Kremen, Vaclav

Abstract Advances in ambulatory intracranial EEG devices have enabled objective analyses of circadian and multiday seizure periodicities, and seizure clusters in humans. This study characterizes circadian and multiday seizure periodicities, and seizure clusters in dogs with naturally occurring focal epilepsy, and considers the implications of an animal model for the study of seizure risk patterns, seizure forecasting and personalized treatment protocols. In this retrospective cohort study, 16 dogs were continuously monitored with ambulatory intracranial EEG devices designed for humans. Detailed medication records were kept for all dogs. Seizure periodicity was evaluated with circular statistics methods. Circular non-uniformity was assessed for circadian, 7-day and approximately monthly periods. The Rayleigh test was used to assess statistical significance, with correction for multiple comparisons. Seizure clusters were evaluated with Fano factor (index of dispersion) measurements, and compared to a Poisson distribution. Relationships between interseizure interval (ISI) and seizure duration were evaluated. Six dogs met the inclusion criteria of having at least 30 seizures and were monitored for an average of 65 weeks. Three dogs had seizures with circadian seizure periodicity, one dog had a 7-day periodicity, and two dogs had approximately monthly periodicity. Four dogs had seizure clusters and significantly elevated Fano factor values. There were subject-specific differences in the dynamics of ISI and seizure durations, both within and between lead and clustered seizure categories. Our findings show that seizure timing in dogs with naturally occurring epilepsy is not random, and that circadian and multiday seizure periodicities, and seizure clusters are common. Circadian, 7-day, and monthly seizure periodicities occur independent of antiseizure medication dosing, and these patterns likely reflect endogenous rhythms of seizure risk.

Cardiovascular diseases are one of most frequent cause of morbidity and mortality in the world. There is an emerging need for integrated, non-invasive, and easy-to-use clinical tools to assess accurately cardiovascular system primarily in the preventative medicine. We present a novel design for a non-invasive pulse wave velocity (PWV) assessment method integrated in a single brachial blood pressure monitor allowing for up to 100 times more sensitive recording of the pressure pulsations based on a brachial occlusion-cuff (suprasystolic) principle. The monitor prototype with built-in proprietary method was validated with a gold standard reference technique SphygmoCor VX device. The blood pressure and PWV were assessed on twenty-five healthy individuals (9 women, age (37 ± 13) years) in a supine position at rest by a brachial cuff blood pressure monitor prototype, and immediately re-tested using a gold standard method. PWV using our BP monitor was (6.67 ± 0.96) m/s compared to PWV determined by SphygmoCor VX (6.15 ± 1.01) m/s. The correlation between methods using a Pearson’s correlation coefficient was r = 0.88 (p < 0.001). The study demonstrates the feasibility of using a single brachial cuff build-in technique for the assessment of the arterial stiffness from a single ambulatory blood pressure assessment.

Abstract Objective This study investigates high‐frequency oscillations ( HFO s; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFO s (path HFO ) from physiological HFO s (phys HFO ). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. Methods We recorded HFO s using intracranial EEG ( iEEG ) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative phys HFO s were induced by cognitive or motor tasks. HFO s were identified using validated detectors. A support vector machine ( SVM ) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. Results There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous phys HFO , task induced phys HFO , and path HFO , but the amplitudes of the path HFO were higher ( P < 0.0001). High gamma path HFO had the strongest association with seizure onset zone ( SOZ ), and were elevated on SOZ electrodes in 70% of epilepsy patients ( P < 0.0001). Failure to resect tissue generating high gamma path HFO was associated with poor outcomes ( P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. Interpretation A broader range of interictal path HFO (65–600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFO s into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

Abstract Objective . An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Approach . Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Main results . Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. Significance . The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.

The gold standard for noninvasive blood pressure (BP) measurement, the Doppler technique, does not provide systolic blood pressure (SBP) and diastolic blood pressure (DBP) and may limit therapy outcomes. To improve patient care, we tested specifically designed experimental BP (ExpBP) monitor and the Doppler technique by comparing noninvasive measures to the intraarterial (I-A) BP in 31 patients with end-stage heart failure (4 females) 2.6 ± 3.4 days post-LVAD implantation (20 HeartMate II and 11 HeartWare). Bland–Altman plots revealed that the ExpBP monitor overestimated mean arterial pressure (MAP) by 1.2 (4.8) mm Hg (mean difference [standard deviation]), whereas the Doppler by 6.7 (5.8) mm Hg. The ExpBP SBP was overestimated by 0.8 (6.1) mm Hg and DBP by 1.9 (5.3) mm Hg compared with the respective I-A pressures. Both techniques achieved similar measurement reliability. In the measurement “success rate” expressed as a frequency (percent) of readable BP values per measurement attempts, Doppler accomplished 100% vs. 97%, 97%, and 94% of successful detections of MAP, SBP, and DBP provided by the ExpBP monitor. The ExpBP monitor demonstrated higher accuracy in the MAP assessment than the Doppler in addition to providing SBP and DBP in majority of subjects. Improved BP control may help to mitigate related neurologic adverse event rates.

Background: Although the hallmark of long-QT syndrome (LQTS) is abnormal cardiac repolarization, there are varying degrees of phenotypic expression and arrhythmic risk. Our aim was to evaluate the performance of a morphological T-wave analysis program in defining breakthrough LQTS arrhythmic risk beyond the QTc value. Methods and Results: We analyzed 407 genetically confirmed patients with LQT1 (n=246; 43% men) and LQT2 (n=161; 41% men) over the mean follow-up period of 6.4±3.9 years. ECG analysis was conducted using a novel, proprietary T-wave analysis program. Time to a LQTS-associated cardiac event was analyzed using Cox proportional hazards regression methods. Twenty-three patients experienced ≥1 defined breakthrough cardiac arrhythmic events with 5- and 10-year event rates of 4% and 7%. Two independent predictors of future LQTS-associated cardiac events from the surface ECG were identified: left slope of T wave in lead V6 (hazard ratio=0.40 [0.24–0.69]; P <0.001) and T-wave center of gravity x axis (last 25% of wave) in lead I (hazard ratio=1.90 [1.21–2.99]; P =0.005), C statistic of 0.77 (0.65–0.89). When added to the QTc (C statistic 0.68 for QTc alone), discrimination improved to 0.78. Genotype analysis showed weaker association between these T-wave variables and LQT1-triggered events while these features were stronger in patients with LQT2 and significantly outperformed the QTc (C statistic, 0.82 [0.71–0.93]). Conclusion: Detailed morphological analysis of the T wave provides novel insights into risk of breakthrough arrhythmic events in LQTS, particularly LQT2. This observation has the potential to guide clinical decision making and further refine risk stratification.