Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis.

OBJECTIVE: In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients. METHODS: We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed. RESULTS: Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). CONCLUSIONS: Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers. SIGNIFICANCE: Automatic cEEG analysis provides useful information in septic patients.

Continuous EEG Monitoring in a Consecutive Patient Cohort with Sepsis and Delirium.

BACKGROUND: Delirium is common during sepsis, although under-recognized. We aimed to assess the value of continuous electroencephalography (cEEG) to aid in the diagnosis of delirium in septic patients. METHODS: We prospectively evaluated 102 consecutive patients in a medical intensive care unit (ICU), who had sepsis or septic shock, without evidence of acute primary central nervous system disease. We initiated cEEG recording immediately after identification. The median cEEG time per patient was 44 h (interquartile range 21-99 h). A total of 6723 h of cEEG recordings were examined. The Confusion Assessment Method for the ICU (CAM-ICU) was administered six times daily to identify delirium. We analyzed the correlation between cEEG and delirium using 1252 two-minute EEG sequences recorded simultaneously with the CAM-ICU scorings. RESULTS: Of the 102 included patients, 66 (65%) had at least one delirium episode during their ICU stay, 30 (29%) remained delirium-free, and 6 (6%) were not assessable due to deep sedation or coma. The absence of delirium was independently associated with preserved high-frequency beta activity (> 13 Hz) (P < 10-7) and cEEG reactivity (P < 0.001). Delirium was associated with preponderance of low-frequency cEEG activity and absence of high-frequency cEEG activity. Sporadic periodic cEEG discharges occurred in 15 patients, 13 of whom were delirious. No patient showed clinical or electrographic evidence of non-convulsive status epilepticus. CONCLUSIONS: Our findings indicate that cEEG can help distinguish septic patients with delirium from non-delirious patients.

Major rapid weight loss induces changes in cardiac repolarization.

INTRODUCTION: Obesity is associated with increased all-cause mortality, but weight loss may not decrease cardiovascular events. In fact, very low calorie diets have been linked to arrhythmias and sudden death. The QT interval is the standard marker for cardiac repolarization, but T-wave morphology analysis has been suggested as a more sensitive method to identify changes in cardiac repolarization. We examined the effect of a major and rapid weight loss on T-wave morphology. METHODS AND RESULTS: Twenty-six individuals had electrocardiograms (ECG) taken before and after eight weeks of weight loss intervention along with plasma measurements of fasting glucose, HbA1c, and potassium. For assessment of cardiac repolarization changes, T-wave Morphology Combination Score (MCS) and ECG intervals: RR, PR, QT, QTcF (Fridericia-corrected QT-interval), and QRS duration were derived. The participants lost on average 13.4% of their bodyweight. MCS, QRS, and RR intervals increased at week 8 (p<0.01), while QTcF and PR intervals were unaffected. Fasting plasma glucose (p<0.001) and HbA1c both decreased at week 8 (p<10(-5)), while plasma potassium was unchanged. MCS but not QTcF was negatively correlated with HbA1c (p<0.001) and fasting plasma glucose (p<0.01). CONCLUSION: Rapid weight loss induces changes in cardiac repolarization. Monitoring of MCS during calorie restriction makes it possible to detect repolarization changes with higher discriminative power than the QT-interval during major rapid weight loss interventions. MCS was correlated with decreased HbA1c. Thus, sustained low blood glucose levels may contribute to repolarization changes.

Heritability of Tpeak-Tend interval and T-wave amplitude: a twin study.

BACKGROUND: Tpeak-Tend interval (TpTe) and T-wave amplitude (Tamp) carry diagnostic and prognostic information regarding cardiac morbidity and mortality. Heart rate and QT interval are known to be heritable traits. The heritability of T-wave morphology parameters such as TpTe and Tamp is unknown. TpTe and Tamp were evaluated in a large sample of twins. METHODS AND RESULTS: Twins from the GEMINAKAR study (611 pairs, 246 monozygotic, 365 dizygotic; mean age, 38±11 years; 49% men) who had an ECG performed during 1997 to 2000 were included. Tamp was measured in leads V1 and V5. Duration variables (RR interval, QTpeak and QTend interval) were measured and averaged over 3 consecutive beats in lead V5. TpTe was calculated as the QTend- and QTpeak-interval difference. Heritability was assessed using structural equation models adjusting for age, sex, and body mass index. All models were reducible to a model of additive genetics and unique environment. All variables had considerable genetic components. Adjusted heritability estimates were as follows: TpTe, 46%; Tamp lead V1, 34%; Tamp lead V5, 47%; RR interval, 55%; QT interval, 67%; and Bazett-corrected QT interval, 42%. CONCLUSIONS: RR interval, QT interval, Tamp, and TpTe interval are heritable ECG parameters.

A comparative study between a simplified Kalman filter and Sliding Window Averaging for single trial dynamical estimation of event-related potentials.

The classical approach for extracting event-related potentials (ERPs) from the brain is ensemble averaging. For long latency ERPs this is not optimal, partly due to the time-delay in obtaining a response and partly because the latency and amplitude for the ERP components, like the P300, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude in the P300 component and in a considerably higher robustness towards suboptimal settings. The latter is of great importance in a clinical setting where the optimal setting cannot be determined.

The prognostic value of the Tpeak-Tend interval in patients undergoing primary percutaneous coronary intervention for ST-segment elevation myocardial infarction.

INTRODUCTION: The Tpeak-Tend interval (TpTe) has been linked to increased arrhythmic risk. TpTe was investigated before and after primary percutaneous coronary intervention (pPCI) in patients with ST-segment elevation myocardial infarction (STEMI). METHOD: Patients with first-time STEMI treated with pPCI were included (n = 101; mean age 62 years; range 39-89 years; 74% men). Digital electrocardiograms were taken pre- and post-PCI, respectively. Tpeak-Tend interval was measured in leads with limited ST-segment deviation. The primary end point was all-cause mortality during 22 +/- 7 months (mean +/- SD) of follow-up. RESULTS: Pre- and post-PCI TpTe were 104 milliseconds [98-109 milliseconds] and 106 milliseconds [99-112 milliseconds], respectively (mean [95% confidence interval], P = .59). A prolonged pre-PCI TpTe was associated with increased mortality (hazard ratio, 10.5 [1.7-20.4] for a cutoff value of 100 milliseconds). Uncorrected QT and heart rate-corrected QT intervals (Fridericia-corrected QT) were prolonged after PCI (QT: 401 vs 410 milliseconds, P = .022, and Fridericia-corrected QT: 430 vs 448 milliseconds, P < .0001). CONCLUSION: In patients with STEMI undergoing pPCI, pre-PCI TpTe predicted subsequent all-cause mortality, and the QT interval was increased after the procedure.

T(peak)T(end) interval in long QT syndrome.

BACKGROUND: The T(peak)T(end) (T(p)T(e)) interval is believed to reflect the transmural dispersion of repolarization. Accordingly, it should be a risk factor in long QT syndrome (LQTS). The aim of the study was to determine the effect of genotype on T(p)T(e) interval and test whether it was related to the occurrence of syncope. METHODS: Electrocardiograms were taken in 95 patients with LQTS drawn from the Danish long QT registry (44 patients with KvLQT1, 43 with HERG, and 8 with SCN5A mutations) and manually evaluated for the QT, QT(peak), and RR interval. RESULTS AND CONCLUSION: (1) T(p)T(e) cannot be used to distinguish symptomatic from asymptomatic patients with LQTS; (2) HERG patients have longer T(p)T(e) than KvLQT1 patients; and (3) there is no need to heart rate-correct T(p)T(e) intervals in patients with LQTS.