QUANTIFICATION OF PRETERM INFANT FEEDING COORDINATION: AN ALGORITHMIC APPROACH

Oral feeding competency is a primary requirement for preterm infant hospital release. Currently there is no widely accepted method to objectively measure oral feeding. Feeding consists primarily of the integration of three individual feeding events: sucking, breathing, and swallowing, and the objective of feeding coordination is to minimize aspiration. The purpose of this work was to quantify the infant feeding process from signals obtained during bottle feeding and ultimately develop a measure of feeding coordination. Sucking was measured using a pressure transducer embedded within a modified silicone bottle block. Breathing was measured using a thermistor embedded within nasal cannula, and swallowing was measured through the use of several different piezoelectric sensors. In addition to feeding signals, electrocardiogram (ECG) signals were obtained as an indicator of overall infant behavioral state during feeding. Event detection algorithms for the individual feeding signals were developed and validated, then used for the development of a measurement of feeding coordination. The final suck event detection algorithm was the result of an iterative process that depended on the validity of the signal model. As the model adapted to better represent the data, the accuracy and specificity of the algorithm improved. For the breath signal, however, the primary barrier to effective event detection was significant baseline drift. The frequency components of the baseline drift overlapped significantly with the breath event frequency components, so a time domain solution was developed. Several methods were tested, and it was found that the acceleration vector of the signal provided the most robust representation of the underlying breath signal while minimizing baseline drift. Swallow signal event detection was not possible due to a lack of available data resulting from problems with the consistency of the obtained signal. A robust method was developed for the batch processing of heart rate variability analysis. Finally a method of coordination analysis was developed based on the event detection algorithm outputs. Coordination was measured by determining the percentage of feeding time that consisted of overlapping suck and breath activity

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Acoustic and Respiratory Characteristics of Infant Vocalization

The purpose of this dissertation was to explore vibratory regime of infant phonation. The first study examined 1) differences in overall levels of acoustic and respiratory variables between different regimes and 2) differences in relationships between the acoustic and respiratory variables among regimes. The second study examined 3) the acoustic and respiratory ranges of modal phonation with respect to other regimes and 4) the range of modal phonation among infants of different ages. Two datasets were used in the study. Dataset I was acquired from eight infants of ages 8-18 months, and Dataset II from one infant of ages 4-6 months. Their vocalizations and respiratory movements were recorded during adult-interaction. Phonated segments were identified through waveform, spectrogram, and auditory inspection, and categorized into six mutually exclusive regimes (modal, pulse, loft, subharmonics, biphonation, and chaos). For each regime segment, the following measurements were made: fundamental frequency (F0), sound pressure level (SPL), expiratory slope, and relative lung volume at regime initiation. A series of linear mixed-effects model analysis and analysis of variance revealed differences in mean F0 between regimes, mean SPL, and mean. Correlations between the acoustic and respiratory variables differed among regimes, indicating their relationships were regime-dependent. The most revealing findings were that regime categories readily distributed into different regions of the intensity-frequency space, and that F0 ranges of modal regime tended to decrease with increasing age. In addition to modal, pulse, and loft distributing around the mid, low, and high intensity-frequency regions, respectively, biphonation and subharmonics were found between modal and loft ranges. The upper end of F0 range for pulse was much higher in infants compared to adults, however, biphonation and subharmonics rarely occurred between pulse and modal ranges. A range of modal F0 was about 500 Hz for the young infant in the vocal expansion stage, and about 200 Hz for older infants in the (post-)canonical stage. Although the results are tentative, this finding suggests that F0 variability decreases with age and phonation becomes more restricted to a lower end of an F0 range

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

Influence of Early Bilingual Exposure in the Developing Human Brain.

190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag