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Li Y, Gu Z, Fan X. Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One Dimensional-Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2024; 24:1680. [PMID: 38475216 DOI: 10.3390/s24051680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/18/2024] [Accepted: 03/02/2024] [Indexed: 03/14/2024]
Abstract
This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization-slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization-slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization-slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.
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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
| | - Zhaoyu Gu
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
| | - Xiumei Fan
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
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Castiglioni P, Merati G, Parati G, Faini A. Sample, Fuzzy and Distribution Entropies of Heart Rate Variability: What Do They Tell Us on Cardiovascular Complexity? ENTROPY (BASEL, SWITZERLAND) 2023; 25:281. [PMID: 36832650 PMCID: PMC9954876 DOI: 10.3390/e25020281] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/25/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
Distribution Entropy (DistEn) has been introduced as an alternative to Sample Entropy (SampEn) to assess the heart rate variability (HRV) on much shorter series without the arbitrary definition of distance thresholds. However, DistEn, considered a measure of cardiovascular complexity, differs substantially from SampEn or Fuzzy Entropy (FuzzyEn), both measures of HRV randomness. This work aims to compare DistEn, SampEn, and FuzzyEn analyzing postural changes (expected to modify the HRV randomness through a sympatho/vagal shift without affecting the cardiovascular complexity) and low-level spinal cord injuries (SCI, whose impaired integrative regulation may alter the system complexity without affecting the HRV spectrum). We recorded RR intervals in able-bodied (AB) and SCI participants in supine and sitting postures, evaluating DistEn, SampEn, and FuzzyEn over 512 beats. The significance of "case" (AB vs. SCI) and "posture" (supine vs. sitting) was assessed by longitudinal analysis. Multiscale DistEn (mDE), SampEn (mSE), and FuzzyEn (mFE) compared postures and cases at each scale between 2 and 20 beats. Unlike SampEn and FuzzyEn, DistEn is affected by the spinal lesion but not by the postural sympatho/vagal shift. The multiscale approach shows differences between AB and SCI sitting participants at the largest mFE scales and between postures in AB participants at the shortest mSE scales. Thus, our results support the hypothesis that DistEn measures cardiovascular complexity while SampEn/FuzzyEn measure HRV randomness, highlighting that together these methods integrate the information each of them provides.
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Affiliation(s)
- Paolo Castiglioni
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, 21100 Varese, Italy
- Laboratory of Movement Analysis and Bioengineering of Rehabilitation (Lamobir), IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy
| | - Giampiero Merati
- Department of Biotechnology and Life Sciences (DBSV), University of Insubria, 21100 Varese, Italy
- Laboratory of Movement Analysis and Bioengineering of Rehabilitation (Lamobir), IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148 Milan, Italy
| | - Gianfranco Parati
- Department of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, 20145 Milan, Italy
| | - Andrea Faini
- Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, 20145 Milan, Italy
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20131 Milan, Italy
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Gong X, Zou G, Wang H, Ji Y, Zhang Y. Electro-optic chaotic system based on time delay feature hiding and key space enhancement based on chaotic post-processing. APPLIED OPTICS 2022; 61:5098-5105. [PMID: 36256188 DOI: 10.1364/ao.457290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/21/2022] [Indexed: 06/16/2023]
Abstract
To improve the output performance of the classical all-optical chaotic system and solve the security problems of its key exposure and small key space, a new chaotic system, to the best of our knowledge, based on logistic map post-processing is proposed. In terms of the general output performance of the system, the spectrum of the proposed system is flatter than the classical system. Through a bifurcation diagram and permutation entropy analysis, it is found that the output of the system is extremely complex. In terms of security, the simulation results show that, with a reasonable selection of system parameters, key hiding can be achieved under a large parameter range. Moreover, through the sensitivity analysis of logistic parameters, it can be seen that the introduction of logistic parameters can improve the key space of the system and further improve the security of the system.
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Borin AMS, Humeau-Heurtier A, Virgílio Silva LE, Murta LO. Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1620. [PMID: 34945926 PMCID: PMC8700117 DOI: 10.3390/e23121620] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/26/2021] [Accepted: 11/28/2021] [Indexed: 11/16/2022]
Abstract
Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions-as a function of time series length-present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.
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Affiliation(s)
- Airton Monte Serrat Borin
- Federal Institute of Education, Science and Technology of Triangulo Mineiro, Uberaba 38064-790, Brazil;
| | - Anne Humeau-Heurtier
- LARIS—Laboratoire Angevin de Recherche en Ingénierie des Systèmes, University of Angers, 49035 Angers, France;
| | - Luiz Eduardo Virgílio Silva
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto 14049-900, Brazil;
| | - Luiz Otávio Murta
- Department of Computing and Mathematics, School of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-901, Brazil
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Wang F, Lu B, Kang X, Fu R. Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1209. [PMID: 34573834 PMCID: PMC8469593 DOI: 10.3390/e23091209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/10/2021] [Accepted: 09/10/2021] [Indexed: 12/21/2022]
Abstract
The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Bin Lu
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Xiaogang Kang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
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Frassineti L, Lanatà A, Olmi B, Manfredi C. Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures. Bioengineering (Basel) 2021; 8:122. [PMID: 34562944 PMCID: PMC8469929 DOI: 10.3390/bioengineering8090122] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn's neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.
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Affiliation(s)
- Lorenzo Frassineti
- Department of Information Engineering, Università degli Studi di Firenze, Via Santa Marta 3, 50139 Firenze, Italy; (A.L.); (B.O.); (C.M.)
- Department of Medical Biotechnologies, Università di Siena, 53100 Siena, Italy
| | - Antonio Lanatà
- Department of Information Engineering, Università degli Studi di Firenze, Via Santa Marta 3, 50139 Firenze, Italy; (A.L.); (B.O.); (C.M.)
| | - Benedetta Olmi
- Department of Information Engineering, Università degli Studi di Firenze, Via Santa Marta 3, 50139 Firenze, Italy; (A.L.); (B.O.); (C.M.)
| | - Claudia Manfredi
- Department of Information Engineering, Università degli Studi di Firenze, Via Santa Marta 3, 50139 Firenze, Italy; (A.L.); (B.O.); (C.M.)
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Jara JL, Morales-Rojas C, Fernández-Muñoz J, Haunton VJ, Chacón M. Using complexity-entropy planes to detect Parkinson's disease from short segments of haemodynamic signals. Physiol Meas 2021; 42. [PMID: 34256359 DOI: 10.1088/1361-6579/ac13ce] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 07/13/2021] [Indexed: 11/11/2022]
Abstract
Objective. There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available. This study aimed to determine whether entropy and complexity measures can detect differences between subjects with Parkinsons disease and healthy controls (HCs).Approach. A method based on a diagram of entropy versus complexity, named complexity-entropy plane, was used to re-analyse a dataset of cerebral haemodynamic signals from subjects with Parkinsons disease and HCs obtained under poikilocapnic conditions. A probability distribution for a set of ordinal patterns, designed to capture regularities in a time series, was computed from each signal under analysis. Four types of entropy and ten types of complexity measures were estimated from these distributions. Mean values of entropy and complexity were compared and their classification power was assessed by evaluating the best linear separator on the corresponding complexity-entropy planes.Main results. Few linear separators obtained significantly better classification, evaluated as the area under the receiver operating characteristic curve, than signal mean values. However, significant differences in both entropy and complexity were detected between the groups of participants.Significance. Measures of entropy and complexity were able to detect differences between healthy volunteers and subjects with Parkinson's disease, in poikilocapnic conditions, even though only short recordings were available for analysis. Further work is needed to refine this promising approach, and to help understand the findings in the context of specific pathophysiological changes.
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Affiliation(s)
- J L Jara
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Usach, Santiago, Chile
| | - Catalina Morales-Rojas
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Usach, Santiago, Chile
| | - Juan Fernández-Muñoz
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Usach, Santiago, Chile
| | - Victoria J Haunton
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Max Chacón
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Usach, Santiago, Chile
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Borin AMS, Silva LEV, Murta LO. Response to "Comment on 'Modified multiscale fuzzy entropy: A robust method for short-term physiologic signals"' [Chaos 30, 083135 (2020)]. CHAOS (WOODBURY, N.Y.) 2021; 31:018102. [PMID: 33754788 DOI: 10.1063/5.0040708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Airton Monte Serrat Borin
- Federal Institute for Education Science and Technology of Triângulo Mineiro, IFTM, Uberaba, MG, Brazil
| | - Luiz Eduardo Virgilio Silva
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, SP 14049-900, Brazil
| | - Luiz Otavio Murta
- Department of Computing and Mathematics, Ribeirão Preto School of Philosophy, Science and Literature, University of São Paulo, Ribeirão Preto, SP 14049-900, Brazil
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Faini A, Castiglioni P. Comment on "Modified multiscale fuzzy entropy: A robust method for short-term physiologic signals" [Chaos 30, 083135 (2020)]. CHAOS (WOODBURY, N.Y.) 2021; 31:018103. [PMID: 33754791 DOI: 10.1063/5.0034877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Affiliation(s)
- Andrea Faini
- Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, Milan, Italy
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