Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

Nsugbe, Ejay; Obajemu, Olusayo; Samuel, Oluwarotimi Williams; Sanusi, Ibrahim

doi:10.1016/j.mlwa.2021.100066

Cited by 11 publications

(12 citation statements)

References 26 publications

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“…Amongst the non‐linear features available, the four that were utilised (sample entropy, maximum fractal length, Higuchi fractal dimension, and detrended fluctuation analysis), were selected from prior studies and application 35,45 . These features are comprised of the most important features within the feature vector.…”

Section: Resultsmentioning

confidence: 99%

“…In related work in the medical field involving EEG and stochastic time‐series signals, it has been seen that concatenation of features from various groups (i.e., linear, non‐linear, etc. ), alongside the training of effective machine learning classifiers, can yield an enhancement in the recognition of a class label 23,34,35 . Thus, in this paper a unique ensemble of signal processing features is applied towards a potentially enhanced recognition of adolescent schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

“…), alongside the training of effective machine learning classifiers, can yield an enhancement in the recognition of a class label. 23,34,35 Thus, in this paper a unique ensemble of signal processing features is applied towards a potentially enhanced recognition of adolescent schizophrenia. Specifically speaking, the contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Nsugbe¹

2023

Applied AI Letters

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Schizophrenia is a psychiatric disorder which is prevalent in individuals around the world, where diagnosis methods for this disorder are done via a combination of interview style questioning of the patient alongside a review of their medical record; but these methods have been largely criticised for being subjective between psychiatrists and largely unreplicable. Schizophrenia also occurs in adolescent individuals who have been said to be even more challenging to diagnose largely due to delusions being mistaken for childhood fantasies, and established methods for adult patients being applied to diagnose adolescents. This work investigates the use of electroencephalography (EEG) signals acquired from adolescent patients in the age range of 10-14 years, alongside signal processing methods and machine learning modelling towards the diagnosis of adolescent schizophrenia. The results from the machine learning modelling showed that the linear discriminant analysis (LDA) and fine K-nearest neighbour (KNN) produced the best recognition results for models with easy and hard interpretability, respectively. Additionally, a computational method was applied towards contrasting the neuroanatomical activation patterns in the brain of the schizophrenic and normal adolescents, where it was seen that the neural activation patterns of the normal adolescents showed a greater consistency when compared with the schizophrenics.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Nsugbe¹

2023

Applied AI Letters

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“…These comprised a concatenation of linear, frequency and non-linear features, which have shown capability to model stochastic physiological signals, as per prior studies [17,32]. The list of features is as follows:…”

Section: • Handcrafted Featuresmentioning

confidence: 99%

Multiscale depth of anaesthesia prediction for surgery using frontal cortex electroencephalography

Nsugbe

Connelly

2022

Healthcare Tech Letters

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Hypnotic and sedative anaesthetic agents are employed during multiple medical interventions to prevent patient awareness. Careful titration of agent dosing is required to avoid negative side effects; the accuracy thereof may be improved by Depth of Anaesthesia Monitoring. This work investigates the potential of a patient specific depth monitoring prediction using electroencephalography recorded neural oscillation from the frontal lobe of 10 patients during sedation, where a comparison of the prediction accuracy was made across five different approaches to post‐processing; Noise Assisted‐Empirical Mode Decomposition, the Raw Signal, Linear Series Decomposition Learner, Deep Wavelet Scattering and Deep Learning features. These methods towards anaesthesia depth prediction were investigated using the Bispectral Index as ground truth, where it was seen that the Raw Signal, enhanced feature set and a low complexity classification model (Linear Discriminant Analysis) provided the best classification accuracy, in the region of 85.65 % ±10.23 % across the 10 subjects. Subsequent work in this area would now build on these results and validate the best performing methods on a wider cohort of patients, investigate means of continuous DoA estimation using regressions, and also feature optimisation exercises in order to further streamline and reduce the computation complexity of the designed model.

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“…This constrains their overall effectiveness, especially in the case where there exists an inhomogeneous spread of the data clusters, as seen in this work. In the case of the supervised learning methods, aside from receiving labels to pair the data with, these classifiers have a lot of complexity and are able to perform advanced coordinate transformation on the data in a higher dimensional space to promote greater cluster separation, such as the kernel trick in support vector machines (SVM), and the hidden layers within a deep neural network (Nsugbe, Obajemu, et al, 2021;Nsugbe, Phillips, et al, 2020). A characteristic table of the various clustering methods used as part of this study can be seen in Table 3: From the results obtained and the information presented in Table 2, it can be assumed that the strength of the SC algorithm is its ability to not have a specified cluster shape assumption, which in turn allows it to best cluster inhomogeneous data that is nonlinear and carries cluster overlap.…”

Section: -Cluster Validity Indexmentioning

confidence: 99%

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

Nsugbe

2022

AIA

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Male fertility has been seen to be declining, prompting for more effective and accessible means of its assessment. AI methods have been effective towards predicting semen quality through a questionnaire-based information source comprising a selection of factors from the medical literature which have been seen to influence semen quality. Prior work has seen the application of supervised learning towards the prediction of semen quality, but since supervised learning hinges on the provision of data class labels it can be said to depend on an external intelligence intervention, which can translate towards further costs and resources in practical settings. In contrast, unsupervised learning methods partition data into clusters and groups based on an objective function and do not rely on the provision of class labels, and can allow for a fully automated flow of a prediction platform. In this paper we apply three unsupervised learning models with different model architectures, namely: Gaussian mixture model (GMM), K-means and spectral clustering (SC), alongside low dimensional embedding methods which include sparse autoencoder (SAE), principal component analysis (PCA) and robust principal component analysis (RPCA). The best results were obtained with a combination of the sparse autoencoder and the SC algorithm, which was likely due to its nonspecific and arbitrary cluster shape assumption. Further work would now involve the exploration of similar unsupervised learning algorithms with a similar framework to the SC to investigate the extent to which various clusters can be learned with maximal accuracy.

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Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

Cited by 11 publications

References 26 publications

Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Multiscale depth of anaesthesia prediction for surgery using frontal cortex electroencephalography

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

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