Defining abnormal slow EEG activity in acute ischaemic stroke: Delta/alpha ratio as an optimal QEEG index. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Finnigan, Simon; Wong, Andrew; Read, Stephen
• Is Part Of: Clinical neurophysiology. Volume 127:Issue 2(2016:Feb.)
• Page Start: 1452
• Page End: 1459
• Publication Date: 2016-02
• Subjects: Acute ischaemic stroke -- Quantitative electroencephalography -- Delta activity -- Alpha activity; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2015.07.014
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: All QEEG indices (sensitive to power of delta, theta, alpha, and/or beta bands) differed highly significantly between acute ischaemic stroke (IS) and control samples. Delta/alpha power ratio (DAR) demonstrated maximal accuracy for discriminating between acute IS patients and controls. DAR <3.7 was 100% specific for the absence, and >3.7 was 100% sensitive for the presence, of a radiologically-confirmed IS lesion. Abstract: Objective: Quantitative electroencephalographic (QEEG) indices sensitive to abnormal slow (relative to faster) activity power seem uniquely informative for clinical management of ischaemic stroke (IS), including around acute reperfusion therapies. However these have not been compared between IS and control samples. The primary objective was to identify the QEEG slowing index and threshold value which can most accurately discriminate between IS patients and controls. Methods: The samples comprised 28 controls (mean age: 70.4; range: 56–84) and 18 patients (mean age: 69.3; range: 51–86). Seven indices were analysed: relative bandpower (delta, theta, alpha, beta), delta/alpha power ratio (DAR), (delta + theta)/(alpha + beta) ratio (DTABR) and Q SLOWING . The accuracies of each index for classifying participants (IS or control) were analysed using receiver operating characteristic (ROC) techniques. Results: All indices differed significantly between the samples ( p < .001). DAR alone exhibited optimal classifier accuracy, with a threshold of 3.7 demonstrating 100% sensitivity and 100% specificity for discriminating between radiologically-confirmed, acute IS or control. DTABR and relative delta were the next most accurate classifiers. Conclusions: DAR of 3.7 demonstrated maximal accuracy for classifying all 46 participants as acute IS or control. Significance: DAR assessment may inform clinical management of IS and perhaps other neurocritical patients.