Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement.

BACKGROUND: Digital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery. METHODS: This was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection. RESULTS: Sixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected. CONCLUSIONS: Future work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

Information Based Similarity Analysis of Oxygen Saturation Recordings to Detect Pulmonary Hypertension in Preterm Infants.

Pulmonary hypertension (PH) is a complex cardiovascular condition associated with multiple morbidities and mortality risk in preterm infants. PH often complicates the clinical course of infants who have bronchopulmonary dysplasia (BPD), a more common lung disease in these neonates, causing respiratory deterioration and an even higher risk of mortality. While risk factors and prevalence of PH are not yet well defined, early screening and management of PH in infants with BPD are recommended by consensus guidelines from the American Heart Association. In this study, we propose a screening method for PH by applying a signal analysis technique to oxygen saturation in infants. Oxygen saturation data from infant groups with BPD (41 with and 60 without PH), recorded prior to their clinical PH diagnosis were analyzed in this study. An information-based similarity approach was applied to quantify the regularity of SpO2 fluctuations represented as binary words between adjacent five-minute segments. Similarity indices (SI) were observed to be lower in subjects with PH compared to those with BPD alone (p<0.001). These measures were also assessed for performance in screening for PH. SI of 7-bit words, exhibited 80% detection accuracy, 76% sensitivity and specificity of 83%. This index also exhibited a cross-validated mean (SD) F1-score of 0.80 (0.08) ensuring that sensitivity and recall of the screening were balanced. Similarity analysis of oxygen saturation patterns is a novel technique that can be potentially developed into a signal based early PH detection method to support clinical decision and care in this vulnerable population.

Comparison of oxygen supplementation in very preterm infants: Variations of oxygen saturation features and their application to hypoxemic episode based risk stratification.

Background: Oxygen supplementation is commonly used to maintain oxygen saturation (SpO2) levels in preterm infants within target ranges to reduce intermittent hypoxemic (IH) events, which are associated with short- and long-term morbidities. There is not much information available about differences in oxygenation patterns in infants undergoing such supplementations nor their relation to observed IH events. This study aimed to describe oxygenation characteristics during two types of supplementation by studying SpO2 signal features and assess their performance in hypoxemia risk screening during NICU monitoring. Subjects and methods: SpO2 data from 25 infants with gestational age <32 weeks and birthweight <2,000 g who underwent a cross over trial of low-flow nasal cannula (NC) and digitally-set servo-controlled oxygen environment (OE) supplementations was considered in this secondary analysis. Features pertaining to signal distribution, variability and complexity were estimated and analyzed for differences between the supplementations. Univariate and regularized multivariate logistic regression was applied to identify relevant features and develop screening models for infants likely to experience a critically high number of IH per day of observation. Their performance was assessed using area under receiver operating curves (AUROC), accuracy, sensitivity, specificity and F1 scores. Results: While most SpO2 measures remained comparable during both supplementations, signal irregularity and complexity were elevated while on OE, pointing to more volatility in oxygen saturation during this supplementation mode. In addition, SpO2 variability measures exhibited early prognostic value in discriminating infants at higher risk of critically many IH events. Poincare plot variability at lag 1 had AUROC of 0.82, 0.86, 0.89 compared to 0.63, 0.75, 0.81 for the IH number, a clinical parameter at observation times of 30 min, 1 and 2 h, respectively. Multivariate models with two features exhibited validation AUROC > 0.80, F1 score > 0.60 and specificity >0.85 at observation times ≥ 1 h. Finally, we proposed a framework for risk stratification of infants using a cumulative risk score for continuous monitoring. Conclusion: Analysis of oxygen saturation signal routinely collected in the NICU, may have extensive applications in inferring subtle changes to cardiorespiratory dynamics under various conditions as well as in informing clinical decisions about infant care.

Stochastic Modeling of Inter-Hypoxemia Intervals in Preterm Infants.

Hypoxemia, characterized by low blood oxygen levels is pervasive in preterm infants and is associated with development of multiple adverse cardiovascular morbidities. In clinical practice, it is often quantified using frequency, pattern and time spent in it. A predictive tool of hypoxemia occurrence will aid clinicians in risk stratifying infant oxygenation patterns and improving personalized care. As a first step towards this goal in characterizing the underlying temporal processes, we studied inter-hypoxemia interval distributions in preterm infants on oxygen supplementation. We derived regression relationships of characterizing parameters of the distributions with gestational age and birth weight of infants. The modeling and goodness of fit tests of pooled and individual inter-hypoxemia intervals indicated that the inverse Gaussian and Birnbaum Saunders distributions fit well over short time scales and the lognormal at longer time scales. Information from distribution modeling may provide insights into hypoxemia recurrence times and be helpful in developing models to predict severe hypoxemic events that may be translated to personalized care in clinical settings. Clinical relevance - Understanding the stochastic nature of temporal processes underlying hypoxemia in preterm infants is a critical step towards developing predictive models for their occurrence. This may potentially aid in the neonatal care and treatment of these vulnerable infants.

Mutual information analysis of EEG signals indicates age-related changes in cortical interdependence during sleep in middle-aged versus elderly women.

Elderly subjects exhibit declining sleep efficiency parameters with longer time spent awake at night and greater sleep fragmentation. In this article, we report on the changes in cortical interdependence during sleep stages between 15 middle-aged (range: 42-50 years) and 15 elderly (range: 71-86 years) women subjects. Cortical interdependence assessed from EEG signals typically exhibits increasing levels of correlation because human subjects progress from wake to deeper stages of sleep. EEG signals acquired from previously existing polysomnogram datasets were subjected to mutual information analysis to detect changes in information transmission associated with change in sleep stage and to understand how age affects the interdependence values. We observed a significant reduction in the interdependence between central EEG signals of elderly subjects in nonrapid eye movement and rapid eye movement stage sleep in comparison with middle-aged subjects (age group effect: elderly versus middle aged P < 0.001, sleep stage effect: P < 0.001, interaction effect between age group and sleep stage: P = 0.007). A narrowband analysis revealed that the reduction in mutual information was present in delta, theta, and sigma frequencies. These findings suggest that the lowered cortical interdependence in sleep of elderly subjects may indicate independently evolving dynamic neural activities at multiple cortical sites. The loss of synchronization between neural activities during sleep in the elderly may make these women more susceptible to localized disturbances that could lead to frequent arousals.

Transient decoupling of cortical EEGs following arousals during NREM sleep in middle-aged and elderly women.

Spontaneous cortical arousals in non-REM sleep increase with age and contribute to sleep fragmentation in the elderly. EEG spectral power in the faster frequencies exhibits well-described shifts during arousals. On the other hand, EEG activities also exhibit correlations, which are interpreted as an index of interdependence between distant cortical neural activities. The possibility of changes to the interdependence between cortical regions due to an arousal has not been considered. In this work, using previously recorded C3A2 and C4A1 EEG signals from two groups of adults, middle-aged (42-50 years) and elderly (71-86 years) women, we examined the effects of spontaneous arousals in NREM sleep on cortical interdependence. We quantified the auto- and cross-correlations in these signals using mutual information and characterized these correlations in periods before the onset and following the end of arousals. The pre-arousal period exhibited significantly higher interdependence between central regions than that following the arousal in both age groups (middle-aged: p=0.004, elderly: p<0.0001). Also, for both EEG signals the auto mutual information had a faster rate of decay, implying lower signal predictability, following the arousal than prior to it (both age groups, p<0.0001). These results indicate that the state of the cortex is different after, compared to before, the arousal even when the spectral power changes characteristic of an arousal are no longer visible. The findings suggest that the state following an arousal characterized by lower interdependence may resemble a more vigilant period during which the system may be vulnerable to more arousals.

Complexity quantification of dense array EEG using sample entropy analysis.

In this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.