Central apnea detection in premature infants using machine learning

Varisco, Gabriele; Peng, Zheng; Kommers, Deedee; Zhan, Zhuozhao; Cottaar, Ward; Andriessen, Peter; Long, Xi; Pul, Carola van

doi:10.1016/j.cmpb.2022.107155

Cited by 10 publications

(22 citation statements)

References 43 publications

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“…In this study we used a dataset which consisted of 10 premature infants who developed late-onset sepsis (LOS) and 10 matched controls, all characterized by a gestational age (GA) 30 weeks and admitted to the NICU of Máxima Medical Center in Veldhoven, the Netherlands, from July 2016 to December 2018 (Varisco et al 2022).…”

Section: Dataset and Annotationsmentioning

confidence: 99%

“…Annotation of apneic events was performed in our previous study (Varisco et al 2022) and required two main steps: (1) a first detection of suspected apneic events, consisting of both true and false CAs (i.e. false CA alarms), by means of the optimized algorithm for the detection of apneic events and (2) manual annotations for the apneic events, which were annotated into CAs, rejections (i.e.…”

Section: Dataset and Annotationsmentioning

confidence: 99%

“…A table with all the features included in the current study is presented in appendix. Further details on the computation of these features can be found in our previous study (Varisco et al 2022).…”

Section: Signal Processing and Feature Extractionmentioning

confidence: 99%

“…Logistic regression (LR) with elastic net penalty was selected as the ML algorithm to create a detection model since it is faster than many other algorithms (an important characteristic when large datasets are considered), allows for an easier interpretation of the results compared to other ML algorithms and performed best using the two-step approach for CA detection (Varisco et al 2022). The detection model used in this study was trained using the training set extracted using the NearMiss 3 undersampling method, as discussed in section (2.3), while their performance was tested on the test set (i.e.…”

Section: Machine Learning: Detection Model and Cross-validationmentioning

confidence: 99%

“…Zuzarte et al (2021) managed instead to predict apneic events accompanied by bradycardia and hypoxia, using cardiorespiratory and movement features derived from physiological waveform data and considering set of events consisting of 7.5 min long inter-apnea frames and pre-apnea frames extracted from 10 premature infants in a model that combined an unsupervised method (Gaussian mixture models) with a supervised one (logistic regression, LR). More recently, we performed a CA detection study in which we included 20 patients and 48 h of data per patient to detect the occurrence of CAs (Varisco et al 2022). We developed a two-step approach consisting of a first detection of apneic events, performed by means of the optimized algorithm for the detection of apneic events which was previously mentioned, followed by the use of a machine-learning based detection model to determine whether these were CAs or not, and obtained significant results which include a mean area under the receiver operating characteristic curve (AUROC) equal to 0.88 with our best detection model, achieved by using LR with elastic net penalty and leave-one-patient-out cross-validation (LOPO CV).…”

Section: Introductionmentioning

confidence: 99%

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Detecting central apneas using multichannel signals in premature infants

Varisco,

Peng,

Kommers

et al. 2024

Physiol. Meas.

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Objective: Monitoring of apnea of prematurity, performed in neonatal intensive care units by detecting central apneas (CAs) in the respiratory traces, is characterized by a high number of false alarms. A two-step approach consisting of a threshold-based apneic event detection algorithm followed by a machine learning model was recently presented in literature aiming to improve CA detection. However, since this is characterized by high complexity and low precision, we developed a new direct approach that only consists of a detection model based on machine learning directly working with multichannel signals. Approach: The dataset used in this study consisted of 48 hours of ECG, chest impedance and peripheral oxygen saturation extracted from 10 premature infants. CAs were labeled by two clinical experts. 47 features were extracted from time series using 30-s moving windows with an overlap of 5 s and evaluated in sets of 4 consecutive moving windows, in a similar way to what was indicated for the two-step approach. An undersampling method was used to reduce imbalance in the training set while aiming at increasing precision. A detection model using logistic regression with elastic net penalty and leave-one-patient-out cross-validation was then tested on the full dataset.Main results: This detection model returned a mean AUROC value equal to 0.86 and, after the selection of a FPR equal to 0.1 and the use of smoothing, an increased precision (0.50 vs. 0.42) at the expense of a decrease in recall (0.70 vs. 0.78) compared to the two-step approach around suspected apneic events. Significance: The new direct approach guaranteed correct detections for more than 81% of CAs with length L ≥ 20 s, which are considered among the most threatening apneic events for premature infants. These results require additional verifications using more extensive datasets but could lead to promising applications in clinical practice.

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Section: Dataset and Annotationsmentioning

confidence: 99%

Section: Dataset and Annotationsmentioning

confidence: 99%

Section: Signal Processing and Feature Extractionmentioning

confidence: 99%

Section: Machine Learning: Detection Model and Cross-validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting central apneas using multichannel signals in premature infants

Varisco,

Peng,

Kommers

et al. 2024

Physiol. Meas.

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Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study

Kloonen,

Varisco,

de Kort

et al. 2023

Physiol. Meas.

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Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure. Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated. Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2 as most important features. Excluding GA decreased performance in both models. Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.

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