New Phase Difference Measurement Method for Non-Integer Number of Signal Periods Based on Multiple Cross-correlations

Sun, Xiaofeng; Shen, Ting’ao; Mu, Xiangjing

doi:10.2174/1874444301507011537

Cited by 3 publications

References 6 publications

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Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Abbaszadeh¹,

Teixeira²,

Yagoub³

2021

TOBEJ

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Background: Because about 30% of epileptic patients suffer from refractory epilepsy, an efficient automatic seizure prediction tool is in great demand to improve their life quality. Methods: In this work, time-domain discriminating preictal and interictal features were efficiently extracted from the intracranial electroencephalogram of twelve patients, i.e., six with temporal and six with frontal lobe epilepsy. The performance of three types of feature selection methods was compared using Matthews’s correlation coefficient (MCC). Results: Kruskal Wallis, a non-parametric approach, was found to perform better than the other approaches due to a simple and less resource consuming strategy as well as maintaining the highest MCC score. The impact of dividing the electroencephalogram signals into various sub-bands was investigated as well. The highest performance of Kruskal Wallis may suggest considering the importance of univariate features like complexity and interquartile ratio (IQR), along with autoregressive (AR) model parameters and the maximum (MAX) cross-correlation to efficiently predict epileptic seizures. Conclusion: The proposed approach has the potential to be implemented on a low power device by considering a few simple time domain characteristics for a specific sub-band. It should be noted that, as there is not a great deal of literature on frontal lobe epilepsy, the results of this work can be considered promising.

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Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Abbaszadeh¹,

Teixeira²,

Yagoub³

2021

TOBEJ

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A Digital One Degree of Freedom Model of an Electromagnetic Position Sensor

Weinmann

2021

AIAA Scitech 2021 Forum

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A Novel Phase Difference Measurement Method for Coriolis Mass Flowmeter Based on Correlation Theory

et al. 2022

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Aiming at the poor precision problem in phase difference measurements with unknown frequencies in engineering practice, a new phase difference measurement method is proposed for Coriolis mass flowmeter based on correlation theories. Firstly, the signal frequency was estimated by using an adaptive notch filter, which was applied to filter the waves and determined the integer period of the sampling signals, and the non-integer period sampling signals needed to be extended. Then, the Hilbert transformation was conducted relative to the extended signals, and the correlation functions of these extended signals with the transformed signals can be computed. Finally, the formula of phase difference can be obtained by utilizing the sinusoidal function. Compared to traditional methods, such as the correlation method, the Hilbert transformation method, and sliding Goertzel algorithm, the proposed method is suitable for both integer period and non-integer period sampling signals, and its accuracy, real-time, and dynamic performance is superior. Simulation and experiment results validate the superiority and effectiveness of the proposed method.

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New Phase Difference Measurement Method for Non-Integer Number of Signal Periods Based on Multiple Cross-correlations

Cited by 3 publications

References 6 publications

Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study

A Digital One Degree of Freedom Model of an Electromagnetic Position Sensor

A Novel Phase Difference Measurement Method for Coriolis Mass Flowmeter Based on Correlation Theory

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