Period-amplitude analysis and power spectral analysis: a comparison based on all-night sleep EEG recordings

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ( alpha + beta / delta + theta ) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592–0.914) ( p < 0.05 ). Mild cognitive dysfunction was positively correlated with NREM-DFA ( r = 0.426 , p = 0.007 ) and negatively correlated with an NREM-fast ratio ( r = − 0.524 , p = 0.001 ). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.

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Spectral Analysis of the EEG in Craniocerebral Trauma

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/s0317167100027244 ◽

1988 ◽

Vol 15 (1) ◽

pp. 82-86 ◽

Cited By ~ 15

Author(s):

Richard J. Moulton ◽

Anthony Marmarou ◽

Jacob Ronen ◽

John D. Ward ◽

Sung Choi ◽

...

Keyword(s):

Spectral Analysis ◽

Craniocerebral Trauma ◽

Power Spectral Analysis ◽

Post Injury ◽

Term Outcome ◽

Long Term Outcome ◽

Linear Discriminant ◽

Power Spectral ◽

Number Of Patients ◽

Eeg Recordings

ABSTRACT:The objectives of the present study were to evaluate the relationship between the fractional amplitudes of the EEG derived from power spectral analysis (PSA) of the electroencephalogram (EEG) and depth of coma measured clinically with the Glasgow Coma Score, and to assess the accuracy of PSA in predicting long-term outcome. Thirty-two patients rendered unconscious by blunt head injury (mean (GCS = 7) had intermittent EEG recordings daily from 1-10 days post injury. There was a significant correlation between fractional amplitude of the EEG and the GCS. The rate and magnitude of change in the EEG and GCS were also correlated. There were significant differences in PSA parameters between improved and deteriorated patient groups at the termination of monitoring (p = .02) and in the change of PSA parameters over time (p = .02). Using linear discriminant analysis of PSA parameters, the accuracy of outcome prognostication based on the six month outcome was approximately 75%. Accurate classification of outcome was possible in a number of patients in whom there was little or no change in the GCS during the period of monitoring.

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