RETRACTED ARTICLE: Bilateral hashing model ofECG signal encryption system using downhill peak follow (DPF)-based encryptiontechnique

This research suggests a unique ECG security solution based on the data pattern-based encryption paradigm to address security issues in ECG signal transmission or storage environments. According to this concept, the encryption system controls the data security process by extracting key signatures fr...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 27; no. 16; pp. 11843 - 11851
Main Authors Kumar, A. N. Sanjeev, Naik, B. Ramesh
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.08.2023
Subjects
Accuracy
Algorithms
Biometric identification
Biometrics
Cardiac arrhythmia
Cryptography
Data encryption
Data integrity
Electrocardiography
Encryption
Feature selection
Medical equipment
Neural networks
Performance evaluation
Security
Security systems
Sensors
Signal analyzers
Signal transmission
Waveforms
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Summary:This research suggests a unique ECG security solution based on the data pattern-based encryption paradigm to address security issues in ECG signal transmission or storage environments. According to this concept, the encryption system controls the data security process by extracting key signatures from the ECG signal peaks to identify the characteristics or attributes of the data and encrypt it. To estimate the hashing qualities, the approach is to build a random key pattern based on the signal peaks and extract the signature. The signal was then encrypted and sent to a doctor or signal analyzer. Bilateral random hashing (BRH) is used to achieve this. This updates the hashing generation model by analyzing the parameters using probabilistic distributional characteristics. This crucial motif was applied. This key pattern was utilized to encrypt the data samples using the downhill peak follow (DPF) encryption technique, which follows the strength of the peak value in the ECG waveform. This suggested project is carried out using MATLAB scripting.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08684-x