It is a fundamental element in both research and clinical applications of electroencephalography to know the frequency composition of brain electrical activity. The quantitative analysis of brain electrical activity uses computer resources to evaluate the electroencephalography and allows quantification of the data. The contribution of the quantitative perspective is unique, since conventional electroencephalography based on the visual examination of the tracing is not as objective. A systematic review was performed on the MEDLINE database in October 2017. The authors independently analyzed the studies, by title and abstract, and selected articles that met the inclusion criteria: comparative studies, not older than 30 years, that compared the use of conventional electroencephalogram (EEG) with the use of quantitative electroencephalogram (QEEG) in the English language. One hundred twelve articles were automatically selected by the MEDLINE search engine, but only six met the above criteria. The review found that given a 95% confidence interval, QEEG had no statistically higher sensitivity than EEG in four of the six studies reviewed. However, these results must be viewed with appropriate caution, particularly as groups in between studies were not matched on important variables such as gender, age, type of illness, recovery stage, and treatment. The authors' findings in this systematic review are suggestive of the importance of QEEG as an auxiliary tool to traditional EEG, and as such, justifying further refinement, standardization, and eventually the future execution of a head-to-head prospective study on comparing the two methods.

Background:In stroke, timeliness of care is essential for optimal patient outcomes. While opportunities for code response time improvements have been extensively documented in the medical literature, this retrospective study aimed at providing data and insights for the development of a quality improvement project in the same hospital, with the ultimate goal of increasing code stroke response speeds without compromising the quality of care.Methods: This was a retrospective cohort study. Data were collected from weekly code stroke review meetings between January and December 2020 from both the emergency department (ED), and inpatient settings from one Joint Commission certified Primary Stroke Center. All code stroke cases with a computed tomography (CT) scan were included. For cases that received tissue plasminogen activator (tPA), variables collected were time from code-to-CT scan start, codeto-tPA, from CT scan start to tPA, and from CT scan completion to tPA. For code stroke cases that did not receive tPA, variables collected were code-to-CT scan start, code-to-CT scan read, from CT scan start to CT scan read, and from CT scan completion to CT scan read. Then, the ED's code stroke response times were compared with those in the inpatient setting by using a two-tailed t-test and a 95% confidence interval.Results: From a sample of 206 code stroke activations in 2020, 157 activations met the study's criteria. For cases that received tPA, the difference in the mean code-to-CT start times between ED and the inpatient settings (9.01 and 24.99 min, respectively) was statistically significant with a P-value < 0.05. For cases that did not receive tPA, the differences between ED and the inpatient settings in the mean code-to-CT start times (14.25 and 30.74 min, respectively) and code-to-CT read times (34.25 and 54.95 min, respectively) were also statistically significant with a P-value < 0.05. Conclusion:This study highlights the urgent need to improve codeto-CT times in this hospital's inpatient setting since ED code stroke times were markedly better from a statistical viewpoint. Improving the quality of care will have to address the evident delay in transporting inpatients to the CT scanner after a code stroke has been activated.