Automated Quantification of Periodic Discharges in Human Electroencephalogram.

Periodic discharges (PDs) are pathologic patterns of epileptiform discharges repeating at regular intervals, commonly detected in the human electroencephalogram (EEG) signals in patients who are critically ill. The frequency and spatial extent of PDs are associated with the tendency of PDs to cause brain injury, existing automated algorithms do not quantify the frequency and spatial extent of PDs. The present study presents an algorithm for quantifying frequency and spatial extent of PDs. The algorithm quantifies the evolution of these parameters within a short (10-14 second) window, with a focus on lateralized and generalized periodic discharges. We test our algorithm on 300 ``easy'', 300 ``medium'', and 240 ``hard'' examples (840 total epochs) of periodic discharges as quantified by interrater consensus from human experts when analyzing the given EEG epochs. We observe $95.0\%$ agreement with a 95\% confidence interval (CI) of $[94.9\%, 95.1\%]$ between algorithm outputs with reviewer clincal judgement for easy examples, $92.0\%$ agreement (95\% CI $[91.9\%, 92.2\%]$) for medium examples, and $90.4\%$ agreement (95\% CI $[90.3\%, 90.6\%]$) for hard examples. The algorithm is also computationally efficient and is able to run in $0.385 \pm 0.038$ seconds for a single epoch using our provided implementation of the algorithm. The results demonstrate the algorithm's effectiveness in quantifying these discharges and provide a standardized and efficient approach for PD quantification as compared to existing manual approaches.

Effects of Aerobic Exercise on Brain Age and Health in Middle-Aged and Older Adults: A Single-Arm Pilot Clinical Trial.

BACKGROUNDS: Sleep disturbances are prevalent among elderly individuals. While polysomnography (PSG) serves as the gold standard for sleep monitoring, its extensive setup and data analysis procedures impose significant costs and time constraints, thereby restricting the long-term application within the general public. Our laboratory introduced an innovative biomarker, utilizing artificial intelligence algorithms applied to PSG data to estimate brain age (BA), a metric validated in cohorts with cognitive impairments. Nevertheless, the potential of exercise, which has been a recognized means of enhancing sleep quality in middle-aged and older adults to reduce BA, remains undetermined. METHODS: We conducted an exploratory study to evaluate whether 12 weeks of moderate-intensity exercise can improve cognitive function, sleep quality, and the brain age index (BAI), a biomarker computed from overnight sleep electroencephalogram (EEG), in physically inactive middle-aged and older adults. Home wearable devices were used to monitor heart rate and overnight sleep EEG over this period. The NIH Toolbox Cognition Battery, in-lab overnight polysomnography, cardiopulmonary exercise testing, and a multiplex cytokines assay were employed to compare pre- and post-exercise brain health, exercise capacity, and plasma proteins. RESULTS: In total, 26 participants completed the initial assessment and exercise program, and 24 completed all procedures. Data are presented as mean [lower 95% CI of mean, upper 95% CI of mean]. Participants significantly increased maximal oxygen consumption (Pre: 21.11 [18.98, 23.23], Post 22.39 [20.09, 24.68], mL/kg/min; effect size: -0.33) and decreased resting heart rate (Pre: 66.66 [63.62, 67.38], Post: 65.13 [64.25, 66.93], bpm; effect size: -0.02) and sleeping heart rate (Pre: 64.55 [61.87, 667.23], Post: 62.93 [60.78, 65.09], bpm; effect size: -0.15). Total cognitive performance (Pre: 111.1 [107.6, 114.6], Post: 115.2 [111.9, 118.5]; effect size: 0.49) was significantly improved. No significant differences were seen in BAI or measures of sleep macro- and micro-architecture. Plasma IL-4 (Pre: 0.24 [0.18, 0.3], Post: 0.33 [0.24, 0.42], pg/mL; effect size: 0.49) was elevated, while IL-8 (Pre: 5.5 [4.45, 6.55], Post: 4.3 [3.66, 5], pg/mL; effect size: -0.57) was reduced. CONCLUSIONS: Cognitive function was improved by a 12-week moderate-intensity exercise program in physically inactive middle-aged and older adults, as were aerobic fitness (VO2max) and plasma cytokine profiles. However, we found no measurable effects on sleep architecture or BAI. It remains to be seen whether a study with a larger sample size and more intensive or more prolonged exercise exposure can demonstrate a beneficial effect on sleep quality and brain age.

Monitoring of sedation in mechanically ventilated patients using remote technology.

PURPOSE OF REVIEW: Two years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected. RECENT FINDINGS: High rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity. SUMMARY: Preventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.

Measuring expertise in identifying interictal epileptiform discharges.

OBJECTIVE: Interictal epileptiform discharges on EEG are integral to diagnosing epilepsy. However, EEGs are interpreted by readers with and without specialty training, and there is no accepted method to assess skill in interpretation. We aimed to develop a test to quantify IED recognition skills. METHODS: A total of 13,262 candidate IEDs were selected from EEGs and scored by eight fellowship-trained reviewers to establish a gold standard. An online test was developed to assess how well readers with different training levels could distinguish candidate waveforms. Sensitivity, false positive rate and calibration were calculated for each reader. A simple mathematical model was developed to estimate each reader's skill and threshold in identifying an IED, and to develop receiver operating characteristics curves for each reader. We investigated the number of IEDs needed to measure skill level with acceptable precision. RESULTS: Twenty-nine raters completed the test; nine experts, seven experienced non-experts and thirteen novices. Median calibration errors for experts, experienced non-experts and novices were -0.056, 0.012, 0.046; median sensitivities were 0.800, 0.811, 0.715; and median false positive rates were 0.177, 0.272, 0.396, respectively. The number of test questions needed to measure those scores was 549. Our analysis identified that novices had a higher noise level (uncertainty) compared to experienced non-experts and experts. Using calculated noise and threshold levels, receiver operating curves were created, showing increasing median area under the curve from novices (0.735), to experienced non-experts (0.852) and experts (0.891). SIGNIFICANCE: Expert and non-expert readers can be distinguished based on ability to identify IEDs. This type of assessment could also be used to identify and correct differences in thresholds in identifying IEDs.

Using electronic health data to explore effectiveness of ICU EEG and anti-seizure treatment.

OBJECTIVES: The purpose of this study was to examine critical care continuous electroencephalography (cEEG) utilization and downstream anti-seizure treatment patterns, their association with outcomes, and generate hypotheses for larger comparative effectiveness studies of cEEG-guided interventions. METHODS: Single-center retrospective study of critically ill patients (n = 14,523, age ≥18 years). Exposure defined as ≥24 h of cEEG and subsequent anti-seizure medication (ASM) escalation, with or without concomitant anesthetic. Exposure window was the first 7 days of admission. Primary outcome was in-hospital mortality. Multivariable analysis was performed using penalized logistic regression. RESULTS: One thousand and seventy-three patients underwent ≥24 h of cEEG within 7 days of admission. After adjusting for disease severity, ≥24 h of cEEG followed by ASM escalation in patients not on anesthetics (n = 239) was associated with lower in-hospital mortality (OR 0.76 [0.53-1.07]), though the finding did not reach significance. ASM escalation with concomitant anesthetic use (n = 484) showed higher odds for mortality (OR 1.41 [1.03-1.94]). In the seizures/status epilepticus subgroup, post cEEG ASM escalation without anesthetics showed lower odds for mortality (OR 0.43 [0.23-0.74]). Within the same subgroup, ASM escalation with concomitant anesthetic use showed higher odds for mortality (OR 1.34 [0.92-1.91]) though not significant. INTERPRETATION: Based on our findings we propose the following hypotheses for larger comparative effectiveness studies investigating the direct causal effect of cEEG-guided treatment on outcomes: (1) cEEG-guided ASM escalation may improve outcomes in critically ill patients with seizures; (2) cEEG-guided treatment with combination of ASMs and anesthetics may not improve outcomes in all critically ill patients.