Quiet sleep detection in preterm infants using deep convolutional neural networks

Ansari, Amir H.; Wel, Ofelie De; Lavanga, Mario; Caicedo, Alexander; Dereymaeker, A; Jansen, Katrien; Vervisch, Jan; Vos, Maarten De; Naulaers, Gunnar; Huffel, Sabine Van

doi:10.1088/1741-2552/aadc1f

Cited by 52 publications

(62 citation statements)

References 44 publications

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“…follow-up, 1 provided the most reliable estimate of a normal developing EEG and has been used extensively in previous studies on preterm brain maturation and sleep staging 23,33,[35][36][37][38] . Consequently, PMA was considered a reasonable estimate of EEG brain-age in this dataset 30 and made it ideal as the training set for the brain-age prediction model.…”

Section: Brain-age Prediction and Deriving Trajectories With A 24-momentioning

confidence: 99%

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Pillay

Dereymaeker

Jansen

et al. 2020

Sci Rep

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Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the electroencephalography (eeG)-derived 'brain-age' and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby's stay in the neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9-24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δ max). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δ max showed significant differences between outcome groups (cluster-based permutation test, p < 0.05). RMSE had a median (IQR) of 0.75 (0.60-1.35) weeks for normal outcome and 1.35 (1.15-1.55) for abnormal outcome, while δ max had a median of 0.90 (0.70-1.70) and 1.90 (1.20-2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.

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Section: Brain-age Prediction and Deriving Trajectories With A 24-momentioning

confidence: 99%

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Pillay

Dereymaeker

Jansen

et al. 2020

Sci Rep

Self Cite

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“…Recent research has also shown promising results for CNN-based EEG classification. In seizure detection (Acharya et al, 2018a;Ansari et al, 2018a), depression detection (Liu et al, 2017) and sleep stage classification (Acharya et al, 2018b;Ansari et al, 2018b), CNN have shown promising classification capabilities for EEG data. A CNN for EEG-based speech stimulus reconstruction was presented recently (de Taillez et al, 2017), showing that deep learning is a feasible alternative to linear decoding methods.…”

Section: Introductionmentioning

confidence: 99%

EEG-based detection of the locus of auditory attention with convolutional neural networks

Vandecappelle

Deckers

Das

et al. 2018

Preprint

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When multiple people talk simultaneously, the healthy human auditory system is able to attend to one particular speaker of interest. Recently, it has been demonstrated that it is possible to infer to which speaker someone is attending by relating the neural activity, recorded by electroencephalography (EEG), with the speech signals. This is relevant for an effective noise suppression in hearing devices, in order to detect the target speaker in a multi-speaker scenario. Most auditory attention detection algorithms use a linear EEG decoder to reconstruct the attended stimulus envelope, which is then compared to the original stimuli envelopes to determine the attended speaker. Classifying attention within a short time interval remains the main challenge. We present two different convolutional neural network (CNN)-based approaches to solve this problem. One aims to select the attended speaker from a given set of individual speaker envelopes, and the other extracts the locus of auditory attention (left or right), without knowledge of the speech envelopes. Our results show that it is possible to decode attention within 1-2 seconds, with a median accuracy around 80%, without access to the speech envelopes. This is promising for neuro-steered noise suppression in hearing aids, which requires fast and accurate attention detection. Furthermore, the possibility of detecting the locus of auditory attention without access to the speech envelopes is promising for the scenarios in which per-speaker envelopes are unavailable. It will also enable establishing a fast and objective attention measure in future studies. Index TermsConvolutional neural networks (CNN), auditory attention detection (AAD), electroencephalography (EEG), neurosteered auditory prosthesis, brain-computer interface (BCI)

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“…The performance of the described algorithm is lower compared to state-of-the-art algorithms [13,14,34]. However, there are multiple advantages of the proposed method.…”

Section: Discussionmentioning

confidence: 91%

“…The majority of these approaches are supervised and combine a set of electroencephalography (EEG) features (e.g., temporal features, spectral features, spatial features, complexity features) with a classification algorithm [11,12]. Recently, deep learning has also found its way to sleep staging in preterm infants [13]. Finally, Dereymaeker et al [14] have proposed a cluster-based algorithm for quiet sleep detection.This paper proposes a novel unsupervised method to discriminate quiet sleep from non-quiet sleep in preterm infants.…”

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confidence: 99%

Decomposition of a Multiscale Entropy Tensor for Sleep Stage Identification in Preterm Infants

Wel

Lavanga

Caicedo

et al. 2019

Entropy

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Established sleep cycling is one of the main hallmarks of early brain development in preterm infants, therefore, automated classification of the sleep stages in preterm infants can be used to assess the neonate’s cerebral maturation. Tensor algebra is a powerful tool to analyze multidimensional data and has proven successful in many applications. In this paper, a novel unsupervised algorithm to identify neonatal sleep stages based on the decomposition of a multiscale entropy tensor is presented. The method relies on the difference in electroencephalography(EEG) complexity between the neonatal sleep stages and is evaluated on a dataset of 97 EEG recordings. An average sensitivity, specificity, accuracy and area under the receiver operating characteristic curve of 0.80, 0.79, 0.79 and 0.87 was obtained if the rank of the tensor decomposition is selected based on the age of the infant.

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Quiet sleep detection in preterm infants using deep convolutional neural networks

Abstract: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

Cited by 52 publications

References 44 publications

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

Applying a data-driven approach to quantify EEG maturational deviations in preterms with normal and abnormal neurodevelopmental outcomes

EEG-based detection of the locus of auditory attention with convolutional neural networks

Decomposition of a Multiscale Entropy Tensor for Sleep Stage Identification in Preterm Infants

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