Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Hussain, Abir Jaafar; Fergus, Paul; Alaskar, Haya; Al‐Jumeily, Dhiya; Jager, F.

doi:10.1016/j.neucom.2014.03.087

Cited by 56 publications

(34 citation statements)

References 49 publications

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“…108/09/09). 171 Materials and methods 172 With the aim to develop a useful and improved automatic method for predicting 173 preterm birth, we followed a general and widely accepted development process [29][30][31][32][33][34][35][36]: 174 1. select or construct a valid batabase for training and testing the model; 175 2. characterize the data and use effective mathematical expressions to formulate the 176 features that reflect their correlation with the target classes;…”

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confidence: 99%

Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the 1/50 proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is cap...

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confidence: 99%

Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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“…The training process implements many full sweeps by the training data. In this model, the classification are performed for a new objective by permitting the ensemble of perceptron's to make vote in the neural network on the label of each test point [11]. D. The Radial basis neural Network Classifiers (RBNC): RBNC is considered one of the most popular models in neural network architectures, which is often utilised in classification problem and complex pattern recognition.…”

Section: Voted Perception Classifier (Vpc)mentioning

confidence: 99%

Training Neural Networks as Experimental Models: Classifying Biomedical Datasets for Sickle Cell Disease

Khalaf

Hussain

Al‐Jumeily

et al. 2016

Intelligent Computing Theories and Application

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Abstract. This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician's experience that can lead to time consuming and stress to patents. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the ROC curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524.

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“…The dataset used for this paper is the same as that used in [5], [9], [12], [13], with four features (root mean square, median frequency, peak frequency and sample entropy). The raw uterine EHG signal has been extracted from Physionet [7] using the Waveform Database (WFDB) toolbox.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…However for analysis and feature extraction purposes, transformation into other domains is possible. These include a frequency representation via Wavelet Transform (WT) and Fourier Transform (FT) [13].…”

Section: B Data Pre-processing/feature Extractionmentioning

confidence: 99%

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

Idowu

Fergus

Hussain

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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-Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%.

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Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Cited by 56 publications

References 49 publications

Characterization and automatic classification of preterm and term uterine records

Characterization and automatic classification of preterm and term uterine records

Training Neural Networks as Experimental Models: Classifying Biomedical Datasets for Sickle Cell Disease

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

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