Citations

Introduction: Long QT syndrome (LQTS) is a genetic disorder associated with abnormal ventricular repolarization and a predisposition for torsade de pointes and sudden cardiac death. Identification of markers to elucidate high risk patients is of major practical importance. Our aim was to use a novel, automated quantitative T wave analysis program to distinguish symptomatic from asymptomatic LQTS patients. Methods: We analyzed a genotyped cohort of 420 patients (22 ± 15.7years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using novel software that we created to automatically detect subtle changes in T wave morphology. Symptomatic patients were defined as those with a history of syncope or resuscitated cardiac arrest that was suspected of being LQTS triggered. Classification was performed using the top three T wave features in a linear discriminant classifier by 10x10 cross-validation. Results: Symptomatic LQT1 patients have a greater QTc duration in lead aVF (437 msec vs 416 msec, p <0.0001), a greater T wave center of gravity on the x axis (COGx) in lead V2 (0.304 vs 0.280, p <0.0001) and longer Tpeak-Tend/QT in lead II (0.208 vs 0.188, p < 0.0001) than asymptomatic patients with LQT1. Use of these 3 features enabled classification of symptomatic status in 72% of LQT1 cases, compared to 68% when QTc was used to classify alone. Symptomatic LQT2 patients had a longer QTc in Lead 1 (460 vs 418 msec, p < 0.0001), COGx in 4th segment of T wave was greater in lead V5 (0.399 vs 0.350, p<0.0001), and the Tpeak-Tend interval in V2 was longer (126 vs 99 msec, P =0.0004). Use of these 3 features enabled correct classification in 73% of LQT2 cases, compared to 67% when QTc was used alone. Conclusions: Application of an automated T wave analysis program was able to identify T wave features that distinguished the patients who presented as symptomatic LQTS, associated with syncope or resuscitated cardiac arrest. Use of these features could help assess arrhythmic risk and guide management strategies for patients with LQTS.

Abstract Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8–13 Hz) relative to a wide band of 0.5–40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

AbstractObjectiveMost seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts.MethodsThis study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective.ResultsFor the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature.SignificanceEnvironmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

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.