Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: +e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. +us, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. +e NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. +e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Non-Nutritive Suck Assessment Tool Development to Characterize Sucking Patterns in Infant with Various Hunger Levels

Sucking abilities are critical in early infant development, and the patterns of non-nutritive suck (NNS) have been found to potentially predict neurodevelopmental issues in the future. Proper NNS assessments are essential to ensure valid conclusions. Previous studies have shown that the level of infant arousal significantly affects NNS patterns. However, the author did not find any studies that observed the influence of infant hunger levels on NNS patterns. Therefore, this study aimed to develop an NNS assessment tool to characterize NNS patterns in infants with various hunger levels. The NNS assessment was conducted using a pressure transducer connected to a pacifier. The results showed that the level of hunger significantly affected the intra-burst frequency and the sucking pressure. The more hungry the infant, the more frequent the intra-burst frequency became, while the sucking pressure tended to decrease. The intra-burst frequency of infant sucking was 2.3, 2.46, and 2.5 Hz on average for a relative hunger index of 0.67, 0.83, and 1.0, respectively. The NNS pressure of infant sucking was 6.31, 4.51, and 2.62 kPa on average for a relative hunger index of 0.67, 0.83, and 1.0, respectively. This study's results suggest that during NNS assessments, the measurement time should consider the next feeding schedule for the infant

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: +e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. +us, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. +e NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. +e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Re(H)abilitar no inicio do ciclo vital

Relat?rio de Est?gio de Natureza Profissional no ?mbito do Mestrado em Enfermagem de Reabilita??o apresentada na Escola Superior de Sa?de do Instituto Polit?cnico de Viana do CasteloEnquadramento: Este relat?rio assenta no percurso formativo de um est?gio de natureza profissional que integra uma vertente investigativa, dedicado ? idade pedi?trica, focado em duas importantes causas de morbilidade, sens?veis aos contributos da enfermagem de reabilita??o ?as doen?as respirat?rias e a prematuridade. Assim, ? abordada a Reabilita??o Respirat?ria na crian?a, sintetizando as suas particularidades anat?micas e fisiol?gicas, as diferentes t?cnicas de reabilita??o funcional respirat?ria desenvolvidas durante o est?gio e outros aspetos relevantes da pr?tica cl?nica. Segue-se o enquadramento da enfermagem de reabilita??o na Neonatologia, onde a sobreviv?ncia de neonatos prematuros e em situa??es cl?nicas de grande complexidade real?a a necessidade de garantir cuidados de sa?de capazes de promover o melhor desenvolvimento poss?vel para que cada crian?a atinja o seu m?ximo potencial. Sendo a aquisi??o de autonomia alimentar um dos principais desafios da prematuridade, neste trabalho aprofunda-se esta problem?tica e ? apresentado o estudo desenvolvido nesta ?rea particular. Metodologia: Atrav?s de uma abordagem cr?tica-reflexiva s?o explorados conceitos e quest?es que v?o de encontro ao desenho do est?gio. No ?mbito da investiga??o, foi conduzido um estudo quase-experimental, com o objetivo de identificar os efeitos da implementa??o do PIOMI na alimenta??o oral, nos RNPT com idade gestacional entre as 33-35 semanas, internados numa neonatologia de n?vel terci?rio portuguesa. A amostra incluiu 10 crian?as, divididas em dois grupos, sendo que um recebeu os cuidados standard da unidade e outro grupo recebeu o protocolo Premature Infant Oral Motor Intervention, durante 14 dias consecutivos, uma vez por dia. Resultados: A enfermagem de reabilita??o na idade pedi?trica, mantendo as suas ra?zes assentes nas teorias do autocuidado e das transi??es que suportam os cuidados de reabilita??o, incorpora modelos de cuidados centrados na fam?lia e de cuidados atraum?ticos, atentando ?s quest?es desenvolvimentais. A Reabilita??o Funcional Respirat?ria contribui para ganhos em sa?de na crian?a com doen?a respirat?ria, sendo essencial adequar as t?cnicas ?s particularidades anat?micas, fisiol?gicas e comportamentais. No contexto de uma unidade de neonatalogia, os modelos neuroprotetores e desenvolvimentais s?o fundamentais para enquadrar todos os cuidados, incluindo os cuidados da Enfermagem de Reabilita??o, que potenciam o desenvolvimento, desde uma fase precoce, agindo sobre as fun??es respirat?ria, motora, sensorial, alimenta??o e educa??o parental. Os programas de reabilita??o da fun??o alimentar incluem t?cnicas de estimula??o das compet?ncias orais - entre as quais a estimula??o oromotora - enquadradas numa abordagem neuroprotetora e desenvolvimental, instrumentos de avalia??o das compet?ncias orais e educa??o parental. No estudo desenvolvido, verificou-se que o protocolo de interven??o oromotora selecionado contribuiu significativamente para a matura??o das compet?ncias orais, mas n?o para a redu??o do tempo de transi??o da gavagem para a ingest?o oral aut?noma, nem para o tempo de hospitaliza??o. Contudo, o grupo PIOMI iniciou o treino de alimenta??o oral numa idade corrigida tendencialmente mais precoce. Conclus?es: A aplica??o do PIOMI por EEER contribuiu para a matura??o das compet?ncias orais no RNPT e n?o impactou negativamente a evolu??o ponderal, mas n?o contribui para diminuir o n?mero de dias necess?rios para a aquisi??o de autonomia alimentar, nem o tempo de hospitaliza??o.Framing: This report lays on the educational path crossed along a clinical practicum in pediatric age, focusing on two important causes of morbidity in the pediatric age, both gaining with rehabilitation nursing expertise ? respiratory diseases and prematurity. Thus, respiratory rehabilitation in children is addressed, synthesizing the anatomical and physiological particularities of the child, the different techniques of respiratory functional rehabilitation developed during the practicum and other relevant aspects of clinical practice. The following is the framework of rehabilitation nursing in Neonatology, where the survival of preterm neonates and clinical situations of great complexity highlights the need to ensure health care capable of promoting the best possible development so that each child reaches its maximum potential. Since the acquisition of independent oral feeding is one of the main challenges of prematurity, this work deepens this problem and the study developed in this specific area is presented. Methodology: Using a critical-reflexive approach, the author explores concepts and issues in line with the design of the practicum. In the scope of the research, a quasi-experimental study was conducted with the aim of identifying the effects of an oral motor stimulation program in the transition from tube to full oral feeding in preterm infants between 33-35 weeks of gestational or post-menstrual age, hospitalized in a Portuguese level 3 neonatology unit. The sample included 10 children, divided into two groups, one of which received standard care from the unit and another group received the Premature Infant Oral Motor Intervention protocol for 14 consecutive days, once a day. Results: Rehabilitation nursing in pediatric age, maintaining its roots in the theories of self-care and transitions that support rehabilitation care, incorporates models of family centered care and atraumatic care, being aware of the developmental specificities. Respiratory therapy contributes to health gains in children with respiratory disease, and it is essential to adapt the techniques to the anatomical, physiological, and behavioral particularities. In the context of a neonatology unit, neuroprotective and developmental models are fundamental to frame all care, including rehabilitation nursing care, which enhances the development, from an early stage, acting on respiratory, motor, feeding, sensory functions and parental education. Rehabilitation care plans include techniques that promote oral skills - including oral motor stimulation - under a neuroprotective and developmental approach, oral skills assessment instruments and parental education. In the study developed, it was found that the selected oral motor intervention technique contributed significantly to the maturation of oral skills, but not to the reduction of the transition time from gavage to autonomous oral intake, nor to the time of hospitalization. However, the PIOMI group tended to start oral feeding in an earlier postmenstrual age. Conclusions: The application of PIOMI by EEER contributed to the maturation of oral competencies in the PTNB and did not negatively impact the weight evolution but did not contribute to reducing the number of days necessary for the acquisition of independent oral feeding, nor the time of hospitalization