A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

Guo, Tiantian; Zhang, Tongpo; Lim, Eng Gee; López-Benítez, Miguel; Ma, Fei; Yu, Limin

doi:10.1109/access.2022.3179517

Cited by 98 publications

(15 citation statements)

References 208 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One should consider the usability scenarios of the runtime optimization techniques presented in this paper when using the last-hour practical applications of wavelets , as those presented in [9] and [10].…”

Section: Discussionmentioning

confidence: 99%

Optimized use of Wavelet Packet Trees for the analysis of electrical waveforms

Nicolae

Cîrstea

et al. 2022

ITM Web Conf.

View full text Add to dashboard Cite

Wavelet packet trees represent a topic which grows in popularity when it comes to analysis of electrical waveforms. It allows for time-frequency analysis providing information on narrower ranges of frequency (as compared to the faster Discrete Wavelet Decomposition), but the computational resources are significantly greater than that involved in other types of wavelet-based analysis. In order to allow for this type of analysis to be usable in real-time applications, that is – to reduce the runtime, original algorithms were conceived and tested. In the first part of this work, previously implemented algorithms are briefly described, along with their pros and cons. Afterward, a new runtime optimization algorithm is proposed. Details on data structures, workflow, tests and study of errors are provided. This algorithm diminishes with up to 59% the runtime required by the application of the superposition theorem in order to evaluate the contribution of clustered harmonics using WPT.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimized use of Wavelet Packet Trees for the analysis of electrical waveforms

Nicolae

Cîrstea

et al. 2022

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…The function of the window function in the Fourier transform is performed by the main wavelet functions in the wavelet transform. There are many main wavelets with different properties and uses (Guo T. et al, 2022, Suhail et al, 2020.Some main wavelet functions (Daubechies (Db), Symlets (Sym), Coiflets (Coif)) used in wavelet transform are given in Figure 2. In this study, Db 2 wavelet was preferred because of the similarity of instantaneous peaks in seizure EEG data.…”

Section: 2feature Extractionmentioning

confidence: 99%

Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures

TUNCER¹

2022

Int. J. Res. Publ. Rev.

View full text Add to dashboard Cite

Epileptic seizures are caused by disturbances in the electrical activity of the brain. Failure to correctly classify epileptic forms may result in inappropriate treatment. Activities occurring prior to ictal activity may be causal and require further investigation. Therefore, it is important in the diagnosis of epilepsy to distinguish between ictal and interictal EEG using Electroencephalography (EEG) signs. In this study, ictal (absence seizure) and interictal EEG recordings were scored using 4 bipolar (C3-P3, T5-O1, FP2-F8, C4-T4) channel EEGs from Temple University Hospital (TUH) EEG Seizure Corpus (TUSZ) data. The data were divided into 3-second epochs and various features were obtained from the data. The data in each epoch were filtered using the Discrete Wavelet Transform (DWT) Daubechies-2 wavelet and were 0-32 Hz. range has been studied. Feature selection was made with Correlation based Feature Selection (CFS). The performances of traditional and deep learning classifier algorithms (Support vector machine (SVM), Long Short-term Memory (LSTM)) were compared and the results were discussed. The highest success rate was 96.96% in 84.86 seconds with the LSTM classifier model and 96.36% in 0.05 seconds with the SVM classifier algorithm

show abstract

“…WNN (Guo et al 2022) works based on signal processing. This study chooses the WNN method to pre-process the meteorological inputs by removing noise through wavelet decomposition, thereby enhancing the simulated streamflow accuracy over traditional ANN.…”

Section: Wnn (Wavelet Neural Network)mentioning

confidence: 99%

Machine learning algorithms for streamflow forecasting of Lower Godavari Basin

2022

View full text Add to dashboard Cite

The present study applies three Machine Learning Algorithms, namely, Bi-directional Long Short-Term Memory (Bi-LSTM), Wavelet Neural Network (WNN), and eXtreme Gradient Boosting (XGBoost), to assess their suitability for streamflow projections of the Lower Godavari Basin. Historical data for 39 years of daily rainfall, evapotranspiration, and discharge are used, of which 80% were for the model training and 20% for validation. A Random Search method is used for hyperparameter tuning. XGBoost performs better than WNN, and Bi-LSTM with an R2, RMSE, NSE, and PBIAS of 0.88, 1.48, 0.86, and 29.3% during training, with corresponding values of 0.86, 1.63, 0.85, and 28.5%, respectively, during validation indicate consistency. Therefore, it is used further for projecting streamflow from a climate change perspective. Global Climate Model, Ec-Earth3 is used because of its potentiality, as observed from previous studies. Four Shared Socioeconomic Pathways (SSPs) are considered. Downscaling of future climate variables is based on Empirical Quantile Mapping. Eight decadal streamflow projections are computed – D1 to D8 (2021–2030 to 2091–2099) – exhibiting more pronounced changes within the warming range. They are compared with three historic time horizons of H1 (1982–1994), H2 (1995–2007), and H3 (2008–2020). The highest daily streamflow is observed in D1, D3, D4, D5, and D8 in SSP245; these are D6 and D7 in SSP585 as per XGBoost analysis.

show abstract

A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities

Cited by 98 publications

References 208 publications

Optimized use of Wavelet Packet Trees for the analysis of electrical waveforms

Optimized use of Wavelet Packet Trees for the analysis of electrical waveforms

Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures

Machine learning algorithms for streamflow forecasting of Lower Godavari Basin

Contact Info

Product

Resources

About