Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption
Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7926 |
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| author | Israel Edem Agbehadji Richard C. Millham Emmanuel Freeman Wanqing Wu Xianbin Zhang |
| author_facet | Israel Edem Agbehadji Richard C. Millham Emmanuel Freeman Wanqing Wu Xianbin Zhang |
| author_sort | Israel Edem Agbehadji |
| collection | DOAJ |
| description | Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features. An example of a human-related attribute includes the electrical signal generated from the heart, mostly referred to as an Electrocardiogram (ECG) signal. The methods to extract features from ECG signals are time domain-based; however, the challenge with relying only on the time-domain or frequency-domain method is the inability to capture the intra-leading relationship of Variational Mode Decomposition signals. In this research, fusing multiple domains ECG feature and adaptive Variational Mode Decomposition approaches are utilized to mitigate the challenge of losing the intra-leading correlations of mode decompositions, which might reduce the robustness of encryption algorithms. The features extracted using the reconstructed signal have a mean (0.0004), standard deviation (0.0391), skewness (0.1562), and kurtosis (1.2205). Among the lightweight encryption methods considered, Chacha20 has a total execution time of 27µs. The study proposes a lightweight encryption technique based on the fused vector representation of extracted features to provide an encryption scheme in addition to a bio-inspired key generation technique for data encryption. |
| format | Article |
| id | doaj-art-7a06fe5d9b6c44c6a9e96e62399a7d1d |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-7a06fe5d9b6c44c6a9e96e62399a7d1d2025-08-20T02:57:01ZengMDPI AGSensors1424-82202024-12-012424792610.3390/s24247926Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and EncryptionIsrael Edem Agbehadji0Richard C. Millham1Emmanuel Freeman2Wanqing Wu3Xianbin Zhang4Honorary Research Fellow, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South AfricaICT and Society Research Group, Department of Information Technology, Durban University of Technology, P.O. Box 1334, Durban 4000, South AfricaCentre for Augmented Intelligence and Data Science, School of Computing, University of South Africa, Johannesburg 1709, South AfricaSchool of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Biomedical Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaSecurity is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features. An example of a human-related attribute includes the electrical signal generated from the heart, mostly referred to as an Electrocardiogram (ECG) signal. The methods to extract features from ECG signals are time domain-based; however, the challenge with relying only on the time-domain or frequency-domain method is the inability to capture the intra-leading relationship of Variational Mode Decomposition signals. In this research, fusing multiple domains ECG feature and adaptive Variational Mode Decomposition approaches are utilized to mitigate the challenge of losing the intra-leading correlations of mode decompositions, which might reduce the robustness of encryption algorithms. The features extracted using the reconstructed signal have a mean (0.0004), standard deviation (0.0391), skewness (0.1562), and kurtosis (1.2205). Among the lightweight encryption methods considered, Chacha20 has a total execution time of 27µs. The study proposes a lightweight encryption technique based on the fused vector representation of extracted features to provide an encryption scheme in addition to a bio-inspired key generation technique for data encryption.https://www.mdpi.com/1424-8220/24/24/7926time-domain feature extractionlightweight encryptionadaptive variational mode decompositionECG feature extractionbio-inspired key generation |
| spellingShingle | Israel Edem Agbehadji Richard C. Millham Emmanuel Freeman Wanqing Wu Xianbin Zhang Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption Sensors time-domain feature extraction lightweight encryption adaptive variational mode decomposition ECG feature extraction bio-inspired key generation |
| title | Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption |
| title_full | Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption |
| title_fullStr | Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption |
| title_full_unstemmed | Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption |
| title_short | Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption |
| title_sort | fused multi domains and adaptive variational mode decomposition ecg feature extraction for lightweight bio inspired key generation and encryption |
| topic | time-domain feature extraction lightweight encryption adaptive variational mode decomposition ECG feature extraction bio-inspired key generation |
| url | https://www.mdpi.com/1424-8220/24/24/7926 |
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