Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features
Preterm birth (gestational age < 37 weeks) is a serious pregnancy related complication that could lead to fetal morbidity and mortality. Monitoring the activity of uterus is considered to be crucial for the early diagnosis of preterm birth. Uterine Electromyography (uEMG) is a non-invasive techni...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2021-10-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2021-2200 |
| _version_ | 1811194203532689408 |
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| author | Padmanabhan Vardhini Namadurai Punitha Swaminathan Ramakrishnan |
| author_facet | Padmanabhan Vardhini Namadurai Punitha Swaminathan Ramakrishnan |
| author_sort | Padmanabhan Vardhini |
| collection | DOAJ |
| description | Preterm birth (gestational age < 37 weeks) is a serious pregnancy related complication that could lead to fetal morbidity and mortality. Monitoring the activity of uterus is considered to be crucial for the early diagnosis of preterm birth. Uterine Electromyography (uEMG) is a non-invasive technique that provides a quantitative measure of uterine activity from the abdominal surface. In this work, an attempt has been made to characterize preterm uEMG signals using Empirical Mode Decomposition based Detrended Fluctuation Analysis (EMD-DFA). Preterm signals with varied gestational ages are considered from an online database. EMD-DFA is applied on these signals to compute the fluctuation function. The double-logarithmic plot of fluctuation function versus scale is evaluated and Chi-square analysis is performed for identifying linear scaling regions. Five features namely shortterm exponent (Hs), long-term exponent (Hl), inflection point, short-term fractal angle (αHs) and long-term fractal angle (αHl) are extracted and analyzed. Further, Coefficient of Variation (CV) is computed to examine the variations of these features among different subjects. Results show that EMD-DFA is able to characterize the fluctuations of preterm signals. From the double-logarithmic plot, a slow variation of fluctuation function is observed with respect to scale when the time to delivery is more. This indicates the presence of rapid signal fluctuations in the early stages of pregnancy. Based on the feature values, it is observed that the signal fluctuations are more correlated and smoother as the time to delivery approaches. Among the extracted features, CV values of Hs, Hl, αHs and αHl are observed to be low indicating that these features have least inter-subject variations in preterm signals. The EMD-DFA based fractal features show the ability to detect the subtle variations in uEMG signals. As early diagnosis of preterm delivery is imperative for timely medical intervention and treatment, it appears that the proposed approach could aid in determining the changes in uterine contractions in preterm condition. |
| first_indexed | 2024-04-12T00:22:01Z |
| format | Article |
| id | doaj.art-2b34f29302af442390370b231ab3d574 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-12T00:22:01Z |
| publishDate | 2021-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-2b34f29302af442390370b231ab3d5742022-12-22T03:55:41ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042021-10-017278378610.1515/cdbme-2021-2200Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal FeaturesPadmanabhan Vardhini0Namadurai Punitha1Swaminathan Ramakrishnan2Research Scholar, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai,Tamilnadu, IndiaAssistant Professor, Department of Biomedical Engineering, SSN College of Engineering, Chennai,Tamilnadu, IndiaProfessor, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai,Tamilnadu, IndiaPreterm birth (gestational age < 37 weeks) is a serious pregnancy related complication that could lead to fetal morbidity and mortality. Monitoring the activity of uterus is considered to be crucial for the early diagnosis of preterm birth. Uterine Electromyography (uEMG) is a non-invasive technique that provides a quantitative measure of uterine activity from the abdominal surface. In this work, an attempt has been made to characterize preterm uEMG signals using Empirical Mode Decomposition based Detrended Fluctuation Analysis (EMD-DFA). Preterm signals with varied gestational ages are considered from an online database. EMD-DFA is applied on these signals to compute the fluctuation function. The double-logarithmic plot of fluctuation function versus scale is evaluated and Chi-square analysis is performed for identifying linear scaling regions. Five features namely shortterm exponent (Hs), long-term exponent (Hl), inflection point, short-term fractal angle (αHs) and long-term fractal angle (αHl) are extracted and analyzed. Further, Coefficient of Variation (CV) is computed to examine the variations of these features among different subjects. Results show that EMD-DFA is able to characterize the fluctuations of preterm signals. From the double-logarithmic plot, a slow variation of fluctuation function is observed with respect to scale when the time to delivery is more. This indicates the presence of rapid signal fluctuations in the early stages of pregnancy. Based on the feature values, it is observed that the signal fluctuations are more correlated and smoother as the time to delivery approaches. Among the extracted features, CV values of Hs, Hl, αHs and αHl are observed to be low indicating that these features have least inter-subject variations in preterm signals. The EMD-DFA based fractal features show the ability to detect the subtle variations in uEMG signals. As early diagnosis of preterm delivery is imperative for timely medical intervention and treatment, it appears that the proposed approach could aid in determining the changes in uterine contractions in preterm condition.https://doi.org/10.1515/cdbme-2021-2200uterine electromyographypreterm pregnanciesempirical mode decomposition based detrended fluctuation analysisfluctuation function. |
| spellingShingle | Padmanabhan Vardhini Namadurai Punitha Swaminathan Ramakrishnan Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features Current Directions in Biomedical Engineering uterine electromyography preterm pregnancies empirical mode decomposition based detrended fluctuation analysis fluctuation function. |
| title | Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features |
| title_full | Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features |
| title_fullStr | Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features |
| title_full_unstemmed | Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features |
| title_short | Analysis of Preterm Pregnancies using Empirical Mode Decomposition based Fractal Features |
| title_sort | analysis of preterm pregnancies using empirical mode decomposition based fractal features |
| topic | uterine electromyography preterm pregnancies empirical mode decomposition based detrended fluctuation analysis fluctuation function. |
| url | https://doi.org/10.1515/cdbme-2021-2200 |
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