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A Generic Method for Event Detection
Published in Mohamed Elgendi, PPG Signal Analysis, 2020
The bandpass filter consists of two filters, the low-pass filter and the high-pass filter. The low-pass filter is used to remove high frequency noise, and the high-pass filter is used to remove low frequency noise. Usually, a Butterworth filter is used due to its simplicity and is characterized by a magnitude response that is maximally flat in the passband and is monotonic overall. MATLAB provides low-pass and high-pass filters with the simple command butter(m, f,′low′) and butter(m, f,′high′), respectively, where m is the filter order and f is the normalized cut-off frequency. The purpose of this step is to retain the characteristics of the main events within the processed signal, remove the undesired noise, and make the main events more salient.
Analog Filter Synthesis
Published in Bogdan M. Wilamowski, J. David Irwin, Fundamentals of Industrial Electronics, 2018
Nam Pham, Bogdan M. Wilamowski
There are many different types of filters such as Butterworth filter, Chebyshev filter, inverse Chebyshev filter, Cauer elliptic filter, etc. The characteristic responses of these filters are different. The Butterworth filter is flat in the stop-band but does not have a sharp transition from the pass-band to the stop-band while the Chebyshev filter has a sharp transition from the pass-band to the stop-band but it has the ripples in the pass-band. Oppositely, the inverse Chebyshev filter works almost the same way as the Chebyshev filter, but it does have the ripples in the stop-band rather than the pass-band. The Cauer filter has ripples in both pass-band and stop-band; however, it has lower order [W02,KAS89]. The analog filter is a broad topic, and this chapter will focus more on the methodology of synthesizing analog filters only (Figures 26.1 and 26.2).
Analog filter design
Published in Alexander D. Poularikas, ®, 2018
An examination of Figure 10.2.2 shows that the Butterworth low-pass amplitude response approaches the ideal in the region of low frequencies and also in the region of high-value frequencies. However, it does not produce very good approximation in the neighborhood of the cutoff frequency (ω = 1). The Chebyshev low-pass filter possesses a sharper cutoff response than the Butterworth filter, but it also possesses amplitude variations within the pass-band. A number of general properties are: The oscillations in the pass-band have equal amplitudes for a given value of ε.The curves for n even always start from the trough of the ripple, whereas the curves for n odd always start from the peak.At the normalized cutoff frequency of 1, all curves pass through the same point.
Techniques to derive and clean acceleration and deceleration data of athlete tracking technologies in team sports: A scoping review
Published in Journal of Sports Sciences, 2022
Susanne Ellens, Kane Middleton, Paul B. Gastin, Matthew C. Varley
Most studies (92%) in this review did not mention using any cleaning techniques on the data. Of the studies using a cleaning technique, 48% used a low pass Butterworth filter. The low pass Butterworth filter passes signals with frequencies lower than a selected cut-off frequency and decreases signals (which are ideally noise) with frequencies higher than the cut-off frequency (Sinclair et al., 2013). The Butterworth filter requires a selected cut-off frequency, which will determine the amount of signal passed through the filter and noise blocked. This review found that the studies using a Butterworth filter used a range of cut-off frequencies between 0.02 and 15 Hz. The cut-off frequency can influence the data, but a rationale for selecting them is rare. The used rationale for selecting a cut-off frequency is visual inspection of residual analysis outputs and is based on a common technique used in biomechanics (Winter, 2009). The selection and influence of different filters and cut-off frequencies has been widely studied in the biomechanics literature (Day et al., 2021; Fazlali et al., 2020; Winter, 2009); however, limited research exists on the selection and influence of different filters and cut-off frequency on data obtained from athlete tracking technologies in team sports. Furthermore, as shown in this review, there is no rationale for using specific filters. As different data processing procedures (deriving and cleaning) on acceleration and deceleration data affect the resulting metrics (Winter, 2009), further research in this area is warranted.
Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design
Published in International Journal of Systems Science, 2018
Yihang Yang, Xiang Cheng, Junrui Cheng, Da Jiang, Shaojun Li
The Butterworth filter is a type of signal processing filter designed to have the maximal flatness in its frequency response in the pass band (Butterworth, 1930). The task of the Butterworth filter design problems is to solve proper values for the circuit components, with a reputation for optimising ‘impossible’ mathematical problems, because of a vast of local optima.