Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

© 2016 Torres, Smith, Mistry, Brincker and Whyatt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail.Peer reviewedFinal Published versio

Vastasyntyneen ja imeväisikäisen vauvan unenaikaisen hengitys- ja syketaajuuden tarkkailu puettavalla liikeanturilla

Vastasyntyneelle ja imeväisikäiselle nukkuminen on elintärkeä toiminto, ja se on välttämätöntä aivoverkkojen kehitykselle. Tiedetään, että huono unenlaatu aiheuttaa pitkällä tähtäimellä muun muassa kasvun hidastumista ja käyttäytymisongelmia. Imeväisikäisillä melko yleisesti esiintyvät unihäiriöt, kuten yöheräily ja nukahtamisvaikeudet aiheuttavat merkittävää rasitusta ja huolta vanhemmille. Objektiivisen mittausmenetelmän puutteen vuoksi ei ole kuitenkaan voitu selvittää imeväisikäisen unen kehittymistä kotiolosuhteissa. Tässä tutkimuksessa tarkasteltiin puettaviin pöksyihin kiinnitetyn liikeanturin ja EKG-kangaselektrodien soveltuvuutta vastasyntyneiden ja imeväisikäisten vauvojen unenaikaisen hengityksen ja sykkeen tarkkailuun. Tutkimuksen ensimmäisessä vaiheessa päiväaikaisten uni-EEG-tutkimuksien yhteydessä verrattiin liikeanturin mittauskanavien rekisteröimiä mittauskäyriä pietsoanturilla varustettuun hengitysvyöhön. Saatujen tutkimustuloksien perusteella liikeanturin gyroskooppi osoittautui tarkimmaksi hengitystaajuutta mittaavaksi parametriksi, kun taas anturin välittämä EKG-signaali oli tulkintakelpoisin osin luotettavaa. Tutkimuksen toisessa vaiheessa vauvaperheille annettiin unipöksyt ja älypuhelimet kotiin arvioidaksemme yön yli kestävää kotikäyttöä. Tutkimustulokset viittaavat siihen, että eri unitilojen tunnistaminen hengityksen vaihtelusta olisi todennäköisesti mahdollista gyroskooppisignaalista. Vanhemmilta saadun palautteen perusteella unipöksyjä pidettiin käytännöllisinä ja helppokäyttöisinä. Tulevissa tutkimuksissa tulisi keskittyä liikeanturin validointiin kliinisesti hyväksyttyjen mittausparametrien avulla, jotta algoritmeja voisi opettaa tunnistamaan eri uni-valve rytmejä automaattisesti. Näin puettava liikeanturi voisi tarjota tietoa vauvan luonnollisen unirakenteen kehittymisestä pitkällä aikavälillä. Lisäksi anturin kliininen validointi voisi mahdollistaa imeväisikäisten kardiorespiratoristen ongelmien ja liikehäiriöiden diagnostisen lisätyökalun kehittämisen.Sleep is one of the most vital functions of newborns and infants, and it is essential for neuronal network development. Therefore, long-term sleep disturbances have been associated with growth delays and behavioral disorders. Commonly reported infant sleep disturbances, such as night awakenings and difficulties falling asleep, cause distress to parents. Yet, the development of infant sleep in the home environment has not been fully elucidated due to lack of objective measurement parameters. In the current study, we assessed the feasibility of a motion sensor, attached to wearable pants, and ECG textile electrodes to monitor sleep-related respiration and heart rate of newborns and infants. First, we compared signals recorded by the motion sensor’s measurement channels to the standard respiratory piezo effort belt’s signal during daytime EEG recordings. According to our results, the motion sensor’s gyroscope proved to measure respiratory rate most accurately, while the ECG signal transmitted by the sensor was reliable in interpretable sections. We then provided wearable garments and smartphones to families with infants to assess overnight home-use. Our results indicate that different sleep states could likely be identified based on respiration fluctuation visible in the gyroscope’s signals. Moreover, the wearable system was considered practical and easy to use by the parents. Future studies should focus on validating the sensor with clinically approved measures, in order to train the algorithms to automatically identify different sleep-wake states. By doing so, the wearable sensor could provide information on natural infant sleep structure development over long time periods. Additionally, clinical validation of the sensor may result in the development of a companion diagnostic tool for infant cardiorespiratory and movement disorders

Southwest Research Institute assistance to NASA in biomedical areas of the technology

Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of −17.7 ± 226.9 g and −6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)Published versio