the results indicated that the ssa is a highly accurate technique for df estimation with a strong agreement (agreement in approximately 95% of cases) with the results of fft-based spectral analysis. additionally, a novel approach using the ssa has been proposed to calculate 3d df maps of af [ 15 ].

in this paper, the feasibility of applying digital spectral analysis (dsa) methods for the analysis of high-spatial-frequency vibration signals produced by human-made non-axisymmetric structures is investigated. the vibration signals were measured using a single-point displacement transducer on a building structure, where ground motion is measured by a sensor array. the dsa method used in this study was developed by the authors. the method uses a digital filter to separate the vibration signal into a sum of high- and low-frequency components. the high-frequency component is then transformed using a polynomial function. the dsa method allows the vibration signal to be transformed to a frequency domain, enabling a comparison of results with the results obtained using fft. the dsa method was applied to the vibration signal, and the results were compared with the results obtained using fft. the agreement between the dsa results and the fft results was generally good for both the low- and high-frequency components of the vibration signals.

second, we show that the ar-based spectral analysis provides a valid approach for monitoring the df in patients with persaf. we have compared the presence of df in patients with persaf (those patients with less than 30days) and those with nonpersaf (those patients with more than 30days). the df was assessed on a 3d-ar map by calculating the mean of the df from all the points from the 3d-ar map (fig. 6 ). the ar model-based df maps demonstrated that the df was present in all patients with persaf, but only in 7 of the 13 patients with nonpersaf. the mean ± sd of the df for all patients with persaf and patients with nonpersaf are 0.056 ± 0.024 hz and 0.019 ± 0.023 hz, respectively. we have also calculated the difference (δdf) of the df between the patients with persaf and nonpersaf (0.047 ± 0.037 hz). for the patients with persaf, the ar-based 3d-ar map shows good agreement between the ar-based and fft-based df maps ( fig. 7 ).
pseudo-trend analysis was carried out to assess the temporal stability of the df. we used the first 3df maps for each patient and calculated the mean df, sd and the coefficient of variation (cv) of the df over 3-5 days. the mean df for each patient was (0.033 ± 0.017 hz) for patients with persaf and (0.006 ± 0.017 hz) for patients with nonpersaf. the sd of the df for patients with persaf was (0.020 ± 0.012 hz) and (0.009 ± 0. the cv was (0.44 ± 0.21) for patients with persaf and (0.13 ± 0.10) for patients with nonpersaf. it is clear from the study that the df is stable over time in patients with persaf.
all of the patients that were included in this study had chronic af. we have tested the ability of ar model-based spectral analysis to track the df in patients with paroxysmal af (persaf) and patients with nonpersaf. the df was calculated in the patients with persaf and nonpersaf on the 3d-ar maps by calculating the mean of the df from all the points from the 3d-ar map. the mean df for the patients with persaf and nonpersaf are 0.051 ± 0.025 hz and 0.016 ± 0.017 hz, respectively. we have also calculated the difference (δdf) of the df between the patients with persaf and nonpersaf (0.045 ± 0.033 hz). for the patients with persaf, the ar-based 3d-ar map shows good agreement between the ar-based and fft-based df maps ( fig. 8 ).
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