China
Africa
Explore chapters and articles related to this topic
Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
Komal Jindal, Rahul Upadhyay, Prabin Kumar Padhy, Hari Shankar Singh
The time-frequency based analysis of time-series is carried out using decomposition of the signal using different time-frequency transforms viz. Continious Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT). Among other time-frequency transforms, the DWT is capable of extracting morphological information such as spikes, slow waves, and sharpness of the signal [17]. The TQWT is an extension of DWT, that provides various tunable parameters over DWT to obtain the desired time-frequency response [18]. Various parameters needed to be adjusted while implementing TQWT are Q-factor (Q), the number of decomposition levels (j), and the oversampling rate (r) [19]. Q-factor (Q) defines the number of wavelet oscillations, and r limits unnecessary ringing without affecting the shape of the waveform [20,21].
Sleep Stage Classification Using DWT and Dispersion Entropy Applied on EEG Signals
Published in Varun Bajaj, G.R. Sinha, Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021
Rajeev Sharma, Sitanshu Sekhar Sahu, Abhay Upadhyay, Rishi Raj Sharma, Ajit Kumar Sahoo
There are several methods for analyzing non-stationary signals like biomedical, earthquake, financial, and mechanical signals, etc. The wavelet transform is considered a powerful method for decomposing non-stationary signals into different scales, and provides a time-scale representation [38]. The wavelet transform is used for resolving the fixed time-frequency resolution problem of the short-time Fourier transform (STFT) [39]. The wavelet transform consists of two properties, namely space and frequency localization and multi-resolution analysis. Theoretically, the wavelet transform is broadly classified as the continuous wavelet transform (CWT) and the discrete wavelet transform (DWT). The CWT of signal y(t) is computed as follows [40]: YCWTτ,s=∫−∞∞ytψτ,s*tdt
Frames and Wavelets: A New Perspective on Sampling Theorems
Published in Ahmed I. Zayed, Advances in Shannon’s Sampling Theory, 2018
From the standpoint of signal analysis, the wavelet transform is a mathematical technique that can be used to split a signal into different frequency components and then studies each component with a resolution matched to its scale, thus providing a very good frequency and spatial resolution. Unlike other techniques used to study signals in the time-frequency domain, such as the windowed Fourier transform, in the wavelet transform the analyzing wavelets have time-width adapted to their frequency: high frequency wavelets are very narrow, while low frequency wavelets are much broader. This is in contrast with the windowed Fourier transform, where the analyzing signals all have the same envelope function, but translated to the proper time location and filled with higher frequency oscillations. This adaptability property of wavelets is especially useful for certain classes of signals, e.g., voiced speech signals as the energy is concentrated at lower frequencies, while the higher frequencies contain very little energy.
Optimization of Discrete Anamorphic Stretch Transform and Phase Recovery for ECG Signal Compression
Published in IETE Journal of Research, 2021
R. Thilagavathy, B. Venkataramani
A number of techniques for transform methods are reported in the literature. Among the transform methods, the wavelet transform has good time–frequency localization. The wavelet-based compression algorithm yields better performance. For the compression using Wavelet Transform, techniques such as thresholding the subband coefficients based on their energy packing efficiency ([3] and [4]) and iterative thresholding till a fixed percentage of wavelet coefficients zeroed [5] are reported. Most of the direct and transform-based algorithms assume the ECG signal to be sampled uniformly. Techniques for data compression using non-uniform sampling which enable the sampling of the ECG signal at a sub-Nyquist rate without causing aliasing are reported in the literature [6]. This includes Compressive sensing (CS) [7–10], [11] and Optimum Sparsity Order Selection (OSOS) [12] which enable the sampling of analog signals at sub-Nyquist rates. Compressive Sensing has been applied in real-time ECG compression [13] on resource-constrained sensors [14]. The wavelet-based CS is reported in [15]. A CS-based block sparse Bayesian learning is proposed in [16] and [17] for the reconstruction of fetal ECG and EEG signals.
Performance Evaluation of Discrete Wavelet Transform, and Wavelet Packet Decomposition for Automated Focal and Generalized Epileptic Seizure Detection
Published in IETE Journal of Research, 2021
N. J. Sairamya, M. Joel Premkumar, S. Thomas George, M. S. P. Subathra
As wavelet transforms are widely used in biomedical engineering areas for elucidating a diverse real-life issue. The physiological signals with irregular patterns are mostly analysed by wavelet transform. In wavelet transform, the window size is variable, hence it efficiently represents a time domain EEG signal in time–frequency domain. Long time and short time windows are used to acquire a better low frequency and high frequency resolution of the signal. Therefore, wavelet transform gives precise frequency information along with the time information at low and high frequencies, respectively. Further, several wavelet functions can be used for the analysis of EEG signals. Hence, in this work the wavelet transform methods are employed for the classification of normal, generalized and focal epileptic seizures.
A Novel Fault Distance Estimation Method for Voltage Source Converter-Based HVDC Transmission Lines
Published in Electric Power Components and Systems, 2021
Aleena Swetapadma, Shobha Agarwal, Ashish Ranjan, Almoataz Y. Abdelaziz
Wavelet transform is a computation tool for analyzing signals and decomposing the signals to different levels of resolution [21]. The wavelet transform has been used in power system transient analysis form last two decades. There are basically two types of wavelet transform, continuous wavelet transforms (CWT) and discrete wavelet transforms (DWT). DWT of a signal can be calculated by passing the signal through filters. Signal is passed through low pass filter with impulse response l and decomposed simultaneously using high pass filter h. The output will be approximate coefficients and detail coefficients. First level of decomposition of the signal u[t] can be expressed as in Eqs. (1) and (2):