The application of data science for the analysis of biomedical data presented as time series allows using new methods of analysis of living systems. Therefore, using these methods it could be possible to discover new diagnostic criteria for neurological diseases. In this paper, in the framework of Memory Functions Formalism, one of the approaches of statistical physics, we analyze recordings of signals of the human brain cortex in the sleep state of 19 patients with nocturnal frontal lobe epilepsy and 8 healthy subjects. We observe alterations of dynamic parameter behavior at the pathology and in healthy subjects. Furthermore, we reveal significant alterations in brain rhythms manifestations at statistical memory functions power spectra of patients with epilepsy and healthy subject. As a result, we show that the application of the statistical analysis of electroencephalogram recordings, after appropriate verification, can be helpful in the search of diagnostic criteria of nocturnal frontal lobe epilepsy.
Modern data science faces a lot of challenges, one of which is the search for diagnostic criteria for neurological diseases. New methods of statistical analysis are actively applied in the field of biophysics to solve this issue. In this paper we apply the Memory Functions Formalism to analyze electroencephalogram signal recordings in the sleeping state of 8 healthy subjects and 19 patients with nocturnal lobe epilepsy. We observe the considerable difference of statistical memory effects and fractal properties at the pathology in comparison with the control group. Furthermore, we reveal significant alterations in brain rhythms at power spectra of statistical memory functions for two groups of subjects. As a result, we show that the application of the statistical analysis methodology of bioelectrical brain cortex activity recordings, after appropriate verification, can be useful in the search for diagnostic criteria of nocturnal frontal lobe epilepsy.
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