Abstract
INTRODUCTION: Differentiating between the two most common forms of dementia, Alzheimer's dementia and dementia with Lewy bodies (DLB) remains difficult and requires the use of invasive, expensive, and resource-intensive techniques. We aimed to investigate the sensitivity and specificity of electroencephalography quantified using the statistical pattern recognition method (qEEG-SPR) for identifying dementia and DLB. METHODS: Thirty-two outpatients and 16 controls underwent clinical assessment (by two blinded neurologists), EEG recording, and a 6-month follow-up clinical assessment. EEG data were processed using a qEEG-SPR protocol to derive a Dementia Index (positive or negative) and DLB index (positive or negative) for each participant which was compared against the diagnosis given at clinical assessment. Confusion matrices were used to calculate sensitivity, specificity, and predictive values for identifying dementia and DLB specifically. RESULTS: Clinical assessment identified 30 cases of dementia, 2 of which were diagnosed clinically with possible DLB, 14 with probable DLB and DLB was excluded in 14 patients. qEEG-SPR confirmed the dementia diagnosis in 26 out of the 32 patients and led to 6.3% of false positives (FP) and 9.4% of false negatives (FN). qEEG-SPR was used to provide a DLB diagnosis among patients who received a positive or inconclusive result of Dementia index and led to 13.6% of FP and 13.6% of FN. Confusion matrices indicated a sensitivity of 80%, a specificity of 89%, a positive predictive value of 92%, a negative predictive value of 72%, and an accuracy of 83% to diagnose dementia. The DLB index showed a sensitivity of 60%, a specificity of 90%, a positive predictive value of 75%, a negative predictive value of 81%, and an accuracy of 75%. Neuropsychological scores did not differ significantly between DLB and non- DLB patients. Head trauma or story of stroke were identified as possible causes of FP results for DLB diagnosis. CONCLUSION: qEEG-SPR is a sensitive and specific tool for diagnosing dementia and differentiating DLB from other forms of dementia in the initial state. This non-invasive, low-cost, and environmentally friendly method is a promising diagnostic tool for dementia diagnosis which could be implemented in local care settings.