Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA ... United States Air Force Contract FA8721-05-C-0002, National Institutes of.
Using Physiological Signals to Predict Apnea in Preterm Infants J. R. Williamson∗ , D. W. Bliss∗ , D. W. Browne∗ , P. Indic† , E. Bloch-Salisbury† , and D. Paydarfar†‡ ∗ MIT
Lincoln Laboratory, Lexington, MA of Neurology, University of Massachusetts Medical School, Worcester, MA ‡ Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA
† Department
Abstract—Apnea of prematurity, a common developmental disorder in preterm infants, is implicated in long-term neurodevelopmental deficits. Preventative clinical interventions, such as mechanosensory stimulation, would benefit from predictive knowledge of when the patient is at high risk for apnea. In this study, the predictive utility of features derived from breathing rate and heart rate is explored. Specifically, the multiscale correlation structure of interbreath intervals and heartbeat intervals is used to train a patient-specific apnea prediction algorithm. The algorithm’s prediction results are significantly better than chance for three of the six patients it is evaluated on. These preliminary studies suggest that features of cardiopulmonary signals can anticipate the occurrence of clinically significant apneas in preterm infants.
I. I NTRODUCTION Apnea of prematurity (AOP) is a developmental disorder exhibited by virtually all infants born at less than 29 weeks gestation. AOP is characterized by long pauses in breathing (often greater than 10 seconds), followed shortly by reductions in heart rate (bradycardia) and in blood oxygen saturation (hypoxemia). Bradycardia and hypoxemia increase the risk for neurodevelopmental impairment, although the precise nature of this relationship is unknown [1]. The primary treatment for AOP is caffeine therapy, which has been shown to produce respiratory and neurodevelopmental improvements [2]. If additional effective treatments are found, these could augment caffeine therapy, producing either an additional reduction in the incidence and severity of apneic episodes, or a reduction in caffeine dose levels. One alternative treatment with promising potential is stochastic mechanosensory stimulation, which has been found to greatly reduce the incidence of respiratory pauses and oxygen desaturations below 85% [3]. In that study, the mechanosensory stimulation (a vibrating mattress) was presented in scheduled 10-minute periods. While the level of stimulation was low enough to avoid arousing the infants from This work was sponsored in part by the United States Air Force under United States Air Force Contract FA8721-05-C-0002, National Institutes of Health (NIH) Grants R01-HL49848 (D.P.), R01-HL071884 (D.P.), American Heart Association Scientist Development Grant (E.B.S.), intramural Pilot Project Programs of the University of Massachusetts Medical School and Mental Retardation Developmental Disabilities Research Center (D.P.) and the Hansjrg Wyss Institute for Biologically Inspired Engineering at Harvard University (D.P.). Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the United States Government.
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sleep, it may be more beneficial to apply the stimulation only when infants are at high risk for apnea. The goal of our work is to evaluate the ability to automatically detect periods of high apneic risk in preterm infants by using physiological signals that are readily available in a clinical setting. The current analysis uses features extracted from interbreath intervals (IBIs) and heartbeat intervals (RRIs), due to their known correlation with AOP [4],[5],[6] and their availability from all the patients in our data set. The computational approach is based on an algorithm origionally developed for analyzing electroencephalographic signals in order to predict epileptic seizures in adults [7]. Features are extracted that capture the correlation structure among IBIs and RRIs at multiple temporal scales. Patient-specific machine learning maps the features into a preapnea score, which indicates the likelihood that the patient is in a preapnea versus a normal background state. Finally, these scores are integrated over time to produce an apnea prediction score that indicates the probability the patient will experience an apnea during a defined prediction window. II. M ETHODS A. Patient data and preprocessing Our study was approved by the Committee on the Use of Humans as Experimental Subjects at the Massachusetts Institute of Technology and the Committee for the Protection of Human Subjects in Research at the University of Massachusetts Medical School. Physiological recordings of six preterm infants were obtained from the University of Massachusetts Memorial Neonatal Intensive Care Unit. These recordings were made as part of a larger study examining interventions to help reduce apnea in premature infants. Multiple channels of physiological measurements were recorded over 5-8 hours for each patient. Table I lists many pertinent details, including the total amount of data analyzed per patient, as well as the total number of apneas and of interapnea minutes that were eligible for prediction per patient (Section 2.2 describes how these were determined). Abdominal respiratory inductance plethysmography (Somonstar PT, Viasys healthcare, Yorbalinda, CA), electrocardiograph (ECG), and transcutaneous arterial blood oxygen saturation (SpO2 ) using a pulse oximeter attached to the infants foot or wrist were acquired using Embla N7000 recording system (Embla, Denver, CO). IBIs
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were extracted from abdominal respiratory movements and RRIs from the ECG signal. The quality of these signals can be adversely affected by gross body movements, which are typically present in about one quarter of recording times [3]. Despite these movement effects, the respiratory and ECG signals were always used when they were both available. Out of a total of 2,030 minutes of recorded data across all 6 patients, only 15 minutes were discarded due to the unavailability of either abdominal or ECG sensor data. Physically implausible interval values were automatically removed, and the remaining values were then linearly interpolated at 10 Hz, log-transformed, and converted to standard units (zero mean, unit variance) for each patient. The log transformation makes the IBI and RRI signals approximately normally distributed, and thus well described by second order statistics. This forms an appropriate basis for our feature extraction approach, which is described in Section III.C. B. Problem definition Severe apneas are typically classified as breathing pauses of 10s or more, followed shortly be bradycardia and hypoxemia. The automatic labeling of apneas in this study was based on co-occurrences of bradycardia and hypoxemia, which are the conditions of primary clinical concern due to their possible effect on infant development; significant bradycardia and hypoxemia episodes could result either from a single long breathing pause or a sequence of several short breathing pauses. Thresholds for detecting bradycardia and hypoxemia (HR
