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

Wel, Ofelie De; Lavanga, Mario; Caicedo, Alexander; Jansen, Katrien; Naulaers, Gunnar; Huffel, Sabine Van

doi:10.3390/e21100936

Cited by 9 publications

(9 citation statements)

References 37 publications

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“…The remaining five studies were carried out by the same research group. They used eight-channel EEG signals to detect QS from non-QS states, in which least squares SVM (LS-SVM) [ 19 ], cluster-based adaptive sleep staging (CLASS) [ 20 ], deep CNN [ 21 ], end-to-end CNN [ 22 ], and multiscale deep CNN [ 23 ] techniques were adopted, respectively.…”

Section: Methodsmentioning

confidence: 99%

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

Sentner

Wang

Groot

et al. 2022

Sleep

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Study Objectives Sleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep-wake states of preterm infants in real-time at the bedside. Methods In this study, sleep-wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep-wake states. Independent training (n = 37) and validation datasets were used (n = 9). Finally, a setup was designed for real-time implementation at the bedside. Results The macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77). Conclusions With this study, to the best of our knowledge, a reliable, non-obtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep-wake states, potentially promoting the early brain development and well-being of preterm infants.

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Section: Methodsmentioning

confidence: 99%

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

Sentner

Wang

Groot

et al. 2022

Sleep

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“…All recordings were anonymized by the clinicians before analysis and further details about the demographics are given in [1], [27]. This database was also previously used in [6], [9], [13], [23], [24]. Additional technical and medical details about this database are provided in [1].…”

Section: A Databasementioning

confidence: 99%

“…1). Multiscale Entropy Tensor Decomposition (METD): an unsupervised QS detection algorithm proposed in [13]. In this method, first the multichannel EEG is tensorized via multiscale entropy and is then factorized by canonical polyadic decomposition (CPD) in a sum of multiple rank-1 tensors.…”

Section: G Benchmark Algorithms and The State-of-the-artmentioning

confidence: 99%

“…Subsequently, more refined feature extraction and classifiers have been applied, such as the Support Vector Machine (SVM) with spectral, temporal and spatial features [8], the Least-Squares SVM (LS-SVM) with multi-fractal features [9], a Multilayer Perceptron (MLP) using entropy features [10], and other extensions that utilised multi-scale entropy [11], [12] . De Wel et al also suggested a tensor decomposition approach for unsupervised classification [13], while Pillay et al incorporated a large number of data-driven features in a combined Hidden Markov Model-Gaussian Mixture Model (HMM-GMM) which has been extended to additionally subclassify the 4 stages of sleep (AS I, AS II, QS I and QS II) that differentiate by term age (≥37 weeks PMA) [14].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels

Ansari

Pillay

Dereymaeker

et al. 2022

IEEE J. Biomed. Health Inform.

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In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of EEG channels are recorded. Existing automated algorithms mainly use multichannel EEGs which perform poorly when fewer numbers of channels are available. The proposed Sinc utilizes multi-scale analysis to place emphasis on the temporal EEG information to be less dependent on the number of EEG channels. In Sinc, we increase the receptive fields through Inception while by additionally sharing the filters that have similar receptive fields, overfitting is controlled and the number of trainable parameters dramatically reduced. To train and test this model, 96 longitudinal EEG recordings from 26 premature infants are used. The Sincbased model significantly outperforms state-of-the-art neonatal quiet sleep detection algorithms, with mean Kappa 0.77 ± 0.01 (with 8-channel EEG) and 0.75 ± 0.01 (with a single bipolar channel EEG). This is the first study using Inception-based networks for EEG analysis that utilizes filter sharing to improve efficiency and trainability. The suggested network can successfully detect quiet sleep stages with even a single EEG channel making it more practical especially in the hospital setting where cerebral function monitoring is predominantly used.

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“…De Wel et al proposed a novel unsupervised method to discriminate quiet sleep from non-quiet sleep in preterm infants, from the decomposition of a multiscale entropy tensor [ 16 ]. This was performed according to the difference in the electroencephalography (EEG) complexity between the neonatal sleep stages.…”

Section: Applications Of Existing Entropy-based Measuresmentioning

confidence: 99%

Multiscale Entropy Approaches and Their Applications

Humeau-Heurtier

2020

Entropy

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

Cited by 9 publications

References 37 publications

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels

Multiscale Entropy Approaches and Their Applications

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