Advanced artificial neural network classification for detecting preterm births using EHG records

Fergus, Paul; Idowu, Ibrahim Olatunji; Hussain, Abir Jaafar; Dobbins, Chelsea

doi:10.1016/j.neucom.2015.01.107

Cited by 77 publications

(91 citation statements)

References 23 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%

“…A low value of 325 the SE suggests the presence of a physiologic mechanism with periodic behavior, while a 326 high value suggests the absence of a mechanism. The SE and MF have been 327 successfully used to classify individual pregnancy and labor contractions [19][20][21]44], and 328 to classify entire preterm and term EHG records [27,[29][30][31][32][33], which are actually 329 sequences of contraction and non-contraction (dummy) intervals. The MF and PA are 330 suitable features for assessing shifts and intensity of the frequency content in any 331 biological signal and in separate frequency bands.…”

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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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Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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“…The growth of medical information has played a significant role in healthcare organisations [27]. The target of these improvements is to develop the usage of technology in medical applications [28].…”

Section: Classificationmentioning

confidence: 99%

“…The technique can therefore be identified as a meta-learning approach to problem solving. The Random Forest model is widely used in the medical domain for the development of classifiers [27]. The notion of random forest was first introduced by Tin Kam Ho in [29,30] and subsequently developed into the popular form known today by Brieman in [31].…”

Section: Random Forest and Support Vector Machinesmentioning

confidence: 99%

Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models

et al. 2017

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Machine Learning approaches to the application of Disease Modifying Therapy for Sickle Cell using Classification Models http://researchonline.ljmu.ac.uk/id/eprint/4141/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively.Abstract-This paper discusses the use of machine learning techniques for the classification of medical data, specifically for guiding disease modifying therapies for Sickle Cell. Extensive research has indicated that machine learning approaches 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. The aim of this paper is to present findings for several classes of learning algorithm for medically related problems. The initial case study addressed in this paper involves classifying the dosage of medication required for the treatment of patients with Sickle Cell Disease. We use different machine learning architectures in order to investigate the accuracy and performance within the case study. The main purpose of applying classification approach is to enable healthcare organisations to provide accurate amount of medication. The results obtained from a range of models during our experiments have shown that of the proposed models, recurrent networks produced inferior results in comparison to conventional feedforward neural networks and the Random Forest model. Results have also indicated that for the recurrent network models tested, the Jordan architecture was found to yield significantly better outcomes over the range of performance measures considered. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall.

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“…Various artificial intelligence methods, such as the artificial neural network (ANN) [1], support vector machine (SVM) [2], decision tree [3], extreme learning machine [4], the linear regression classifier [5] and other classifiers, have been proposed for classification problems. Among these methods, ANN is very popular due to the features of self-learning, self-adaptive and high generalization capability.…”

Section: Introductionmentioning

confidence: 99%

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

2017

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Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn-Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

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Advanced artificial neural network classification for detecting preterm births using EHG records

Cited by 77 publications

References 23 publications

Characterization and automatic classification of preterm and term uterine records

Characterization and automatic classification of preterm and term uterine records

Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

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