A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

Ahmed, Mosabber Uddin; Chanwimalueang, Theerasak; Thayyil, Sudhin; Mandic, Danilo P.

doi:10.3390/e19010002

Cited by 66 publications

(70 citation statements)

References 44 publications

(55 reference statements)

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“…The performance improvement, as measured by the percentage of precise detection and extraction of a plate from a complex image has been noted in a fuzzified approach used in FuzzyGF. The performance of the two presented and compared algorithms is similar to the performance of other license plate detection algorithms [36], [37]. For example, the algorithm proposed by Tan et al [36], uses the morphological operations for detection and separation of the license plate region similarly to the algorithms compared in this paper.…”

Section: Validation Of the Algorithmssupporting

confidence: 53%

“…In the applications where some parameters are imprecisely or incorrectly set (such as the input image for detection of license plates, where the precise angle of shooting, brightness, etc. are not known in advance), the use of the fuzzy logic is a good solution, since the fuzzy system successfully uses human reasoning in solving the problem [1], [9], [10], [37]. In the reality, the boundaries between the wavelength and the angle values may be ambiguous and imprecise, and it is difficult to determine whether an input wavelength or angle belongs completely to a certain interval.…”

Section: Validation Of the Algorithmsmentioning

confidence: 99%

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Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a comparative study of improvements to the algorithms for license plate extraction from images captured using conventional, modest-quality cameras. It compares the results and efficacy of two similar algorithms which primarily differ in the raw image pre-processing stage of the initial image. One algorithm uses the Gabor filter bank with distinctly crisp parameters; the other relies on the fuzzified Gabor filter bank with fuzzified parameters to facilitate their adjustment. Results indicate that the fuzzy reasoning introduced for Gabor filter parameter adjustment improves the detection of the components of interest in complex images and adds minimal deviation compared to the Gabor filter bank with distinctly crisp parameters.

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Section: Validation Of the Algorithmssupporting

confidence: 53%

Section: Validation Of the Algorithmsmentioning

confidence: 99%

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Tadić¹,

Király²,

Ódry³

et al. 2020

ACTA POLYTECH HUNG

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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;…”

mentioning

confidence: 99%

“…between preterm and term, contraction and dummy intervals of the TPEHGT DS, but 1009 also between the entire preterm and term, early and all records of the TPEHG DB. In 1010 comparison to the use of a single frequency band such as 0.34-1.0 Hz [29], or 0.3-3.0 Hz 1011 and MMFE [36], and in comparison to the use of multifrequency band decomposition 1012 approaches such as the EMD method [34,35], or wavelets [35], the proposed method 1013 yielded the classification accuracy of 100% for early records, and quite comparable and 1014 slightly higher classification performance, CA=96.33%, AU C=99.44%, for all records of 1015 the TPEHG DB (Table 11). Sharp cut-offs and high attenuation in the stopbands 1016 appear to be important characteristics to separate frequency bands when the signals 1017 have non-uniform spectral content due to physiological mechanisms residing in separate 1018 frequency bands, as in the case of EHG records.…”

mentioning

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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“…Being similar to the specification of the τ parameter used in the MSE, an optimal selection for the k max used in the TSME is still an ad hoc choice reported in many studies [17], which needs further investigation. Future development of the TSME for multivariate multiscale entropy is worth pursuing, as it has been formulated based on the concept of the MSE [27,28]. An extension of the differential Shannon entropy rate of time series using kernel density estimators for selecting the order and bandwidth parameters [29] may have implications for improving the performance of the TSME.…”

Section: Resultsmentioning

confidence: 99%

Time-Shift Multiscale Entropy Analysis of Physiological Signals

Pham

2017

Entropy

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Measures of predictability in physiological signals using entropy measures have been widely applied in many areas of research. Multiscale entropy expresses different levels of either approximate entropy or sample entropy by means of multiple factors for generating multiple time series, enabling the capture of more useful information than using a scalar value produced by the two entropy methods. This paper presents the use of different time shifts on various intervals of time series to discover different entropy patterns of the time series. Examples and experimental results using white noise, 1/ f noise, photoplethysmography, and electromyography signals suggest the validity and better performance of the proposed time-shift multiscale entropy analysis of physiological signals than the multiscale entropy.

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A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

Cited by 66 publications

References 44 publications

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Comparison of Gabor Filter Bank and Fuzzified Gabor Filter for License Plate Detection

Characterization and automatic classification of preterm and term uterine records

Time-Shift Multiscale Entropy Analysis of Physiological Signals

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