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Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. SENSORS 2021; 21:s21103350. [PMID: 34065847 PMCID: PMC8151582 DOI: 10.3390/s21103350] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/23/2021] [Accepted: 05/07/2021] [Indexed: 11/17/2022]
Abstract
Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.
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Vardhini P, Punitha N, Ramakrishnan S. Differentiation of fluctuations in uterine contractions associated with Term pregnancies using adaptive fractal features of electromyography signals. Med Eng Phys 2021; 88:78-85. [PMID: 33485517 DOI: 10.1016/j.medengphy.2020.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 10/23/2020] [Accepted: 12/17/2020] [Indexed: 11/16/2022]
Abstract
Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.
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Affiliation(s)
- P Vardhini
- Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, India.
| | - N Punitha
- Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, India
| | - S Ramakrishnan
- Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, 600036, India
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Song X, Qiao X, Hao D, Yang L, Zhou X, Xu Y, Zheng D. Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate. Sci Rep 2021; 11:1956. [PMID: 33479344 PMCID: PMC7820321 DOI: 10.1038/s41598-021-81492-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/14/2020] [Indexed: 11/09/2022] Open
Abstract
Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.
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Affiliation(s)
- Xiaoxiao Song
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Xiangyun Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Dongmei Hao
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China.
| | - Lin Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, 100124, China
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yuhang Xu
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
| | - Dingchang Zheng
- Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Priory Street, Coventry, CV1 5FB, UK
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Diaz-Martinez A, Mas-Cabo J, Prats-Boluda G, Garcia-Casado J, Cardona-Urrego K, Monfort-Ortiz R, Lopez-Corral A, De Arriba-Garcia M, Perales A, Ye-Lin Y. A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity. SENSORS 2020; 20:s20113023. [PMID: 32466584 PMCID: PMC7308960 DOI: 10.3390/s20113023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/04/2020] [Accepted: 05/23/2020] [Indexed: 11/16/2022]
Abstract
Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.
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Affiliation(s)
- Alba Diaz-Martinez
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Javier Mas-Cabo
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Karen Cardona-Urrego
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
| | - Rogelio Monfort-Ortiz
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Angel Lopez-Corral
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Maria De Arriba-Garcia
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Alfredo Perales
- Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain; (R.M.-O.); (A.L.-C.); (M.D.A.-G.); (A.P.)
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain; (A.D.-M.); (J.M.-C.); (G.P.-B.); (J.G.-C.); (K.C.-U.)
- Correspondence: ; Tel.: +34-96-387-70-00 (ext. 76026)
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Punitha N, Ramakrishnan S. Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions. Proc Inst Mech Eng H 2019; 233:362-371. [PMID: 30706756 DOI: 10.1177/0954411919827323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.
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Affiliation(s)
- N Punitha
- Non-Invasive Imaging and Diagnostic (NIID) Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - S Ramakrishnan
- Non-Invasive Imaging and Diagnostic (NIID) Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
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Namadurai P, Padmanabhan V, Swaminathan R. Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions. IEEE J Biomed Health Inform 2018; 23:1972-1979. [PMID: 30369459 DOI: 10.1109/jbhi.2018.2878059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVES The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm. METHODS The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents. RESULTS The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability. CONCLUSION It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.
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Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Med Eng Phys 2018; 56:27-35. [DOI: 10.1016/j.medengphy.2018.04.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/21/2018] [Accepted: 04/10/2018] [Indexed: 11/19/2022]
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Garcia-Casado J, Ye-Lin Y, Prats-Boluda G, Mas-Cabo J, Alberola-Rubio J, Perales A. Electrohysterography in the diagnosis of preterm birth: a review. Physiol Meas 2018; 39:02TR01. [PMID: 29406317 DOI: 10.1088/1361-6579/aaad56] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. OBJECTIVE A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. APPROACH This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. MAIN RESULTS Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. SIGNIFICANCE This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.
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Affiliation(s)
- J Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería (CI2B), Universitat Politècnica de València (UPV), Camino de Vera SN, 46022, Valencia, Spain
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Alberola-Rubio J, Garcia-Casado J, Prats-Boluda G, Ye-Lin Y, Desantes D, Valero J, Perales A. Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 144:127-133. [PMID: 28494996 DOI: 10.1016/j.cmpb.2017.03.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 01/31/2017] [Accepted: 03/21/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG). METHODS EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (≤41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared. RESULTS The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC= 0.65) and GA at recording time (AUC= 0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC= 0.76. Multiple input SVM obtained AUC= 0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC= 0.93. CONCLUSIONS Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique.
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Affiliation(s)
- Jose Alberola-Rubio
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain; Servicio de Obstetricia y Ginecología, Área de la Salud de la Mujer, Hospital Universitario y Politécnico La Fe de Valencia, Bulevar Sur SN, 46033, Valencia, Spain.
| | - Javier Garcia-Casado
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain.
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain
| | - Yiyao Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.8B, 46022 Valencia, Spain
| | - Domingo Desantes
- Servicio de Obstetricia y Ginecología, Área de la Salud de la Mujer, Hospital Universitario y Politécnico La Fe de Valencia, Bulevar Sur SN, 46033, Valencia, Spain
| | - Javier Valero
- Servicio de Obstetricia y Ginecología, Área de la Salud de la Mujer, Hospital Universitario y Politécnico La Fe de Valencia, Bulevar Sur SN, 46033, Valencia, Spain
| | - Alfredo Perales
- Servicio de Obstetricia y Ginecología, Área de la Salud de la Mujer, Hospital Universitario y Politécnico La Fe de Valencia, Bulevar Sur SN, 46033, Valencia, Spain
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Fergus P, Hussain A, Hignett D, Al-Jumeily D, Abdel-Aziz K, Hamdan H. A machine learning system for automated whole-brain seizure detection. APPLIED COMPUTING AND INFORMATICS 2016. [DOI: 10.1016/j.aci.2015.01.001] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Hussain A, Fergus P, Al-Askar H, Al-Jumeily D, Jager F. Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.03.087] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. BIOMED RESEARCH INTERNATIONAL 2015; 2015:986736. [PMID: 25710040 PMCID: PMC4325968 DOI: 10.1155/2015/986736] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 12/09/2014] [Accepted: 12/23/2014] [Indexed: 11/17/2022]
Abstract
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.
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Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S. Prediction of preterm deliveries from EHG signals using machine learning. PLoS One 2013; 8:e77154. [PMID: 24204760 PMCID: PMC3810473 DOI: 10.1371/journal.pone.0077154] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Accepted: 08/30/2013] [Indexed: 12/16/2022] Open
Abstract
There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier.
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Affiliation(s)
- Paul Fergus
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
| | - Pauline Cheung
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
| | - Abir Hussain
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
| | - Dhiya Al-Jumeily
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
| | - Chelsea Dobbins
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
| | - Shamaila Iram
- Applied Computing Research Group, Liverpool John Moores University, Liverpool, Merseyside, United Kingdom
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Garcia-Gonzalez MT, Charleston-Villalobos S, Vargas-Garcia C, Gonzalez-Camarena R, Aljama-Corrales T. Characterization of EHG contractions at term labor by nonlinear analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:7432-5. [PMID: 24111463 DOI: 10.1109/embc.2013.6611276] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Uterine electromyogram on the abdomen of pregnant women (electrohysterogram, EHG) plays an interesting role to evaluate possible risks to the binomial mother-fetus. In this sense, the present study explored the characterization of contractions by EHG during active phase of labor at term in a population at low risk. The goal was to investigate the differences in the contractions generated by women that evolve labor to a vaginal delivery (group 1) to those associated with caesarean section (group 2). Abdominal signals were acquired using Ag-AgCl electrodes in a bipolar configuration and the EHG was obtained by band-pass filtering in the range of 0.3 to 4 Hz. Sample entropy (SampEn) was used to calculate the irregularity of manually selected contractions of the EHG time series. The results showed that it is plausible to discriminate contractions from both groups as the average SampEn was 2.1359 with a standard deviation of 0.0583 for group 1 (N=8), while for group 2 (N=8) was 2.0352 with standard deviation of 0.0946; it was found significant statistical difference between groups as p was 0.046. Consequently, the nonlinear analysis via SampEn of EHG could provide an index to evaluate the quality of the active phase labor at term.
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Ye-Lin Y, Prats-Boluda G, Alberola-Rubio J, Bueno Barrachina JM, Perales A, Garcia-Casado J. Prediction of labor using non-invasive Laplacian EHG recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:7428-7431. [PMID: 24111462 DOI: 10.1109/embc.2013.6611275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Non-invasive electrohysterogram (EHG) recordings could be used as an alternative technique for monitoring uterine dynamics. Bipolar recordings of EHG have proven to provide valuable information to predict labor. Recently it has been stated that uterine EHG bursts could also be identified in Laplacian recordings on abdominal surface. Taking into account that Laplacian potential technique permits to acquire more localized electrical activity than conventional recordings; these recordings could also be helpful for deducing uterine contraction efficiency. The aim of this paper is to examine the feasibility of Laplacian potential EHG recording for labor prediction and to compare it with monopolar recordings. To this purpose, a total of 42 EHG recordings were acquired from women of similar gestational age: 29 antepartum patients, and 13 patients in labor. Then linear and non-linear classifiers have been implemented using EHG burst parameters as input features. Experimental results show significant differences in temporal and spectral parameters in both monopolar and Laplacian potential recordings between the two groups. In addition, support vector machine based classifier achieved an accuracy of 93% for labor prediction for monopolar recordings, 92% for bipolar recordings and 91% for Laplacian potential.
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Furdea A, Preissl H, Lowery CL, Eswaran H, Govindan RB. Conduction velocity of the uterine contraction in serial magnetomyogram (MMG) data: event based simulation and validation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6025-8. [PMID: 22255713 DOI: 10.1109/iembs.2011.6091489] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in more than one quadrant, we calculate the center of gravity (CoG) in each quadrant for each time point as the sum of the product of the sensor coordinates with the Hilbert amplitude of the MMG signals normalized by the sum of the Hilbert amplitude of the signals over all sensors. Following this we compute the delay between the CoGs of all (six) possible quadrant pairs combinations. As a first step, we validate this approach by simulating a stochastic model based on independent second-order autoregressive processes (AR2) and we divide them into 30 second disjoint windows and insert burst activity at specific time instances in preselected sensors. Also we introduce a lag of 5 ± 1 seconds between different quadrants. Using our approach we calculate the CoG of the signals in a quadrant. To this end, we compute the delay between CoGs obtained from different quadrants and show that our approach is able to reliably capture the delay incorporated in the model. We apply the proposed approach to 19 serial MMG data obtained from two subjects and show an increase in the CV as the subjects approached labor.
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Affiliation(s)
- Adrian Furdea
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen 72074, Germany.
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Gustafson KM, Allen JJB, Yeh HW, May LE. Characterization of the fetal diaphragmatic magnetomyogram and the effect of breathing movements on cardiac metrics of rate and variability. Early Hum Dev 2011; 87:467-75. [PMID: 21497027 PMCID: PMC3114157 DOI: 10.1016/j.earlhumdev.2011.03.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2010] [Revised: 03/04/2011] [Accepted: 03/25/2011] [Indexed: 11/17/2022]
Abstract
Breathing movements are one of the earliest fetal motor behaviors to emerge and are a hallmark of fetal well-being. Fetal respiratory sinus arrhythmia (RSA) has been documented but efforts to quantify the influence of breathing on heart rate (HR) and heart rate variability (HRV) are difficult due to the episodic nature of fetal breathing activity. We used a dedicated fetal biomagnetometer to acquire the magnetocardiogram (MCG) between 36 and 38 weeks gestational age (GA). We identified and characterized a waveform observed in the raw data and independent component decomposition that we attribute to fetal diaphragmatic movements during breathing episodes. RSA and increased high frequency power in a time-frequency analysis of the IBI time-series was observed during fetal breathing periods. Using the diaphragmatic magnetomyogram (dMMG) as a marker, we compared time and frequency domain metrics of heart rate and heart rate variability between breathing and non-breathing epochs. Fetal breathing activity resulted in significantly lower HR, increased high frequency power, greater sympathovagal balance, increased short-term HRV and greater parasympathetic input relative to non-breathing episodes confirming the specificity of fetal breathing movements on parasympathetic cardiac influence. No significant differences between breathing and non-breathing epochs were found in two metrics reflecting total HRV or very low, low and intermediate frequency bands. Using the fetal dMMG as a marker, biomagnetometry can help to elucidate the electrophysiologic mechanisms associated with diaphragmatic motor function and may be used to study the longitudinal development of human fetal cardiac autonomic control and breathing activity.
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Affiliation(s)
- Kathleen M Gustafson
- University of Kansas Medical Center, Department of Neurology, Kansas City, KS, USA.
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Lucovnik M, Kuon RJ, Chambliss LR, Maner WL, Shi SQ, Shi L, Balducci J, Garfield RE. Use of uterine electromyography to diagnose term and preterm labor. Acta Obstet Gynecol Scand 2010; 90:150-7. [PMID: 21241260 DOI: 10.1111/j.1600-0412.2010.01031.x] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Current methodologies to assess the process of labor, such as tocodynamometry or intrauterine pressure catheters, fetal fibronectin, cervical length measurement and digital cervical examination, have several major drawbacks. They only measure the onset of labor indirectly and do not detect cellular changes characteristic of true labor. Consequently, their predictive values for term or preterm delivery are poor. Uterine contractions are a result of the electrical activity within the myometrium. Measurement of uterine electromyography (EMG) has been shown to detect contractions as accurately as the currently used methods. In addition, changes in cell excitability and coupling required for effective contractions that lead to delivery are reflected in changes of several EMG parameters. Use of uterine EMG can help to identify patients in true labor better than any other method presently employed in the clinic.
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Affiliation(s)
- Miha Lucovnik
- Department of Obstetrics and Gynecology, St Joseph's Hospital and Medical Center, Phoenix, AZ 85004, USA
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Jacod BC, Graatsma EM, Van Hagen E, Visser GHA. A validation of electrohysterography for uterine activity monitoring during labour. J Matern Fetal Neonatal Med 2009; 23:17-22. [DOI: 10.3109/14767050903156668] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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21
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Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstet Gynecol Surv 2009; 64:529-41. [DOI: 10.1097/ogx.0b013e3181a8c6b1] [Citation(s) in RCA: 72] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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22
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Schlembach D, Maner WL, Garfield RE, Maul H. Monitoring the progress of pregnancy and labor using electromyography. Eur J Obstet Gynecol Reprod Biol 2009; 144 Suppl 1:S33-9. [DOI: 10.1016/j.ejogrb.2009.02.016] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Extraction, quantification and characterization of uterine magnetomyographic activity--a proof of concept case study. Eur J Obstet Gynecol Reprod Biol 2009; 144 Suppl 1:S96-100. [PMID: 19303190 DOI: 10.1016/j.ejogrb.2009.02.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
OBJECTIVE The objective was to extract, quantify and characterize the uterine magnetomyographic (MMG) signals that correspond to the electrophysiological activity of the uterus. METHODS Transabdominal MMG recordings with high spatial-temporal resolution were performed with the use of the 151 non-invasive magnetic sensor system. The extraction, quantification and characterization procedures were developed and applied to representative MMG signals that were recorded from a pregnant woman at regular intervals starting at 37 weeks of gestation until the subject reached active labor. RESULTS Multiple MMG recordings were successfully performed on the subject before she went into active labor. The extracted MMG burst activity showed a statistically significant correlation (r=0.2; p<0.001) with the contractile events perceived by mothers. The time-frequency analysis of the burst activity showed a power shift towards higher-frequency at 48 h before the subject went into active labor as compared to earlier recordings. Further there was a gradual increase in the synchrony in the higher-frequency band as the subject reached close to active labor. CONCLUSIONS The non-invasive recording of the magnetic signals of pregnant uterus with high spatial-temporal resolution can provide an insight into the preparatory phase of labor and has the potential of predicting term and preterm labor.
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A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Med Biol Eng Comput 2008; 46:911-22. [PMID: 18437439 DOI: 10.1007/s11517-008-0350-y] [Citation(s) in RCA: 114] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2007] [Accepted: 04/06/2008] [Indexed: 10/22/2022]
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Shi SQ, Maner WL, Mackay LB, Garfield RE. Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals. Am J Obstet Gynecol 2008; 198:235.e1-4. [PMID: 18226633 DOI: 10.1016/j.ajog.2007.08.039] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2006] [Revised: 06/06/2007] [Accepted: 08/20/2007] [Indexed: 02/07/2023]
Abstract
OBJECTIVE This study was undertaken to use artificial neural networks on uterine electromyography data to identify term and preterm labor in rats. STUDY DESIGN Controls (group 1: n = 4) and preterm labor models (group 2: n = 4, treated with onapristone) were used. Uterine electromyography and intrauterine pressure (IUP) variables were measured by implanted telemetric devices. For each timepoint assessed, either a "labor event" or "nonlabor event" was first assigned by using visual and other means. 112 total labor and nonlabor events were observed. Artificial neural networks were then used with electromyography and intrauterine pressure parameters to attempt algorithmic, objective identification for time of labor in each group. RESULTS For group 1, all 8 (100%) labor events and all 44 (100%) nonlabor events were correctly identified by the artificial neural networks. For group 2, 22 of 24 (92%) labor events and 31 of 36 (86%) nonlabor events were correctly determined by the artificial neural networks. CONCLUSION Artificial neural networks can effectively predict term and preterm labor during pregnancy with the use of uterine electromyography and intrauterine pressure variables.
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Affiliation(s)
- Shao-Qing Shi
- Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX 77555, USA
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Taggart MJ, Blanks A, Kharche S, Holden A, Wang B, Zhang H. Towards understanding the myometrial physiome: approaches for the construction of a virtual physiological uterus. BMC Pregnancy Childbirth 2007; 7 Suppl 1:S3. [PMID: 17570163 PMCID: PMC1892060 DOI: 10.1186/1471-2393-7-s1-s3] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Premature labour (PTL) is the single most significant factor contributing to neonatal morbidity in Europe with enormous attendant healthcare and social costs. Consequently, it remains a major challenge to alleviate the cause and impact of this condition. Our ability to improve the diagnosis and treatment of women most at risk of PTL is, however, actually hampered by an incomplete understanding of the ways in which the functions of the uterine myocyte are integrated to effect an appropriate biological response at the multicellular whole organ system. The level of organization required to co-ordinate labouring uterine contractile effort in time and space can be considered immense. There is a multitude of what might be considered mini-systems involved, each with their own regulatory feedback cycles, yet they each, in turn, will influence the behaviour of a related system. These include, but are not exclusive to, gestational-dependent regulation of transcription, translation, post-translational modifications, intracellular signaling dynamics, cell morphology, intercellular communication and tissue level morphology. We propose that in order to comprehend how these mini-systems integrate to facilitate uterine contraction during labour (preterm or term) we must, in concert with biological experimentation, construct detailed mathematical descriptions of our findings. This serves three purposes: firstly, providing a quantitative description of series of complex observations; secondly, proferring a database platform that informs further testable experimentation; thirdly, advancing towards the establishment of a virtual physiological uterus and in silico clinical diagnosis and treatment of PTL.
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Affiliation(s)
- Michael John Taggart
- Maternal and Fetal Health Research Centre, University of Manchester, St Mary's Hospital, Hathersage Road, Manchester, M13 0JH, UK
| | - Andrew Blanks
- Clinical Sciences Research Centre, Warwick Medical School, Coventry, UK
| | - Sanjay Kharche
- School of Physics, University of Manchester, Manchester, M13 9PL, UK
| | - Arun Holden
- Institute of Membrane & Systems Biology, University of Leeds, Leeds, UK
| | - Bin Wang
- School of Engineering and Physical Sciences, University of Aberdeen, Aberdeen, UK
| | - Henggui Zhang
- School of Physics, University of Manchester, Manchester, M13 9PL, UK
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Garfield RE, Maner WL. Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 2007; 18:289-95. [PMID: 17659954 PMCID: PMC2048588 DOI: 10.1016/j.semcdb.2007.05.004] [Citation(s) in RCA: 145] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2007] [Accepted: 05/03/2007] [Indexed: 11/22/2022]
Abstract
Presently, there is no effective treatment for preterm labor. The most obvious reason for this anomaly is that there is no objective manner to evaluate the progression of pregnancy through steps leading to labor, either at term or preterm. Several techniques have been adopted to monitor labor, and/or to diagnose labor, but they are either subjective or indirect, and they do not provide an accurate prediction of when labor will occur. With no method to determine preterm labor, treatment might never improve. Uterine electromyography (EMG) methods may provide such needed diagnostics.
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Affiliation(s)
- Robert E Garfield
- University of Texas Medical Branch, Department of Obstetrics and Gynecology, Division of Reproductive Sciences, 301 University, Route 1062, Galveston, TX 77555, United States.
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Maner WL, Garfield RE. Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 2007; 35:465-73. [PMID: 17226089 DOI: 10.1007/s10439-006-9248-8] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2006] [Accepted: 12/07/2006] [Indexed: 10/23/2022]
Abstract
OBJECTIVE To use artificial neural networks (ANNs) on uterine electromyography (EMG) data to classify term/preterm labor/non-labor pregnant patients. MATERIALS AND METHODS A total of 134 term and 51 preterm women (all ultimately delivered spontaneously) were included. Uterine EMG was measured trans-abdominally using surface electrodes. "Bursts" of elevated uterine EMG, corresponding to uterine contractions, were quantified by finding the means and/or standard deviations of the power spectrum (PS) peak frequency, burst duration, number of bursts per unit time, and total burst activity. Measurement-to-delivery (MTD) time was noted for each patient. Term and preterm patient groups were sub-divided, resulting in the following categories: [term-laboring (TL): n = 75; preterm-laboring (PTL): n = 13] and [term-non-laboring (TN): n = 59; preterm-non-laboring (PTN): n = 38], with labor assessed using clinical determinations. ANN was then used on the calculated uterine EMG data to algorithmically and objectively classify patients into labor and non-labor. The percent of correctly categorized patients was found. Comparison between ANN-sorted groups was then performed using Student's t test (with p < 0.05 significant). RESULTS In total, 59/75 (79%) of TL patients, 12/13 (92%) of PTL patients, 51/59 (86%) of TN patients, and 27/38 (71%) of PTN patients were correctly classified. CONCLUSION ANNs, used with uterine EMG data, can effectively classify term/preterm labor/non-labor patients.
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Affiliation(s)
- William L Maner
- Department of Obstetrics and Gynecology, University of Texas Medical Branch, 301 University, Route 1062, Galveston, TX 77555, USA
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