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Peripheral nerve stimulation (PNS) is an effective means to elicit sensation for rehabilitation of people with loss of a limb or limb function. While most current PNS paradigms deliver current through single electrode contacts to elicit each tactile percept, multi-contact extraneural electrodes offer the opportunity to deliver PNS with groups of contacts individually or simultaneously. Multi-contact PNS strategies could be advantageous in developing biomimetic PNS paradigms to recreate the natural neural activity during touch, because they may be able to selectively recruit multiple distinct neural populations. We used computational models and optimization approaches to develop a novel biomimetic PNS paradigm that uses interleaved multi-contact (IMC) PNS to approximate the critical neural coding properties underlying touch. The IMC paradigm combines field shaping, in which two contacts are active simultaneously, with pulse-by-pulse contact and parameter variations throughout the touch stimulus. We show in simulation that IMC PNS results in better neural code mimicry than single contact PNS created with the same optimization techniques, and that field steering via two-contact IMC PNS results in better neural code mimicry than one-contact IMC PNS. We also show that IMC PNS results in better neural code mimicry than existing PNS paradigms, including prior biomimetic PNS. Future clinical studies will determine if the IMC paradigm can improve the naturalness and usefulness of sensory feedback for those with neurological disorders.

Introduction: Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1β, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration., Competing Interests: The author TD declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Campanaro, Nethery, Guo, Kaffashi, Loparo, Jacono, Dick and Hsieh.)

Objective: The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome., Methods: Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers. Intracranial PES was quantified using the Hilbert transform. HRV was quantified using root mean square of successive differences of interbeat intervals, low-frequency to high-frequency power ratio, and RR-intervals., Results: There were 64 seizures from 63 patients without SUDEP and 11 seizures from 6 SUDEP patients. PES occurred in 99% and 87% of seizures on intracranial-EEG and scalp-EEG, respectively. Mean PES duration in intracranial and scalp-EEG was similar. Intracranial PES was regional (<90% of channels) in 46% of seizures; scalp PES was generalized in all seizures. Generalized PES showed greater decrease in postictal parasympathetic activity than regional PES. PES duration and extent were similar between patients with and without SUDEP., Conclusions: Regional intracranial PES can be present despite scalp-EEG demonstrating generalized or no PES. Postictal autonomic dysfunction correlates with the extent of PES., Significance: Intracranial-EEG demonstrates changes in autonomic regulatory networks not seen on scalp-EEG., Competing Interests: Conflict of interest B. Esmaeili, D. Weisholtz, S. Tobochnik, F. Kaffashi, S. Cash, B. Cha, J. Laze, D. Reich, P. Farooque, T. Gholipour, M. Singleton, K. Loparo, and M. Koubeissi report no disclosures relevant to the manuscript. B. Dworetzky has received support from the A.J. Trustey Research Fund and royalties from Oxford University Press. D. Friedman has received salary support for consulting and clinical trial related activities performed on behalf of The Epilepsy Study Consortium, a non-profit organization. D. Friedman receives no personal income for these activities. NYU receives a fixed amount from the Epilepsy Study Consortium towards D. Friedman’s salary. Within the past two years, The Epilepsy Study Consortium received payments for research services performed by D. Friedman from: Alterity, Baergic, Biogen, BioXcell, Cerevel, Cerebral, Jannsen, Lundbeck, Neurocrine, SK Life Science, and Xenon. He has also served as a paid consultant for Neurelis Pharmaceuticals and Receptor Life Sciences. He has received travel support from the Epilepsy Foundation. He has received research support from NINDS, CDC, Epitel, and Neuropace unrelated to this study. He serves on the scientific advisory board for Receptor Life Sciences. He holds equity interests in Neuroview Technology. He received royalty income from Oxford University Press. S. Cash is supported by the National Institute of Neurological Disorders and Stroke (NINDS, K24NS088568 and R01NS062092). M. Singleton is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR000423). O. Devinsky is supported by Finding a Cure for Epilepsy and Seizures (FACES), the National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), Multidisciplinary University Research Initiatives (MURI), Centers for Disease Control and Prevention (CDC) and National Science Foundation (NSF). He has equity interests in Qstate Biosciences, Tevard Biosciences, Regel Therapeutics and Script Biosciences, Privateer Holdings, Tilray, Receptor Life Sciences, Empatica, Engage, Egg Rock/Papa & Barkley, Rettco, SilverSpike, and California Cannabis Enterprises (CCE). He is an investigator for PTC Therapeutics, Inc., Stoke Therapeutics, Marinus, Ovid and GW Pharmaceuticals. J.W. Lee has contract work with Bioserenity and Teladoc. He has served as a consultant for Biogen. He has received research funding from NINDS and Epilepsy Foundation. He is a co-founder of Soterya, Inc., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Background: With evolving understanding of psychiatric diagnosis and treatment, demand for biomarkers for psychiatric disorders in children and adolescents has grown dramatically. This study utilized quantitative electroencephalography (qEEG) to develop a predictive model for adolescent major depressive disorder (MDD). We hypothesized that youth with MDD compared to healthy controls (HCs) could be differentiated using a singular logistic regression model that utilized qEEG data alone. Methods: qEEG data and psychometric measures were obtained in adolescents aged 14-17 years with MDD ( n  = 35) and age- and gender-matched HCs ( n  = 14). qEEG in four frequency bands (alpha, beta, theta, and delta) was collected and coherence, cross-correlation, and power data streams obtained. A two-stage analytical framework was then used to develop the final logistic regression model, which was then evaluated using a receiver-operating characteristic curve (ROC) analysis. Results: Within the initial analysis, six qEEG dyads (all coherence) had significant predictive values. Within the final biomarkers, just four predictors, including F3-C3 (R frontal) alpha coherence, P3-O1 (R parietal) theta coherence, CZ-PZ (central) beta coherence, and P8-O2 (L parietal occipital) theta power were used in the final model, which yielded an ROC area of 0.8226. Conclusions: We replicated our previous findings of qEEG differences between adolescents and HCs and successfully developed a single-value predictive model with a robust ROC area. Furthermore, the brain areas involved in behavioral disinhibition and resting state/default mode networks were again shown to be involved in the observed differences. Thus, qEEG appears to be a potential low-cost and effective intermediate biomarker for MDD in youth.

Modern intensive care units (ICU) are equipped with a variety of different medical devices to monitor the physiological status of patients. These devices can generate large amounts of multimodal data daily that include physiological waveform signals (arterial blood pressure, electrocardiogram, respiration), patient alarm messages, numeric vitals data, etc. In order to provide opportunities for increasingly improved patient care, it is necessary to develop an effective data acquisition and analysis system that can assist clinicians and provide decision support at the patient bedside. Previous research has discussed various data collection methods, but a comprehensive solution for bedside data acquisition to analysis has not been achieved. In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. We also discuss how the acquired data can be used for patient state tracking. INSMA is being tested in the ICU at University Hospitals Cleveland Medical Center., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The objective of this study was to examine whether heart rate variability (HRV) measures can be used to detect neurocardiogenic injury (NCI)., Methods: Three hundred and twenty-six consecutive admissions with aneurysmal subarachnoid hemorrhage (SAH) met criteria for the study. Of 326 subjects, 56 (17.2%) developed NCI which we defined by wall motion abnormality with ventricular dysfunction on transthoracic echocardiogram or cardiac troponin-I > 0.3 ng/mL without electrocardiogram evidence of coronary artery insufficiency. HRV measures (in time and frequency domains, as well as nonlinear technique of detrended fluctuation analysis) were calculated over the first 48 h. We applied longitudinal multilevel linear regression to characterize the relationship of HRV measures with NCI and examine between-group differences at baseline and over time., Results: There was decreased vagal activity in NCI subjects with a between-group difference in low/high frequency ratio (β 3.42, SE 0.92, p = 0.0002), with sympathovagal balance in favor of sympathetic nervous activity. All time-domain measures were decreased in SAH subjects with NCI. An ensemble machine learning approach translated these measures into a classification tool that demonstrated good discrimination using the area under the receiver operating characteristic curve (AUROC 0.82), the area under precision recall curve (AUPRC 0.75), and a correct classification rate of 0.81., Conclusions: HRV measures are significantly associated with our label of NCI and a machine learning approach using features derived from HRV measures can classify SAH patients that develop NCI.

Background: Biomarkers for psychiatric disorders in children and adolescents are urgently needed. This cross-sectional pilot study investigated quantitative electroencephalogram (qEEG), a promising intermediate biomarker, in pediatric patients with major depressive disorder (MDD) compared with healthy controls (HCs). We hypothesized that youth with MDD would have increased coherence (connectivity) and absolute alpha power in the frontal cortex compared with HC. Methods: qEEG was obtained in adolescents aged 14-17 years with MDD ( n  = 25) and age- and gender-matched HCs ( n  = 14). The primary outcome was overall coherence on qEEG in the four frequency bands (alpha, beta, theta, and delta). Other outcomes included frontal-only coherence, overall and frontal-only qEEG power, and clinician-rated measures of anhedonia and anxiety. Results: Average coherence in the theta band was significantly lower in MDD patients versus HCs, and also lower in frontal cortex among MDD patients. Seven node pairs were significantly different or trending toward significance between MDD and HC; all had lower coherence in MDD patients. Average frontal delta power was significantly higher in MDD versus HCs. Conclusions: Brain connectivity measured by qEEG differs significantly between adolescents with MDD and HCs. Compared with HCs, youth with MDD showed decreased connectivity, yet no differences in power in any frequency bands. In the frontal cortex, youth with MDD showed decreased resting connectivity in the alpha and theta frequency bands. Impaired development of a resting-state brain network (e.g., default mode network) in adolescents with MDD may represent an intermediate phenotype that can be assessed with qEEG.

This paper discusses the acquisition and processing of multimodal physiological data from patients with epilepsy in Epilepsy Monitoring Units for the discovery of risk factors for Sudden Expected Death in Epilepsy (SUDEP) that can be combined through integrative analysis for biomarker discovery.

Objective: Seizure-related autonomic dysregulation occurs in epilepsy patients and may contribute to Sudden Unexpected Death in Epilepsy (SUDEP). We tested how different types of seizures affect baroreflex sensitivity (BRS) and heart rate variability (HRV). We hypothesized that BRS and HRV would be reduced after bilateral convulsive seizures (BCS). Methods: We recorded blood pressure (BP), electrocardiogram (ECG) and oxygen saturation continuously in patients ( n = 18) with intractable epilepsy undergoing video-EEG monitoring. A total of 23 seizures, either focal seizures (FS, n = 14) or BCS ( n = 9), were analyzed from these patients. We used 5 different HRV measurements in both the time and frequency domains to study HRV in pre- and post-ictal states. We used the average frequency domain gain, computed as the average of the magnitude ratio between the systolic BP (BPsys) and the RR-interval time series, in the low-frequency (LF) band as frequency domain index of BRS in addition to the instantaneous slope between systolic BP and RR-interval satisfying spontaneous BRS criteria as a time domain index of BRS. Results: Overall, the post-ictal modulation of HRV varied across the subjects but not specifically by the type of seizures. Comparing pre- to post-ictal epochs, the LF power of BRS decreased in 8 of 9 seizures for patients with BCS; whereas following 12 of 14 FS, BRS increased. Similarly, spontaneous BRS decreased following 7 of 9 BCS. The presence or absence of oxygen desaturation was not consistent with the changes in BRS following seizures, and the HRV does not appear to be correlated with the BRS changes. These data suggest that a transient decrease in BRS and temporary loss of cardiovascular homeostatic control can follow BCS but is unlikely following FS. Significance: These findings indicate significant post-ictal autonomic dysregulation in patients with epilepsy following BCS. Further, reduced BRS following BCS, if confirmed in future studies on SUDEP cases, may indicate one quantifiable risk marker of SUDEP.

Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.

Background: Autonomic dysfunction contributes to atrial fibrillation (AF)., Objective: We hypothesized that polysomnogram (PSG)-based heart rate variability (HRV) autonomic function biomarkers are associated with incident AF and these associations are modified by measures of sleep disordered breathing (SDB)., Methods: 2350 participants of a multi-center prospective study (Outcomes of Sleep Disorders in Older Men Study) without baseline AF underwent sleep studies with incident adjudicated AF follow up (8.0 ± 2.6 years). Cox proportional hazard models were used to analyze sleep study-ECG spectral HRV indices [low and high frequency power (LF, HF), LF/HF] and time domain indices [mean of normal to normal beats (MNN), short and long term variability (STV, LTV) and STV/LTV] and premature atrial contractions (PACs) and incident AF (HR and 95% CI). Statistical interactions between HRV and SDB were examined. Models were adjusted for age, race, body mass index, waist circumference, cardiac medications, co-morbid diseases, alcohol use and study site., Results: Lower LF/HF and lower LF were associated with higher AF incidence (LF/HF Q1 vs. Q4: 1.46, 1.02-2.08, LF Q1 vs. Q4: 1.46, 1.02-2.10). Higher STV/LTV was associated with an increased risk of AF (p-trend= 0.028). The highest PAC quartile had a 3-fold increased AF risk (2.99, 1.94-4.62) compared to the lowest quartile. A significant interaction of obstructive apnea was observed in the LF-AF relationship (0.045)., Conclusions: Sleep-related reduced sympathovagal balance (LF/HF) and increased atrial ectopy are independently associated with future AF; a relationship modified by obstructive apnea.

The aim of this study is to investigate functional connectivity between right and left mesial temporal structures using cerebrocerebral evoked potentials. We studied seven patients with drug-resistant focal epilepsy who were explored with stereotactically implanted depth electrodes in bilateral hippocampi. In all patients cerebrocerebral evoked potentials evoked by stimulation of the fornix were evaluated as part of a research project assessing fornix stimulation for control of hippocampal seizures. Stimulation of the fornix elicited responses in the ipsilateral hippocampus in all patients with a mean latency of 4.6 ms (range 2-7 ms). Two patients (29 %) also had contralateral hippocampus responses with a mean latency of 7.5 ms (range 5-12 ms) and without involvement of the contralateral temporal neocortex or amygdala. This study confirms the existence of connections between bilateral mesial temporal structures in some patients and explains seizure discharge spreading between homotopic mesial temporal structures without neocortical involvement.

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes.

Objective: The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies., Materials and Methods: We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy., Results: Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology., Discussion: Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards., Conclusion: The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

Generalized tonic-clonic seizure (GTCS) is the commonest seizure type associated with sudden unexpected death in epilepsy (SUDEP). This study examined the semiological and electroencephalographic differences (EEG) in the GTCSs of adults as compared with those of children. The rationale lies on epidemiological observations that have noted a tenfold higher incidence of SUDEP in adults. We analyzed the video-EEG data of 105 GTCS events in 61 consecutive patients (12 children, 23 seizure events and 49 adults, 82 seizure events) recruited from the Epilepsy Monitoring Unit. Semiological, EEG, and 3-channel EKG features were studied. Periictal seizure phase durations were analyzed including tonic, clonic, total seizure, postictal EEG suppression (PGES), and recovery phases. Heart rate variability (HRV) measures including RMSSD (root mean square successive difference of RR intervals), SDNN (standard deviation of NN intervals), and SDSD (standard deviation of differences) were analyzed (including low frequency/high frequency power ratios) during preictal baseline and ictal and postictal phases. Generalized estimating equations (GEEs) were used to find associations between electroclinical features. Separate subgroup analyses were carried out on adult and pediatric age groups as well as medication groups (no antiepileptic medication cessation versus unchanged or reduced medication) during admission. Major differences were seen in adult and pediatric seizures with total seizure duration, tonic phase, PGES, and recovery phases being significantly shorter in children (p<0.01). Generalized estimating equation analysis, using tonic phase duration as the dependent variable, found age to correlate significantly (p<0.001), and this remained significant during subgroup analysis (adults and children) such that each 0.12-second increase in tonic phase duration correlated with a 1-second increase in PGES duration. Postictal EEG suppression durations were on average 28s shorter in children. With cessation of medication, total seizure duration was significantly increased by a mean value of 8s in children and 11s in adults (p<0.05). Tonic phase duration also significantly increased with medication cessation, and although PGES durations increased, this was not significant. Root mean square successive difference was negatively correlated with PGES duration (longer PGES durations were associated with decreased vagally mediated heart rate variability; p<0.05) but not with tonic phase duration. This study clearly points out identifiable electroclinical differences between adult and pediatric GTCSs that may be relevant in explaining lower SUDEP risk in children. The findings suggest that some prolonged seizure phases and prolonged PGES duration may be electroclinical markers of SUDEP risk and merit further study., (© 2013.)

Periictal autonomic dysregulation is best studied using a "polygraphic" approach: electroencephalography ([EEG]), 3-channel electrocardiography [ECG], pulse oximetry, respiration, and continuous noninvasive blood pressure [BP]), which may help elucidate agonal pathophysiologic mechanisms leading to sudden unexpected death in epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the most common seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias, and changes in skin conductance. Postictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP, and PGES has been found to correlate with post-GTCS autonomic dysregulation in some patients. Herein, we describe a patient with a GTCS in whom polygraphic measurements were obtained, including continuous noninvasive blood pressure recordings. Significant postictal hypotension lasting >60 s was found, which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon, and consequent cerebral hypoperfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged, and irrecoverable hypotension may comprise one potential SUDEP mechanism., (Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.)

High-grade aneurysmal subarachnoid hemorrhage patients are monitored in the ICU for up to 21 days, as they are at risk for complications such as vasospasm of cerebral arteries, cardiac arrhythmias and neurogenic stress cardiomyopathy. The diagnosis of these treatable complications is often delayed by the limitations of monitoring capabilities. We applied computational analysis to a cohort of 24 aneurysmal subarachnoid hemorrhage patients, to identify heart rate variability and ECG frequency profiles that may be potential biomarkers of severe vasospasm, reversible cardiomyopathy and death.

Objective: To investigate visual processing over the inferior temporal cortex (ITC) by recording intracranial event-related potentials (IERPs), and correlating the results with those of electrocortical stimulation mapping (ESM)., Methods: IERPs to word, non-word, and non-letter visual stimuli were recorded over the ITC in 6 patients with intractable epilepsy. Two patients underwent ESM of the same contacts., Results: IERPs were observed at 18 electrodes in 4 out of 6 patients. Nine electrodes showed early IERPs (peak latency ≤ 200 ms) over the posterior and middle ITC and 7 of them showed a following late ERP component, "early+late IERPs". Nine electrodes showed late IERPs (peak latency>200 ms) over the middle and anterior ITC. Among four electrodes showing language or visual phenomena by ESM, one electrode showed a short latency IERP, another electrode showed a late IERP, and the remaining two electrodes showed no IERPs., Conclusions: Our findings further support that the visual recognition occurred sequentially from posterior to anterior ITC. Dissociation of IERPs and ESM may be explained by the methodological difference., Significance: IERP study disclosed that visual recognition occurred sequentially from posterior to anterior ITC., (Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Background: We have previously shown an increased incidence of intermittent hypoxemia (IH) events in preterm infants with severe retinopathy of prematurity (ROP). Animal models suggest that patterns of IH events may play a role in ROP severity as well. We hypothesize that specific IH event patterns are associated with ROP in preterm infants., Methods: Variability in IH event duration, severity, and the time interval between IH events (≤80%, ≥10 s, and ≤3 min) along with the frequency spectrum of the oxygen saturation (SpO2) waveform were assessed., Results: Severe ROP was associated with (i) an increased mean and SD of the duration of IH event (P < 0.005), (ii) more variability (histogram entropy) of the time interval between IH events (P < 0.005), (iii) a higher IH nadir (P < 0.05), (iv) a time interval between IH events of 1-20 min (P < 0.05), and (v) increased spectral power in the range of 0.002-0.008 Hz (P < 0.05), corresponding to SpO2 waveform oscillations of 2-8 min in duration. Spectral differences were detected as early as 14 d of life., Conclusion: Severe ROP was associated with more variable, longer, and less severe IH events. Identification of specific spectral components in the SpO2 waveform may assist in early identification of infants at risk for severe ROP.

Objective: Skin-to-skin contact (SSC) promotes physiological stability and interaction between parents and infants. Analyses of EEG-sleep studies can compare functional brain maturation between SSC and non-SSC cohorts., Methods: Sixteen EEG-sleep studies were performed on eight preterm infants who received 8 weeks of SSC, and compared with two non-SSC cohorts at term (N=126), a preterm group corrected to term age and a full-term group. Seven linear and two complexity measures were compared (Mann-Whitney U test comparisons p<.05)., Results: Fewer REMs, more quiet sleep, increased respiratory regularity, longer cycles, and less spectral beta were noted for SSC preterm infants compared with both control cohorts. Fewer REMs, greater arousals and more quiet sleep were noted for SSC infants compared with the non-SSC preterms at term. Three right hemispheric regions had greater complexity in the SSC group. Discriminant analysis showed that the SSC cohort was closer to the non-SSC full-term cohort., Conclusions: Skin-to-skin contact accelerates brain maturation in healthy preterm infants compared with two groups without SSC., Significance: Combined use of linear and complexity analysis strategies offer complementary information regarding altered neuronal functions after developmental care interventions. Such analyses may be helpful to assess other neuroprotection strategies.

In this commentary we discuss the basic concept of an epileptogenicity level, which is variable in different brain regions and is a function of multiple factors including the basic epileptogenicity level, routine environmental or internal stimuli, and various triggering and causative factors. This concept blurs the distinction between focal versus generalized and between idiopathic versus symptomatic epilepsies. We suggest dropping the dichotomy idiopathic versus symptomatic and to instead simply list the different etiologic factors that increase the epileptogenicity level. On the other hand, even if there is a continuum between focal and generalized epilepsies, most epilepsies are either predominantly focal or predominantly generalized. It is useful to maintain this distinction (even if somewhat artificial) because focal epilepsies can be treated with epilepsy surgery, and all focal epilepsies tend to respond to the same type of anticonvulsants. Generalized epilepsies cannot be treated surgically and respond to different anticonvulsants depending on the semiologic type of seizures.

A blind watermarking algorithm for polysomnography (PSG) data in European Data Format (EDF) has been developed for the identification and attribution of shared data. This is accomplished by hiding a unique identifier in the phase spectrum of each PSG epoch using an undisclosed key so that a third party cannot retrieve the watermark without knowledge of the key. A pattern discovery algorithm is developed to find the watermark pattern even though the data may have been altered. The method is evaluated using 25 PSG studies from the Sleep Heart Health Study database. The integrity of the signal data was determined using time series measures of both the original and watermarked signals, and by determining its effect on scoring sleep stages from the PSG data. The results of the analysis indicate that the proposed watermarking method for PSG data is an effective and efficient way to identify shared data without compromising its intended use.