Types of EMD Algorithms

Ahmadi, Hessam; Ekhlasi, Ali

doi:10.1109/icspis48872.2019.9066155

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The empirical mode decomposition (EMD) method was applied to eliminate unnecessary variations in the signals and obtain the intrinsic mode functions (IMF) [44] more representative of contractions and non-contractions. The second IMF was considered in our case, as it is more representative, as confirmed in [36].…”

Section: Dataset Pre-processingmentioning

confidence: 99%

N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy

et al. 2022

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The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, eventually, death. In this regard, several artificial-intelligence-based methods have been proposed based on the detection of contractions using electrohysterogram (EHG) signals. However, the forecasting of pregnancy contractions based on real-time EHG signals is a challenging task. This study proposes a novel model based on neural basis expansion analysis for interpretable time series (N-BEATS) which predicts labour based on EHG forecasting and contraction classification over a given time horizon. The publicly available TPEHG database of Physiobank was exploited in order to train and test the model, where signals from full-term pregnant women and signals recorded after 26 weeks of gestation were collected. For these signals, the 30 most commonly used classification parameters in the literature were calculated, and principal component analysis (PCA) was utilized to select the 15 most representative parameters (all the domains combined). The results show that neural basis expansion analysis for interpretable time series (N-BEATS) forecasting can forecast EHG signals through training after few iterations. Similarly, the forecasting signal’s duration is determined by the length of the recordings. We then deployed XG-Boost, which achieved the classification accuracy of 99 percent, outperforming the state-of-the-art approaches using a number of classification features greater than or equal to 15.

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Section: Dataset Pre-processingmentioning

confidence: 99%

N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy

et al. 2022

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“…Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes [29,30].…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar Algorithm

Liu

2022

Sensors

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To reduce the pain and the probability of cross-infection caused by the invasive blood glucose testing instruments, the ex vivo glucose measurement is of high significance. The electrical property of blood varies with the density of the glucose, which can be sensed by measuring its reflected coefficient in millimeter-wave. In this article, we built a contactless glucose solution concentration measurement system based on 77-GHz FMCW radar. Several preliminary signal processing algorithms are cascaded with a deep neural network to improve the accuracy of glucose solution concentration measurement. Our experiment shows that the resolution of this ex vivo glucose measurement can achieve up to 0.1 mg/mL.

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“…First, the number of extrema and the number of zero crossings must be the same or may differ by one at most. Second, at any point, the mean value of the envelopes defined by the local maxima and the envelopes defined by the local minima is zero [46], [60]. The algorithm is iterative and can be described in the following steps [46], [59]- [61]:…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

“…The original signal can be reconstructed by summing all extracted IMFs and the last residue. More detailed description of the method can be found, for example, in [60]- [63]. In [10], [46], [59], [64], the method was tested for the purpose of fPCG filtration.…”

Section: A Empirical Mode Decompositionmentioning

confidence: 99%

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

et al. 2020

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Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative lowcost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|∆T i |), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC > 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC > 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. INDEX TERMSFetal phonocardiography (fPCG), fetal heart rate (fHR), non-invasive fetal monitoring, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), adaptive wavelet transform (AWT).

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Types of EMD Algorithms

Cited by 6 publications

References 9 publications

N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy

N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy

A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar Algorithm

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

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