Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

Nieto-del-Amor, Félix; Beskhani, Raja; Ye-Lin, Yiyao; Garcia-Casado, Javier; Díaz-Martinez, Alba; Monfort-Ortíz, Rogelio; Almela, Vicente Diago; Hao, Dongmei; Prats-Boluda, Gema

doi:10.3390/s21186071

Cited by 19 publications

(28 citation statements)

References 60 publications

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“…The measure is relatively faster, insensitive to noise, and detects simultaneous amplitude and phase variations. Dispersion entropy (DispEn) uses a mapping function that transforms the EEG data to a new time series data of symbolic sequences with fewer elements (Azami and Escudero, 2018 ; Nieto-Del-amor et al, 2021 ). It estimates the regularity of the patterns with similar dispersion patterns.…”

Section: Methodsmentioning

confidence: 99%

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Das

Puthankattil

2022

Front. Comput. Neurosci.

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BackgroundFunctional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.MethodFunctional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.ResultsReal EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.ConclusionTaken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.

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Section: Methodsmentioning

confidence: 99%

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Das

Puthankattil

2022

Front. Comput. Neurosci.

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“…After feature selection by a genetic algorithm, the authors reported a mean F1 score of 92.04% using an ensemble classifier. Later on, Ye-Lin et al [5] showed the efficiency of entropy measures for the classification of the term-preterm EHG recordings by a linear discriminant analysis (LDA) classifier with an average F1 score of 90.1%. Yang et al [34] extracted RMS, median frequency, peak frequency, and sample entropy from the EHG signals, and applied SMOTE for overcoming the data imbalance issue.…”

Section: Related Workmentioning

confidence: 99%

“…Even the survivors are exposed to various lifelong disabilities, such as, but not limited to, learning difficulties and vision or hearing impairments. Regardless of its complications, the price of medical care for preterm babies imposes a significant financial burden on the family and society, as it costs 5-to 10-times more than a term birth [5]. Thus, early prediction of preterm delivery, combined with appropriate medication to prevent this phenomenon, can greatly minimize the corresponding complications for both the mother and the baby, and reduce the economic load on public health systems.…”

Section: Introductionmentioning

confidence: 99%

“…SampEn represents the irregularity of the signal. As stronger contractions can also increase irregularity, it can be expected as a proper feature for the term and preterm segmentation [5,18]. SampEn requires two parameters to be adjusted before implementation, embedding dimension m and scaling factor r. In this paper, we used m = 3 and r = 0.15, as suggested in [5].…”

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confidence: 99%

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Prediction of Preterm Delivery from Unbalanced EHG Database

Far

Beiramvand

Shahbakhti

et al. 2022

Sensors

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Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.

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“…The EHG signal spectral content also shifts towards higher frequencies as delivery approaches, suggesting increased cell excitability [ 9 , 12 ]. Previous studies found increased signal regularity, thus reduced complexity, by analyzing Lempel–Ziv and different entropy measures [ 10 , 13 , 14 , 15 , 16 , 17 ], although controversial results were obtained due to the limited database with different compositions depending on the inclusion criteria and the analysis bandwidth, among others. Time reversibility and Poincaré plot-derived parameters were also used for characterizing the EHG signal [ 13 , 14 , 18 ], with an increased signal non-linearity degree and less randomness as pregnancy progresses.…”

Section: Introductionmentioning

confidence: 99%

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Nieto-del-Amor

Prats-Boluda

Garcia-Casado

et al. 2022

Sensors

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Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

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Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

Cited by 19 publications

References 60 publications

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Prediction of Preterm Delivery from Unbalanced EHG Database

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

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