Carola van Pul

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10–20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA <32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27+4 to 36+3 wk of postmenstrual age (PMA, GA + postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5–1 Hz), delta 2 (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

Automatic burst detection for the EEG of the preterm infant

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

Fetal heart rate variability during pregnancy, obtained from non-invasive electrocardiogram recordings

Non‐invasive spectral analysis of fetal heart rate variability is a promising new field of fetal monitoring. To validate this method properly, we studied the relationship between gestational age and the influence of fetal rest–activity state on spectral estimates of fetal heart rate variability.

Topography of maturational changes in EEG burst spectral power of the preterm infant with a normal follow-up at 2 years of age

OBJECTIVE To quantify the electroencephalography (EEG) burst frequency spectrum of preterm infants by automated analysis and to describe the topography of maturational change in spectral parameters. METHODS Eighteen preterm infants <32weeks gestation and normal neurological follow-up at 2years underwent weekly 4-h EEG recordings (10-20 system). The recordings (n=77) represent a large variability in postmenstrual age (PMA, 28-36weeks). We applied an automated burst detection algorithm and performed spectral analysis. The frequency spectrum was divided into δ1 (0.5-1Hz), δ2 (1-4Hz), θ (4-8Hz), α (8-13Hz) and β (13-30Hz) bands. Spectral parameters were evaluated as a function of PMA by regression analysis. Results were interpolated and topographically visualised. RESULTS The majority of spectral parameters show significant change with PMA. Highest correlation is found for δ and θ band. Absolute band powers decrease with increasing PMA, while relative α and β powers increase. Maturational change is largest in frontal and temporal region. CONCLUSIONS Topographic distribution of maturational changes in spectral parameters corresponds with studies showing ongoing gyration and postnatal white matter maturation in frontal and temporal lobes. SIGNIFICANCE Computer analysis of EEG may allow objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment.

Features of Heart Rate Variability Capture Regulatory Changes During Kangaroo Care in Preterm Infants

Objective To determine whether heart rate variability (HRV) can serve as a surrogate measure to track regulatory changes during kangaroo care, a period of parental coregulation distinct from regulation within the incubator. Study design Nurses annotated the starting and ending times of kangaroo care for 3 months. The pre‐kangaroo care, during‐kangaroo care, and post‐kangaroo care data were retrieved in infants with at least 10 accurately annotated kangaroo care sessions. Eight HRV features (5 in the time domain and 3 in the frequency domain) were used to visually and statistically compare the pre‐kangaroo care and during‐kangaroo care periods. Two of these features, capturing the percentage of heart rate decelerations and the extent of heart rate decelerations, were newly developed for preterm infants. Results A total of 191 kangaroo care sessions were investigated in 11 preterm infants. Despite clinically irrelevant changes in vital signs, 6 of the 8 HRV features (SD of normal‐to‐normal intervals, root mean square of the SD, percentage of consecutive normal‐to‐normal intervals that differ by >50 ms, SD of heart rate decelerations, high‐frequency power, and low‐frequency/high‐frequency ratio) showed a visible and statistically significant difference (P < .01) between stable periods of kangaroo care and pre‐kangaroo care. HRV was reduced during kangaroo care owing to a decrease in the extent of transient heart rate decelerations. Conclusion HRV‐based features may be clinically useful for capturing the dynamic changes in autonomic regulation in response to kangaroo care and other changes in environment and state.

Pattern discovery in critical alarms originating from neonates under intensive care

Patient monitoring generates a large number of alarms, the vast majority of which are false. Excessive non-actionable medical alarms lead to alarm fatigue, a well-recognized patient safety issue. While multiple approaches to reduce alarm fatigue have been explored, patterns in alarming and inter-alarm relationships, as they manifest in the clinical workspace, are largely a black-box and hamper research efforts towards reducing alarms. The aim of this study is to detect opportunities to safely reduce alarm pressure, by developing techniques to identify, capture and visualize patterns in alarms. Nearly 500 000 critical medical alarms were acquired from a neonatal intensive care unit over a 20 month period. Heuristic techniques were developed to extract the inter-alarm relationships. These included identifying the presence of alarm clusters, patterns of transition from one alarm category to another, temporal associations amongst alarms and determination of prevalent sequences in which alarms manifest. Desaturation, bradycardia and apnea constituted 86% of all alarms and demonstrated distinctive periodic increases in the number of alarms that were synchronized with nursing care and enteral feeding. By inhibiting further alarms of a category for a short duration of time (30 s/60 s), non-actionable physiological alarms could be reduced by 20%. The patterns of transition from one alarm category to another and the time duration between such transitions revealed the presence of close temporal associations and multiparametric derangement. Examination of the prevalent alarm sequences reveals that while many sequences comprised of multiple alarms, nearly 65% of the sequences were isolated instances of alarms and are potentially irreducible. Patterns in alarming, as they manifest in the clinical workspace were identified and visualized. This information can be exploited to investigate strategies for reducing alarms.

Accuracy and precision of CPET equipment: A comparison of breath-by-breath and mixing chamber systems

Cardiopulmonary exercise testing (CPET) has become an important diagnostic tool for patients with cardiorespiratory disease and can monitor athletic performance measuring maximal oxygen uptake Vo2; max. The aim of this study is to compare the accuracy and precision of a breath-by-breath and a mixing chamber CPET system, using two methods. First, this study developed a (theoretical) error analysis based on general error propagation theory. Second, calibration measurements using a metabolic simulator were performed. Error analysis shows that the error in oxygen uptake (Vo2) and carbon dioxide production (Vco2) is smaller for mixing chamber than for breath-by-breath systems. In general, the error of the flow sensor δV, the error in temperature of expired air δTB and the delay time error δtdelay are significant sources of error. Measurements using a metabolic simulator show that breath-by-breath systems are less stabile for different values of minute ventilation than mixing chamber systems.

Effects of midazolam and lidocaine on spectral properties of the EEG in full-term neonates with stroke

Assessment of the neonatal EEG may be hampered by drug-specific changes in electrocortical activity. To quantify effects of a loading dose of midazolam and lidocaine on the EEG frequency spectrum of full-term neonates with perinatal arterial ischemic stroke (PAIS), 11 full-term infants underwent multi-channel amplitude-integrated EEG (aEEG) and EEG recordings. During recording, midazolam and/or lidocaine were administered as anti-epileptic drug. Retrospectively, we performed spectral analysis on 4-h EEG segments around the loading dose. The frequency spectrum was divided in δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz) and β (13-30 Hz) bands. Midazolam induced immediate suppression of the aEEG background pattern for 30-60 min. Spectral EEG analysis showed decreased total and absolute frequency band powers. Relative δ power decreased, θ power increased while α and β powers remained constant. Lidocaine induced no aEEG background pattern suppression. Total and absolute EEG band powers were unchanged. Relative δ power decreased, θ and α power increased and β power remained constant. Effects of lidocaine were more pronounced in the stroke-affected hemisphere. In conclusions, both drugs induced a shift from low to higher frequency electrocortical activity. Additionally, midazolam reduced total EEG power. These spectral changes differ from those seen in adult studies.

The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

Aim Alarm fatigue is a well-recognized patient safety concern in intensive care settings. Decreased nurse responsiveness and slow response times to alarms are the potentially dangerous consequences of alarm fatigue. The aim of this study was to determine the factors that modulate nurse responsiveness to critical patient monitor and ventilator alarms in the context of a private room neonatal intensive care setting. Methods The study design comprised of both a questionnaire and video monitoring of nurse-responsiveness to critical alarms. The Likert scale questionnaire, comprising of 50 questions across thematic clusters (critical alarms, yellow alarms, perception, design, nursing action, and context) was administered to 56 nurses (90% response rate). Nearly 6000 critical alarms were recorded from 10 infants in approximately 2400 hours of video monitoring. Logistic regression was used to identify patient and alarm-level factors that modulate nurse-responsiveness to critical alarms, with a response being defined as a nurse entering the patient’s room within the 90s of the alarm being generated. Results Based on the questionnaire, the majority of nurses found critical alarms to be clinically relevant even though the alarms did not always mandate clinical action. Based on video observations, for a median of 34% (IQR, 20–52) of critical alarms, the nurse was already present in the room. For the remaining alarms, the response rate within 90s was 26%. The median response time was 55s (IQR, 37-70s). Desaturation alarms were the most prevalent and accounted for more than 50% of all alarms. The odds of responding to bradycardia alarms, compared to desaturation alarms, were 1.47 (95% CI = 1.21–1.78; <0.001) while that of responding to a ventilator alarm was lower at 0.35 (95% CI = 0.27–0.46; p <0.001). For every 20s increase in the duration of an alarm, the odds of responding to the alarm (within 90s) increased to 1.15 (95% CI = 1.1–1.2; p <0.001). The random effect per infant improved the fit of the model to the data with the response times being slower for infants suffering from chronic illnesses while being faster for infants who were clinically unstable. Discussion Even though nurses respond to only a fraction of all critical alarms, they consider the vast majority of critical and yellow alarms as useful and relevant. When notified of a critical alarm, they seek waveform information and employ heuristics in determining whether or not to respond to the alarm. Conclusion Amongst other factors, the category and duration of critical alarms along with the clinical status of the patient determine nurse-responsiveness to alarms.

Automatic detection of burst synchrony in preterm infants

Electroencephalographic characteristics are useful in assessment of the functional status of specific neuronal connections relative to postmenstrual age. Interhemispheric burst synchrony (IBS) is a measure of the functional connectivity between the hemispheres in the maturing preterm brain. An algorithm was developed to assess IBS and was used in a prospective, longitudinal EEG study on 18 very preterm infants (< 32 weeks gestational age) with normal follow-up at 2 years of age. The preterm infants underwent weekly 4-hour multi-channel EEG recordings, resulting in n = 77 EEGs. After automated detection of bursts, the algorithm defines the start and end of interhemispheric synchronous burst activity, based on selection criteria found in literature. The algorithm was designed to emulate visual inspection, providing objective results in an automated manner. This approach may be applied in clinical use and open novel avenues to automated analysis in EEG monitoring and, moreover, it may facilitate assessment of the functional status of interhemispheric connections. As such, assessment of low interhemispheric synchrony may be associated with brain injury.