Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

Alimuradov, Alan K.; Tychkov, Alexander Yu.; Kuzmin, A. V.; Churakov, Pyotr P.; Ageykin, Alexey V.; Vishnevskaya, G. V.

doi:10.4018/ijertcs.2019010102

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…ICEEMDAN was proposed to resolve the issues of the spurious modes and the frequency aliasing as faced by the other EMD based techniques [28]. By adding white noise, ICEEMDAN realizes the frequency continuity among adjacent scales, which results in the weakening of frequency aliasing effect [35]. e calculation methodology of ICEEMDAN is given as follows:…”

Section: Improved Complete Ensemble Empirical Mode Decomposition Withmentioning

confidence: 99%

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

Sibtain

Saleem

2020

Advances in Meteorology

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The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.

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Section: Improved Complete Ensemble Empirical Mode Decomposition Withmentioning

confidence: 99%

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

Sibtain

Saleem

2020

Advances in Meteorology

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“…As for the endpoint effect, there are many methods [17][18][19] to suppress it. CEEMDAN is more effective in aspects such as mechanical failure detection [20][21][22][23][24][25] and model optimization [26][27][28]; Patricio Fuentealba et al [29] used CEEMDAN to assess the condition of the fetus during delivery; Yao and Liu [30] applied MPE to the identification of EEG signals; Hu et al [31] combined CEEMDAN with MPE to identify the state of ball mills under different loads; Wang et al [32] proposed an optimized filtering method that combines CEEMDAN with MPE. However, the completeness of the CEEMDAN algorithm itself needs to be strengthened further.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

He¹,

Wang²,

Gao³

et al. 2021

EESRJ

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Aiming at the serious noise of bridge vibration signals in complex environment, this paper proposed an adaptive filtering and denoising optimization method for bridge structural health monitoring. The method took CEEMDAN algorithm as the core, during the step-by-step decomposition of original signals, white noise with opposite signs was added in each stage, meanwhile multi-scale permutation entropy (MPE) was introduced to analyze the mean entropy of each intrinsic mode function (IMF) at different scales, and components with serious noise were eliminated to complete the first filtering; then, in order to optimize the remaining IMFs for signal reconstruction and ensuring the smoothness and similarity of filtering, an optimized reconstruction model was established to complete the second filtering. Compared with the CEEMDAN method, the proposed method could solve the problems of mode mixing and endpoint effect with good completeness, orthogonality, and signal-to-noise ratio. At last, the advantages and application value of the proposed method were verified again by the vibration signal analysis of a real long-span bridge structure.

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“…For instance, individuals with depression may speak with a monotone or reduced vocal inflection, reflecting a lack of energy or enthusiasm. In contrast, heightened pitch and vocal intensity may be observed in individuals experiencing heightened emotional arousal or anxiety[11,12]. • Articulation and Pronunciation: Changes in articulation and pronunciation may be present in individuals with certain mental health conditions.…”

mentioning

confidence: 99%

Harnessing Speech Technology for Mental Health Assessment and Detection

Bhat,

Kopparapu

2023

SMM23, Workshop on Speech, Music and Mind 2023

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One in eight people world wide live with some kind of mental disorder which results in significant disturbances in their thinking and emotional behaviour. While effective prevention and treatment options exist, early detection and intervention are essential for improving therapy outcomes and reducing the longterm impact of mental health conditions. Speech technology can be employed, effectively, to not only assess and monitor mental health conditions but also can contribute to the early detection of mental health. By analyzing speech patterns, including changes in speech rate, pitch, or other vocal features, algorithms can identify potential markers of conditions like depression, anxiety, or even neurodegenerative disorders such as Alzheimer's disease, dementia. Additionally, speech technology can provide insights into a person's emotional state, stress levels, facilitating proactive healthcare measures. In this paper, we present an overview of the speech processing technologybased research from the perspective of application to mental healthcare.

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Improved CEEMDAN Based Speech Signal Analysis Algorithm for Mental Disorders Diagnostic System

Cited by 11 publications

References 30 publications

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

An Adaptive Filtering Method for Bridge Vibration Signals Based on Improved CEEMDAN and Multi-Scale Permutation Entropy

Harnessing Speech Technology for Mental Health Assessment and Detection

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