Machine Learning Techniques for Neonatal Apnea Prediction

Shirwaikar, Rudresh Deepak; Acharya, Dinesh U; Makkithaya, Krishnamoorthi; Mallayasamy, Surulivelrajan; Lewis, Leslie Edward

doi:10.3923/jai.2016.33.38

Cited by 15 publications

(16 citation statements)

References 3 publications

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“…The cumulative density metric used in the study is a simplistic approach to characterize high densities of normalized M(t) and V(t) pairs as a function of distance from the overall cluster. We can explore other characterizations of point process patterns, like computing spike-time distances and evaluating the temporal changes in the point process estimates [45,46], and use other frameworks like machine learning [13,19, 20] for predicting cardiorespiratory events.…”

Section: Discussionmentioning

confidence: 99%

“…To aid clinicians and medical staff, therapeutic interventions, for example as presented in [16, 17], might be most effective if intervention is initiated early in high risk infants. In particular, implementation of algorithms for detection of apnea-bradycardia [18] and their limited success in prediction [13, 19, 20] might help risk-stratify infants for long-term outcomes, alert clinicians for short-term intervention, and ultimately provide automated therapeutic care that reduce the hypoxic-ischemic complications of preterm cardio-respiratory control.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate

Gee

Barbieri²,

Paydarfar

et al. 2017

IEEE Trans. Biomed. Eng.

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Objective Episodes of bradycardia are common and recur sporadically in preterm infants, posing a threat to the developing brain and other vital organs. We hypothesize that bradycardias are a result of transient temporal destabilization of the cardiac autonomic control system and that fluctuations in the heart rate signal might contain information that precedes bradycardia. We investigate infant heart rate fluctuations with a novel application of point process theory. Methods In 10 preterm infants, we estimate instantaneous linear measures of the heart rate signal in neonates, use these measures to extract statistical features of bradycardia, and propose a simplistic framework for prediction of bradycardia. Results We present the performance of a prediction algorithm using instantaneous linear measures (mean AUC = 0.79±0.018) for over 440 bradycardia events. The algorithm achieves an average forecast time of 116 seconds prior to bradycardia onset (FPR = 0.15). Our analysis reveals that increased variance in the heart rate signal is a precursor of severe bradycardia. This increase in variance is associated with an increase in power from low content dynamics in the LF band (0.04–0.2 Hz) and lower multiscale entropy values prior to bradycardia. Conclusion Point process analysis of the heartbeat time series reveals instantaneous measures that can be used to predict infant bradycardia prior to onset. Significance Our findings are relevant to risk stratification, predictive monitoring, and implementation of preventative strategies for reducing morbidity and mortality associated with bradycardia in neonatal intensive care units.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate

Gee

Barbieri²,

Paydarfar

et al. 2017

IEEE Trans. Biomed. Eng.

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“…, for t > 0, (7) and s c (0) = 0. The final memory cell output, c (t), is then computed by multiplying (ie, gating) it by the output gate activation, out (t):…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…There have also been several efforts to use machine learning for improving neonatal care . Shirwaikar et al used machine learning classification methods, such as support vector machines, decision trees, and random forest to predict apnea episodes in 229 neonates during their first week of life. In this study, they were able to achieve an accuracy of 88% to detect the presence of apnea using 22 input features, including heart rate at different times, gestation age, birth mode, birth weight, and others.…”

Section: Introductionmentioning

confidence: 99%

Predicting responses to mechanical ventilation for preterm infants with acute respiratory illness using artificial neural networks

Brigham

Gupta

Brigham

2018

Numer Methods Biomed Eng

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Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health.

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“…With a large, complex, and variable input dataset, and without any known algorithms to predict apnoeic events in preterm infants, machine learning systems have been investigated for their potential to develop a predictive model for apnoeic events. Different machine learning systems have been previously tested to predict apnoeic events in both adults and preterm infants with varying success ( 11 – 16 ).…”

Section: Prediction Of Apnoea—the Challengementioning

confidence: 99%

Predicting Apnoeic Events in Preterm Infants

Jiang

Marshall

et al. 2020

Front. Pediatr.

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Apnoea, a pause in respiration, is almost ubiquitous in preterm infants born before completing 30 weeks gestation. Apnoea often begets hypoxemia and/or bradycardia, and has the potential to result in adverse neurodevelopmental consequences. Our current inability to predict apnoeic events in preterm infants requires apnoea to first be detected by monitoring device/s in order to trigger an intervention by bedside (medical or nursing) staff. Such a reactive management approach is laborious, and makes the consequences of apnoeic events inevitable. Recent technological advances and improved signal processing have allowed the possibility of developing prediction models for apnoeic events in preterm infants. However, the development of such models has numerous challenges and is only starting to show potential. This paper identifies requisite components and current gaps in developing prediction models for apnoeic events, and reviews previous studies on predicting apnoeic events in preterm infants.

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Machine Learning Techniques for Neonatal Apnea Prediction

Cited by 15 publications

References 3 publications

Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate

Predicting Bradycardia in Preterm Infants Using Point Process Analysis of Heart Rate

Predicting responses to mechanical ventilation for preterm infants with acute respiratory illness using artificial neural networks

Predicting Apnoeic Events in Preterm Infants

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