One-minute Segments Research Articles

Abstract 137: Importance of Ventilation Rate When Assessing EtCO 2 Levels and Applying TOR Guidelines

Introduction: Ventilation is essential for the elimination of carbon dioxide (CO 2 ) from the body during CPR. However, variation in ventilation rate (VR) is an acknowledged confounder for the interpretation of EtCO 2 . VRs that are not according to guidelines alter measured EtCO 2 levels and make the decision of termination of resuscitation (TOR) based on EtCO 2 unreliable. Aim: To retrospectively analyze the effect of ventilation rate on levels of EtCO 2 for patients that do not achieve ROSC during prehospital resuscitation. Methods: Capnograms obtained from monitor-defibrillator recordings of non-ROSC adult OHCA cases, attended by TVF&R (OR, USA), were manually inspected to identify individual ventilations. Episodes were partitioned into consecutive 1-min segments, each characterized by its mean EtCO 2 and VR. We corrected the mean EtCO 2 values by adjusting the VRs to the recommended standard for patients with advanced airways (10 vpm), and obtained new EtCO 2,s values multiplying by the standardization factor 1.64 (1 - 0.91 VR ) published previously. Results: One-minute segments ( n = 3,791) from 244 cases were studied, for a total number of 27,312 ventilations. Of these, 206 cases had a duration equal or greater than 20-min, and 9.7% met the TOR criterium of EtCO 2 < 10 mmHg at 20-min. Median (IQR) VR, measured EtCO 2 and corrected EtCO 2,s were 8.1 (6.2-10.6) vpm, 27.7 (17.9-39.8) and 24.1 (16.0-33.7) mmHg, respectively, all at the 20-min mark. Median EtCO 2,s was at all times lower than measured EtCO 2 , with differences from 10 to 20 min ranging from 3.3 to 5.1 mmHg. Conclusion: TOR guidelines lack consensus, but the threshold of EtCO 2 < 10 mmHg at the 20-minute mark of resuscitation, as predictive of non-survivability, needs re-examination. Modern CPR introduces several confounders that produce higher measured EtCO 2 , including low VRs that are common but can be corrected. Based on a ventilation rate of 10 vpm, a new threshold should be considered as part of a multimodal approach in TOR guidelines.

A framework for BEB energy prediction using low-resolution open-source data-driven model

• We proposed a low-resolution open-source data-driven BEB energy prediction model. • The prediction model is applied to one-minute segments and full trips. • A sensitivity analysis is conducted to assess the model performance. • On full trips, the model is very accurate (more than 90% accuracy). • On one-minute segments, the model is acceptable (more than 80% accuracy). The accurate calculation of the energy consumption (E C ) rates of Battery-Electric Buses (BEBs) might be ambiguous due to uncertainties in amassing real-world operation data such as speed profiles, route topology, passenger loading, and weather conditions. Therefore, attaining an extensive validated E C prediction model is essential to surmount the challenges in collecting real-world data. In this respect, this study develops and assesses an open-source low-resolution data-driven framework to estimate BEB's E C in transit operation, using vehicular, operational, topological, and external parameters. Moreover, a three-step validation process is used to assess the proposed framework's performance. The results show that the prediction model provides a reasonable error margin (20%). The validation analyses show a powerful goodness-of-fit where the prediction model can explain more than 90% of the E C variation. The framework can superbly provide transit planners and agencies with an optimal transit operating profile that reinforces the BEB’s energy savings.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove.

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

ECG Signal Modeling Using Volatility Properties: Its Application in Sleep Apnea Syndrome.

This study presents and evaluates the mathematical model to estimate the mean and variance of single-lead ECG signals in sleep apnea syndrome. Our objective is to use the volatility property of the ECG signal for modeling. ECG signal is a stochastic signal whose mean and variance are time-varying. So, we propose to decompose this nonstationarity into two additive components; a homoscedastic Autoregressive Integrated Moving Average (ARIMA) and a heteroscedastic time series in terms of Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH), where the former captures the linearity property and the latter the nonlinear characteristics of the ECG signal. First, ECG signals are segmented into one-minute segments. The heteroskedasticity property is then examined through various tests such as the ARCH/GARCH test, kurtosis, skewness, and histograms. Next, the ARIMA model is applied to signals as a linear model and EGARCH as a nonlinear model. The appropriate orders of models are estimated by using the Bayesian Information Criterion (BIC). We assess the effectiveness of our model in terms of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The data in this article is obtained from the Physionet Apnea-ECG database. Results show that the ARIMA-EGARCH model performs better than other models for modeling both apneic and normal ECG signals in sleep apnea syndrome.

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Detection and Classification of Sleep Apnea and Hypopnea Using PPG and SpO 2 Signals.

In this work, a detection and classification method for sleep apnea and hypopnea, using photopletysmography (PPG) and peripheral oxygen saturation (SpO 2) signals, is proposed. The detector consists of two parts: one that detects reductions in amplitude fluctuation of PPG (DAP)and one that detects oxygen desaturations. To further differentiate among sleep disordered breathing events (SDBE), the pulse rate variability (PRV) was extracted from the PPG signal, and then used to extract features that enhance the sympatho-vagal arousals during apneas and hypopneas. A classification was performed to discriminate between central and obstructive events, apneas and hypopneas. The algorithms were tested on 96 overnight signals recorded at the UZ Leuven hospital, annotated by clinical experts, and from patients without any kind of co-morbidity. An accuracy of 75.1% for the detection of apneas and hypopneas, in one-minute segments,was reached. The classification of the detected events showed 92.6% accuracy in separating central from obstructive apnea, 83.7% for central apnea and central hypopnea and 82.7% for obstructive apnea and obstructive hypopnea. The low implementation cost showed a potential for the proposed method of being used as screening device, in ambulatory scenarios.

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.

Noise exposure of cyclists in Ho Chi Minh City: A spatio-temporal analysis using non-linear models

Abstract The auditory and non-auditory effects of noise on health are today well known. A number of studies have recently shown that cyclists represent a population that is especially strongly exposed to noise in urban environments, particularly because of their proximity to road traffic. These studies have however very rarely examined the case of the cities of the South, despite the fact that these cities are known to have higher levels of exposure to noise. The objective of this article is therefore to analyze variations in cyclists’ levels of noise exposure in Ho Chi Minh City (Vietnam) by integrating three dimensions: that is, the characteristics of the trip, neighbourhood effects, and the temporal dimension. Three participants cycled more than 1000 km in the city, equipped with noise dosimeters and GPS watches, for a total of 3300 one-minute segments, each of which measured noise intensity (LAeq dB(A)). It is not surprising that the levels of exposure registered were particularly high (average 78.8 dB(A)), notably compared with earlier studies conducted in Europe and North America. The use of generalized additive models in particular made it possible to highlight the effect of the complex interaction between the slope and the cyclists’ speed on the levels of noise exposure, the effect and duration of the morning and afternoon rush hour periods, and the spatial distribution of residual environmental noise. One of the main findings was that in sectors with the highest levels of exposure to noise (central neighbourhoods or areas near the airport), these levels are up to four times higher than in more peripheral or rural areas.

Platform Session – Evoked potentials & NIOM: Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed?

Introduction To investigate the effect of systematic electrode reduction from a common 10–20 EEG system on pattern detection sensitivity (SEN). Methods Two reviewers rated 17,130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. Results High automated baseline SENs (84.99–93.39%) and SPEs (90.05–95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the “hairline + vertex” montage. While the “forehead + behind ear” montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. Conclusion For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.

Neural encoding of phonetic features relates to phonological processing in PPA

Neural encoding of phonetic features relates to phonological processing in PPA

Reduced electrode arrays for the automated detection of rhythmic and periodic patterns in the intensive care unit: Frequently tried, frequently failed?

To investigate the effect of systematic electrode reduction from a common 10-20 EEG system on pattern detection sensitivity (SEN). Two reviewers rated 17130 one-minute segments of 83 prospectively recorded cEEGs according to the ACNS standardized critical care EEG terminology (CCET), including burst suppression patterns (BS) and unequivocal electrographic seizures. Consensus annotations between reviewers were used as a gold standard to determine pattern detection SEN and specificity (SPE) of a computational algorithm (baseline, 19 electrodes). Electrodes were than reduced one by one in four different variations. SENs and SPEs were calculated to determine the most beneficial assembly with respect to the number and location of electrodes. High automated baseline SENs (84.99-93.39%) and SPEs (90.05-95.6%) were achieved for all patterns. Best overall results in detecting BS and CCET patterns were found using the "hairline+vertex" montage. While the "forehead+behind ear" montage showed an advantage in detecting ictal patterns, reaching a 15% drop of SEN with 10 electrodes, all montages could detect BS sufficiently if at least nine electrodes were available. For the first time an automated approach was used to systematically evaluate the effect of electrode reduction on pattern detection SEN in cEEG. Prediction of the expected detection SEN of specific EEG patterns with reduced EEG montages in ICU patients.

Narrative and emotion integration processes in emotion-focused therapy for complex trauma: an exploratory process-outcome analysis

ABSTRACTThe Narrative-Emotion Process Coding System (NEPCS) is a behavioral coding system that identifies eight client markers: Story, Empty Story, Unstoried Emotion, Inchoate Story, Same Old Story, Competing Plotlines Story, Unexpected Outcome Story, and Discovery Story. Each marker varies in the degree to which specific narrative and emotion process indicators are represented in one-minute time segments drawn from videotaped therapy sessions. As enhanced integration of narrative and emotional expression has previously been associated with recovery from complex trauma, the purpose of the present pilot study was to examine the contribution of within session NEPCS markers to treatment outcomes for clients undergoing Emotion-Focused Therapy for Trauma (EFTT). The NEPCS was applied to two early, two middle, and two late videotaped therapy sessions selected from two recovered and two unchanged complex trauma EFTT clients. Exploratory statistical analyses established that unchanged clients expressed significantly more Unstoried Emotion than recovered clients, and a significant stage by outcome interaction effect was found for Competing Plotlines Story, Unexpected Outcome Story, and Discovery Story. Finally, limitations of the current study are identified and implications for future research and clinical practice are addressed.

Blinking and Driving: the Influence of Saccades and Cognitive Workload

Purpose: The aim of this study was to investigate the joint influence of cognitive demands and large amplitude saccades on spontaneous blink rate.Materials and Methods: Twenty healthy volunteers were enrolled in the study and instructed to follow a 60-minute real-life driving circuit while a video camera recorded ocular movements and blinking. Five different complexity levels were predefined in terms of driving difficulty and traffic intensity, that is, cognitive workload. Five one-minute segments were selected for each complexity level in each driving trial, whereupon spontaneous blink rate and horizontal saccades were monitored. Saccades were classified according to their amplitude and blink-saccade pairs were recorded.Results: Albeit showing a high inter-subject variability, spontaneous blink rate and saccades were consistent within the same complexity level. At different complexity levels, no statistically significant difference in spontaneous blink rate was encountered, with an average of 20.3 ± 1.6 blinks/minute (mean ± SD). The number of large amplitude gaze shifts increased with the level of complexity (F = 15.403; p < 0.001). An average of 87.5% of large amplitude saccades were accompanied by a blink, and this percentage was similar for all complexity levels, that is, the number of blink-saccade pairs increased at higher complexity levels (F = 20.597; p < 0.001).Conclusions: In a complex, dynamic visual setting, spontaneous blinking accompanying large amplitude saccades may help in counteracting the reduction in blink rate associated with high cognitive demands.

Crew Performance in Spacelab

This paper discusses two studies utilizing videotaped footage of crew members working in the micro-g environments aboard two flights of Spacelab. Various aspects of crew restraint, stabilization, manipulation of controls, and moving about were observed, operationally defined, and quantified by observing videotaped scenes of spacelab crew members. In the first study four performance behaviors were quantified in order to provide estimates of their frequency of occurrence and variation over the course of each of the flights: (1) movement between work stations (translation), (2) holding on with one hand to stabilize while working (Hand Hold), (3) use of provided foot restraints while working (Foot Restraint), (4) out-of-control body movements (Struggle). Eighty one-minute segments were randomly chosen of which 59 met the restrictive definitions for coding. Four raters timed all behaviors using stopwatches. Inter-rater reliabilities (Chronbach's ALPHA) varied between .87 and .96 for the four behaviors. The averaged responses of the four raters showed the following mean proportions of time for each of the behaviors: Translating=9.4%, Hand Hold=32.2%, Foot Restraint=35.3%, and Struggle=3.7%. Because more than a third of a crew member's time was spent inefficiently holding on with one hand while trying to work with the other, a second study was conducted exploring the use of foot restraints and hand stabilization. In both flights hand stabilization occurred more often when using a single cloth foot loop than when using two, t(90)=2.37, p &lt; .01. Crew members increased their use of double foot restraints with experience.

Rapport expressed through nonverbal behavior

Family Medicine residents were videotaped in interviews with a new and a return-visit patient. Two coders recorded nonverbal behavior performed by the residents for two, one-minute segments of each interview. Categories of movement included: proxemic behaviors of distance, orientation, and trunk lean, and head, hand/arm, and leg/foot movement, facial expression, and direction of gaze.