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Liu J, Duan Z, Liu H. A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion. Neural Netw 2024; 178:106400. [PMID: 38850633 DOI: 10.1016/j.neunet.2024.106400] [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: 11/23/2023] [Revised: 05/07/2024] [Accepted: 05/19/2024] [Indexed: 06/10/2024]
Abstract
In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.
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Affiliation(s)
- Jiangxun Liu
- Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China
| | - Zhu Duan
- Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China
| | - Hui Liu
- Institute of Artificial Intelligence and Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, Hunan, China.
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Škorić T. Stress Level Detection Based on the Capacitive Electrocardiogram Signals of Driving Subjects. SENSORS (BASEL, SWITZERLAND) 2023; 23:9158. [PMID: 38005545 PMCID: PMC10674978 DOI: 10.3390/s23229158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023]
Abstract
The automotive industry and scientific community are making efforts to develop innovative solutions that would increase successful driver performance in preventing crashes caused by drivers' health and concentration. High stress is one of the causes of impaired driver performance. This study investigates the ability to classify different stress levels based on capacitive electrocardiogram (cECG) recorded during driving by unobtrusive acquisition systems with different hardware implementations. The proposed machine-learning model extracted only four features, based on the detection of the R peak, which is the most reliably detected characteristic point even in inferior quality cECG. Another criterion for selecting the features is their low computational complexity, which enables real-time application. The proposed method was validated on three open data sets recorded during driving: electrocardiogram (ECG) recorded by electrodes with direct skin contact (high quality); cECG recorded without direct skin contact through clothes by electrodes built into a portable multi-modal cushion (middle quality); and cECG recorded through the clothes without direct skin contact by electrodes built into a car seat (lowest quality). The proposed model achieved a high accuracy of 100% for high-quality ECG, 96.67% for middle-quality cECG, and 98.08% for the lower-quality cECG.
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Affiliation(s)
- Tamara Škorić
- Faculty of Technical Science, University of Novi Sad, 21000 Novi Sad, Serbia
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Škorić T. Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects. ENTROPY (BASEL, SWITZERLAND) 2021; 24:13. [PMID: 35052039 PMCID: PMC8775042 DOI: 10.3390/e24010013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 11/27/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status.
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Affiliation(s)
- Tamara Škorić
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
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Bajić D, Đajić V, Milovanović B. Entropy Analysis of COVID-19 Cardiovascular Signals. ENTROPY (BASEL, SWITZERLAND) 2021; 23:E87. [PMID: 33435378 PMCID: PMC7826611 DOI: 10.3390/e23010087] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/28/2020] [Accepted: 01/06/2021] [Indexed: 12/23/2022]
Abstract
The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.
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Affiliation(s)
- Dragana Bajić
- Faculty of Technical Sciences, University of Novi Sad, Novi Sad 21000, Serbia
| | - Vlado Đajić
- Neurology Clinic, University Clinical Centre of the Republic of Srpska, 78000 Banja Luka, Bosnia and Herzegovina;
| | - Branislav Milovanović
- Faculty of Medicine, University of Belgrade, Belgrade 11000, Serbia;
- University Hospital Center Bežanijska Kosa, Belgrade 11000, Serbia
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Bajić D, Mišić N, Škorić T, Japundžić-Žigon N, Milovanović M. On Entropy of Probability Integral Transformed Time Series. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1146. [PMID: 33286915 PMCID: PMC7597301 DOI: 10.3390/e22101146] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/27/2020] [Accepted: 10/09/2020] [Indexed: 11/17/2022]
Abstract
The goal of this paper is to investigate the changes of entropy estimates when the amplitude distribution of the time series is equalized using the probability integral transformation. The data we analyzed were with known properties-pseudo-random signals with known distributions, mutually coupled using statistical or deterministic methods that include generators of statistically dependent distributions, linear and non-linear transforms, and deterministic chaos. The signal pairs were coupled using a correlation coefficient ranging from zero to one. The dependence of the signal samples is achieved by moving average filter and non-linear equations. The applied coupling methods are checked using statistical tests for correlation. The changes in signal regularity are checked by a multifractal spectrum. The probability integral transformation is then applied to cardiovascular time series-systolic blood pressure and pulse interval-acquired from the laboratory animals and represented the results of entropy estimations. We derived an expression for the reference value of entropy in the probability integral transformed signals. We also experimentally evaluated the reliability of entropy estimates concerning the matching probabilities.
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Affiliation(s)
- Dragana Bajić
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia;
| | - Nataša Mišić
- Research and Development Institute Lola Ltd., 11000 Belgrade, Serbia;
| | - Tamara Škorić
- Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia;
| | | | - Miloš Milovanović
- Mathematical Institute of the Serbian Academy of Sciences and Arts, 11000 Beograd, Serbia;
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Gardašević G, Katzis K, Bajić D, Berbakov L. Emerging Wireless Sensor Networks and Internet of Things Technologies-Foundations of Smart Healthcare. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3619. [PMID: 32605071 PMCID: PMC7374296 DOI: 10.3390/s20133619] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/19/2020] [Accepted: 06/23/2020] [Indexed: 12/16/2022]
Abstract
Future smart healthcare systems - often referred to as Internet of Medical Things (IoMT) - will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.
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Affiliation(s)
- Gordana Gardašević
- Faculty of Electrical Engineering, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina;
| | - Konstantinos Katzis
- Department of Computer Science and Engineering, European University Cyprus, 2404 Nicosia, Cyprus;
| | - Dragana Bajić
- Faculty of Technical Science, University of Novi Sad, 21000 Novi Sad, Serbia;
| | - Lazar Berbakov
- Institute Mihajlo Pupin, University of Belgrade, 11060 Belgrade, Serbia
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Liu J, Huang R, Xiao Y, Lin S. ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients. Sleep Breath 2020; 24:1481-1486. [PMID: 31919715 DOI: 10.1007/s11325-019-02004-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 12/09/2019] [Accepted: 12/19/2019] [Indexed: 11/26/2022]
Abstract
OBJECTIVE A new index, approximate entropy (ApEn) of oxygen saturation, was used to assess the severity of hypoxemia in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), determine the correlation with other parameters, and explore its clinical value. METHODS A retrospective analysis was performed on 1200 patients with OSAHS and snorers (normal control). All subjects underwent sleep apnea monitoring for 6 h. Subjects were divided into four subgroups by apnea-hypopnea index (AHI): normal control (AHI < 5), mild OSAHS (5 ≤ AHI < 15), moderate OSAHS (15 ≤ AHI < 30), and severe OSAHS 104 (AHI ≥ 30). ApEn was initially compared among the subgroups. Then a correlation analysis of AHI with ApEn and a correlation analysis of ApEn with oxygen desaturation index (ODI), lowest oxygen saturation (LO2), and T < 90% were performed. (2) The AHI was used as the gold standard, and an attempt was performed to determine the value of ApEn to assess the severity of hypoxemia in OSAHS. RESULTS Among the 1200 subjects, 822 subjects were men (72%) and mean age was 53.2 ± 15.2 years (range 24-95 years). The ApEn in each group was significantly different (P <0.001), and the ApEn synchronously increased with AHI. Furthermore, a significant difference in ApEn was found among the groups (P <0.001). In addition, ApEn had a good correlation with ODI, LO2, and T <90%. According to the ROC analysis results, the boundary value of ApEn to judge OSAHS patients with mild, moderate, and severe hypoxia was 16.72, 17.84, and 20.06, respectively. CONCLUSION ApEn synchronously increased with the AHI and had a good correlation with AHI, ODI, LO2, and T <90%. These findings suggest that ApEn may have clinical value for assessing hypoxia severity in OSAHS patients.
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Affiliation(s)
- Jie Liu
- Department of health management department, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Rong Huang
- Department of Respiratory, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Yi Xiao
- Department of Respiratory, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Songbai Lin
- Department of health management department, Peking Union Medical College Hospital, Beijing, 100730, China.
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(Multiscale) Cross-Entropy Methods: A Review. ENTROPY 2019; 22:e22010045. [PMID: 33285820 PMCID: PMC7516475 DOI: 10.3390/e22010045] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 12/24/2019] [Accepted: 12/26/2019] [Indexed: 11/16/2022]
Abstract
Cross-entropy was introduced in 1996 to quantify the degree of asynchronism between two time series. In 2009, a multiscale cross-entropy measure was proposed to analyze the dynamical characteristics of the coupling behavior between two sequences on multiple scales. Since their introductions, many improvements and other methods have been developed. In this review we offer a state-of-the-art on cross-entropy measures and their multiscale approaches.
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Jovanović S, Jovanović M, Škorić T, Jokić S, Milovanović B, Katzis K, Bajić D. A Mobile Crowd Sensing Application for Hypertensive Patients. SENSORS 2019; 19:s19020400. [PMID: 30669464 PMCID: PMC6359414 DOI: 10.3390/s19020400] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 01/08/2019] [Accepted: 01/11/2019] [Indexed: 11/16/2022]
Abstract
Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension—the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient’s current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems.
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Affiliation(s)
| | - Milan Jovanović
- Endava, Bulevar Milutina Milankovića 11, Belgrade 11000, Serbia.
| | - Tamara Škorić
- Faculty of Technical Sciences, University of Novi Sad, Trg. D. Obradovića 6, Novi Sad 21000, Serbia.
| | - Stevan Jokić
- Svezdrav Rešenja LLC, Đenerala Draže 44, Klenje 15357, Serbia.
| | | | - Konstantinos Katzis
- Department of Computer Science and Engineering, European University Cyprus, Diogenis Str 6, Nicosia 1516, Cyprus.
| | - Dragana Bajić
- Faculty of Technical Sciences, University of Novi Sad, Trg. D. Obradovića 6, Novi Sad 21000, Serbia.
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Thanaj M, Chipperfield AJ, Clough GF. Analysis of microvascular blood flow and oxygenation: Discrimination between two haemodynamic steady states using nonlinear measures and multiscale analysis. Comput Biol Med 2018; 102:157-167. [DOI: 10.1016/j.compbiomed.2018.09.026] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 09/07/2018] [Accepted: 09/24/2018] [Indexed: 11/16/2022]
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