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Mansourian N, Sarafan S, Torkamani-Azar F, Ghirmai T, Cao H. Fetal QRS extraction from single-channel abdominal ECG using adaptive improved permutation entropy. Phys Eng Sci Med 2024; 47:563-573. [PMID: 38329662 DOI: 10.1007/s13246-024-01386-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/07/2024] [Indexed: 02/09/2024]
Abstract
Fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal condition during pregnancy. However, current fECG extraction algorithms are not suitable for wearable devices due to their high computational cost and multi-channel signal requirement. The paper introduces a novel and efficient algorithm called Adaptive Improved Permutation Entropy (AIPE), which can extract fetal QRS from a single-channel abdominal ECG (aECG). The proposed algorithm is robust and computationally efficient, making it a reliable and effective solution for wearable devices. To evaluate the performance of the proposed algorithm, we utilized our clinical data obtained from a pilot study with 10 subjects, each recording lasting 20 min. Additionally, data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations were simulated. The proposed methodology demonstrates an average positive predictive value ( + P ) of 91.0227%, sensitivity (Se) of 90.4726%, and F1 score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. The results suggest that AIPE could enable continuous home-based monitoring of unborn babies, even when mothers are not engaging in any hard physical activities.
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Affiliation(s)
- Nastaran Mansourian
- Faculty of Electrical Engineering, University of Shahid Beheshti, Tehran, Iran
| | - Sadaf Sarafan
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - Tadesse Ghirmai
- Division of Engineering and Mathematics, University of Washington, Bothell Campus, Bothell, WA, 98011, USA
| | - Hung Cao
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA
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Li Z, Yang K, Zhou X, Duan S. A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040669. [PMID: 37190457 PMCID: PMC10138060 DOI: 10.3390/e25040669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023]
Abstract
As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.
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Affiliation(s)
- Zipeng Li
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Kunde Yang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Xingyue Zhou
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
| | - Shunli Duan
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Ocean Acoustics and Sensing, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China
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Mansourian N, Sarafan S, Torkamani-Azar F, Ghirmai T, Cao H. Novel QRS detection based on the Adaptive Improved Permutation Entropy. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Li Y. Entropy and Information Theory in Acoustics. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1760. [PMID: 36554165 PMCID: PMC9777916 DOI: 10.3390/e24121760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Acoustics is one of the most studied fields in the 21st century, encompassing underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychological acoustics, signal processing in acoustics, and so on [...].
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Affiliation(s)
- Yuxing Li
- School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China;
- Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China
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Li J, Wang B, Cui X, Li S, Liu J. Underwater Acoustic Target Recognition Based on Attention Residual Network. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1657. [PMID: 36421512 PMCID: PMC9688950 DOI: 10.3390/e24111657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/11/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.
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Affiliation(s)
- Juan Li
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Baoxiang Wang
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
| | - Xuerong Cui
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
| | - Shibao Li
- College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
| | - Jianhang Liu
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
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Jabloun M, Ravier P, Buttelli O. On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1343. [PMID: 37420363 DOI: 10.3390/e24101343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/12/2022] [Accepted: 09/14/2022] [Indexed: 07/09/2023]
Abstract
Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.
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Affiliation(s)
- Meryem Jabloun
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
| | - Philippe Ravier
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
| | - Olivier Buttelli
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
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Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6070345] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which has shown excellent performance in the fields of medical and hydroacoustics. In order to improve the ability of SlEn to distinguish different types of signals and solve the problem of two threshold parameters selection, a new time series complexity indicator on the basis of SlEn is proposed by introducing fractional calculus and combining particle swarm optimization (PSO), named PSO fractional SlEn (PSO-FrSlEn). Then we apply PSO-FrSlEn to the field of fault diagnosis and propose a single feature extraction method and a double feature extraction method for rolling bearing fault based on PSO-FrSlEn. The experimental results illustrated that only PSO-FrSlEn can classify 10 kinds of bearing signals with 100% classification accuracy by using double features, which is at least 4% higher than the classification accuracies of the other four fractional entropies.
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Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy. ENTROPY 2021; 24:e24010022. [PMID: 35052048 PMCID: PMC8774539 DOI: 10.3390/e24010022] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/14/2021] [Accepted: 12/21/2021] [Indexed: 02/05/2023]
Abstract
In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.
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