Estimation Of Frequency Components Research Articles

A robust QRS detection approach using stationary wavelet transform

Accurate QRS detection is crucial for reliable ECG signal analysis and the development of automatic diagnosis tools. In this paper, we propose a simple yet efficient new algorithm for QRS detection using the Stationary Wavelet Transform (SWT). The wavelet transform has been extensively exploited for QRS detection and proved to be an efficient mathematical tool for scale analysis; it provides good frequency components estimation for the input signal and has good localization capability. The proposed procedure exploits solely the first level approximation coefficients of the wavelet transform applied to the bandpass-filtered ECG signal. Therefore, it resulted in a reduced complexity algorithm compared to the existing methods which use many decomposition levels. Thresholding has been implemented using the Pan-Tompkins procedure which is known to be very powerful. Our approach has been assessed over the MIT/BIH benchmark database, the MIT noise stress test database for noise robustness evaluation and the European ST-T database. The obtained results show competitive performance with state-of-the-art algorithms. The proposed scheme achieved a sensitivity of 99.83%, a positive predictivity of 99.94% and a detection error rate of 0.228% using Lead I MIT-BIH Database, this performance is one of the best results over this benchmark, and 99.35% of sensitivity, 99.76% of positive predictivity and detection error rate of 0.9% using the European ST-T Database, hence, our algorithm achieved high performance on Holter environment. Using the MIT noise stress test database, our algorithm achieved 98.77% of sensitivity, 91.01% of positive predictivity, and 10.12% of DER. Thus, our algorithm is robust and outperforms state-of-the-art algorithms on noisy recordings.

Simplified Welch Algorithm for Spectrum Monitoring

Power Spectral Density (PSD) is an essential representation of the signal spectrum that depicts the power measurement content versus frequency. PSD is typically used to characterize broadband random signals and has a variety of usages in many fields like physics, engineering, biomedical, etc. This paper proposes a simple and practical method to estimate the PSD based on the Welch algorithm for spectrum monitoring. The proposed method can be easily implemented in most of software-based systems or low-level Field-Programmable Gate Arrays (FPGAs) and yields a smooth overview of the spectrum. The original Welch method utilizes the average of the amplitude squared of the previous Fast Fourier Transform (FFT) samples for better estimation of frequency components and noise reduction. Replacing the simple moving average with a weighted moving average can significantly reduce the complexity of the Welch’s method. In this way, the amount of required Random Access Memory (RAM) is reduced from K (where K is the number of FFT packets in averaging) to one. This new method allows users to adjust the dependency of the PSD on the previous observed FFTs and its smoothness by setting only one feedback parameter without any hardware change. The obtained results show that the algorithm gives a clear spectrum, even in the noisy situation because of the significant Signal to Noise Ratio (SNR) enhancement. The trade-off between spectrum accuracy and time convergence of the modified algorithm is also fully analysed. In addition, a simple solution based on Xilinx Intellectual Property (IP), which converts the proposed method to a practical spectrum analyzer device, is presented. This modified algorithm is validated by comparing it with two standard and reliable spectrum analyzers, Rohde & Schwarz (R&S) and Tektronix RSA600. The modified design can track any signal type as the other spectrum analyzers, and it has better performance in situations where the power of the desired signal is weak or where the signal is mixed with the background noise. It can display the spectrum when the input signal power is 5 dB lower than the visible threshold level of R&S and Tektronix. In both narrowband and wideband scenarios, the new implemented design can still display frequency components 5 dB higher than the noise, while the output spectrum of other analyzers is completely covered by noise.

A frequency estimation method for two adjacent frequencies

The frequency-domain estimation methods are estimate frequency from main-lobe. One shortcoming of these methods lies in the fact that they are not suited to two adjacent frequency components. When the distance between two frequency components is less than 3 DFT bins, former methods are subject to severe spectral interference, which results in considerable errors of estimate. In this paper, a discovery is made that side-lobes have relatively smaller spectral interference as compared to main-lobe, for which a novel estimation method based on the first side-lobes is proposed. As demonstrated by simulation, the proposed method performs better in two adjacent frequency components estimation. When the distance between two frequency components reaches 1.5 bins, the maximum estimation error is approximately 0.07 bins for the two frequencies.

Identification and Tracking of Physiological Parameters from Skin using Video Photoplethysmography.

In recent years, there has been a growing interest in video Photoplethysmography (vPPG), a technique able to estimate cardiovascular parameters from video recordings of the skin. Despite the growing interest in vPPG technology, there are still problems in extracting the correct waveform of blood volume pulse, mainly due to real world artifacts, such as changes in light condition and movement artifacts. Another important issue is the correct definition of skin against background. Therefore, we propose an algorithm of skin detection that is able to recognize skin pixels solid to variations of luminosity. We recorded the signals of interest during an experimental protocol designed to provide thermal stimulation and observe the resulting Autonomic Nervous System changes. Experimental data were gathered from 10 young healthy subjects (age: 21±2 years). Video recordings are processed using a band-pass filter and then an automatic algorithm of peak detection is applied to detect the pulse wave peaks, then used to estimate heart rate variability (HRV). The efficiency and stability of the algorithm are compared against finger-PPG waveforms. Preliminary results show an overall statistical agreement between time and frequency domain indexes. However, further efforts are required to improve the estimation of frequency components, particularly during rest.

Application of Recursive Least Squares Algorithm With Variable Forgetting Factor for Frequency Component Estimation in a Generic Input Signal

Signal estimation is important for protection, system study, and control purposes. This paper deals with the application of a modified recursive least squares algorithm, based on a variable forgetting factor, for estimation of frequency components in a generic input signal. Application areas such as synchronization and control in three- and single-phase systems for various grid conditions as well as estimation of harmonic and subsynchronous components will be discussed. Advantages of the proposed method over the available techniques in terms of estimation speed, frequency selectivity, and noise rejection capability will be described. Finally, simulation and experimental results will be used to evaluate the dynamic performance of the proposed method.

Spectral Analysis and Mitigation of GPS Multipath Error Using Digital Filtering for Static Applications

Multipath is one of the prominent error sources in Global Positioning System (GPS), which adversely affects the accuracy of the user’s position. This paper describes the estimation of frequency components of multipath error signal using spectral analysis and its effective mitigation using time-varying digital Alters. Four types of 2nd order Alters, namely, Butterworth, Type I and II Chebyshev and Elliptic filters, are examined for mitigation of multipath and their performance is compared. It is observed that by applying digital filters of different cut-off frequencies over the spectrum of the multipath, one can significantly reduce the multipath errors. The designed filters are applied to GPS data acquired from a typical urban environment site and found that Butterworth filter reduced the error most effectively. To extract the magnitude of error contribution in low and medium multipath frequency ranges, its mitigation is carried out with Butterworth filter. The performance of the filter is validated by the spectral analysis for multipath signals before and after filtering process. However, this spectral analysis followed by digital filtering is different from the approaches reported elsewhere in the open literature. This new approach is very useful in identifying least-affecting multipath sites for GPS applications.

Precise recognition of warhead and decoy based on components of micro-Doppler frequency curves

From the view of electromagnetic scattering, it is indicated that the micro-Doppler (m-D) characteristics of an extended target undergoing micro-motions are actually induced by the change of incident directions of radar pulses. Different micro-motions may lead to similar change of incident directions, consequently inducing similar m-D characteristics. To tackle this problem, rather than distinguish warhead and decoy directly from m-D characteristics, the frequency components of m-D frequency curves are used as a new characteristic for recognition in this paper. To get high precision of frequency components estimation, model-based parameter estimation (MBPE) is utilized to extract the m-D frequency curves from TFR. To obtain high accurate simulation results, the backscattered signal simulation is conducted by full-wave numerical method. The simulation results validate the theoretical analysis and the high performance of the proposed method.

Estimation of frequency components in stator current for the detection of broken rotor bars in induction machines

The limitation of data window length in induction machine broken rotor bar diagnostics is a real challenge in practice. Sideband frequencies which are used as broken rotor bar indicators are very close to the fundamental frequency and have low magnitude. Traditional spectral analysis approach such as Discrete Fourier Transform (DFT) can be inaccurate in these conditions due to its inherent drawbacks such as the requirement of long data window for high resolution and the side lobe leakage in frequency domain. In this paper, a high-resolution spectral analysis technique, Prony Analysis (PA), is proposed for broken rotor bar detection in induction machines. The method is described and demonstrated in detail, validated by experimental data, and compared with DFT. Results clearly indicate the advantages of PA over DFT in terms of maintaining a high resolution with a much shorter window and a better frequency estimate accuracy with the same window length.

A continued fraction analysis of periodic wavelet coefficients

We define and prove the existence of crossings of wavelet coefficients translated by integer multiples of the numerator of a continued fraction convergent of the ratio of the sampling interval to the period of the wavelet coefficients. Crossings are found to be translation invariant ±1. Intervals between crossings are analyzed for wavelets with n vanishing moments. These wavelets act as multiscale differential operators. These crossings reveal different locations in the period where there is equality in the nth derivative of an averaging of the signal. These results will be employed in the estimation of frequency components in future publications.