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Analysis of EEG records in an epileptic patient using wavelet transform.

机译:使用小波变换分析癫痫患者的脑电图记录。

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About 1% of the people in the world suffer from epilepsy and 30% of epileptics are not helped by medication. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records. Wavelet transform is used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. The capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals. Wavelet analyses of EEGs obtained from a population of patients can potentially suggest the physiological processes undergoing in the brain in epilepsy onset. A better understanding of the dynamics of the human brain through EEG analysis can be obtained through further analysis of such EEG records.
机译:世界上约有1%的人患有癫痫病,而30%的癫痫病患者没有药物治疗。仔细分析脑电图(EEG)记录可以提供有价值的见解,并更好地理解引起癫痫病的机制。小波变换对于表示非平稳信号的各个方面特别有效,例如趋势,不连续性和其他信号处理方法失败或效果不佳的重复模式。在这项研究中,对离散Daubechies和谐波小波进行了研究,以分析癫痫性脑电图记录。小波变换用于以癫痫发作患者的3 Hz尖峰和波复合体的形式分析和表征癫痫样放电。通过脑电图记录的小波分解,可以在时间和频率范围内准确捕获瞬态特征并将其定位。这种数学显微镜分析不同程度的神经节律的能力被证明是研究脑信号小范围振荡的强大工具。从患者人群中获得的脑电信号的小波分析可能会提示癫痫发作时大脑中正在发生的生理过程。通过对脑电图记录的进一步分析,可以通过脑电图分析更好地了解人脑的动态。

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