F81. Diagnosis of dementia and subtype classification by digital EEG: Is it also endorsed by automatic EEG analysis?

Introduction Precise diagnosis of subtypes of dementia is essential in daily medical practice. Electroencephalography (EEG) is a less expensive and non-invasive examination, but is not used for the diagnosis of dementia and its subtype classification because of lack of clear evidence. Therefore, we aimed at delineating clinical utility of EEG for this matter for Alzheimer disease (AD), dementia with Lewy body (DLB) and Parkinson disease with dementia (PDD). Methods We retrospectively analyzed the EEG data of 16 patients of AD (mean age ad SD = 67 ± 9.1-years, 6 male and 10 female) and 22 of synucleinopathy including 12 DLB and 10 PDD (72 ± 9.1-years, 17 male and 7 female) at Kyoto University Hospital from 2008 to 2015. First, by visual analysis we compared the posterior dominant rhythm (PDR) frequency and the ratio of generalized and regional slow waves between AD and synucleinopathy. Then, by automatic analysis we employed automatic EEG interpretation software (QP270 Nihonkoden) to investigate the same EEG parameters. Finally, we compared the results of visual analysis and automatic analysis in all patients. Results (1) With respect to the visual analysis, patients with synucleinopathy showed significantly lower PDR frequency (7.8 ± 1.0 Hz) than that of AD (9.2 ± 0.91 Hz) ( p p  = 0.002). As for regional slow waves, synucleinopathy patients (9/22) had occipital slow waves more frequent than that of AD (1/16) ( p  = 0.025). There was no statistical difference in the number of patients with generalized slow waves (GS) between AD and synucleinopathy. In case of PDR of less than 8 Hz and with occipital slow waves, synucleinopathy (6/22) was suspected but AD (0/16) was unlikely ( p  = 0.029). In case of PDR of less than 8 Hz and GS, synucleinopathy (9/22) was suspected but AD (0/16) was unlikely ( p  = 0.004). (2) With respect to the automatic analysis, most of the findings by means of visual analysis were also obtained as follows. (3) For comparison between visual and automatic analysis, there was a significant positive correlation in PDR frequency between the two ( r  = 0.81). There was an overlap in the regions detected by both visual and automatic analysis in 20 out of 38 patients. There was a consistent result in GS detection by both in 25 of 37 patients. Conclusion In the visual analysis, EEG provides the information to differentiate AD from synucleinopathy by means of PDR, occipital slow waves and GS. The result of automatic analysis of EEG well matched with the visual analysis and there is a possibility that automatic EEG analysis was a useful tool to endorse the visual findings.