Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography. (February 2021)

• Record Type: Journal Article
• Authors: Banerjee, Soumyendu; Singh, Girish Kumar
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: 3D beat tensor -- Tucker decomposition -- Principal component analysis -- Particle swarm optimization -- Multi agent supervised learning system -- Steganography; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102230
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: In this article, a quality controlled compression of multilead electrocardiogram (MECG) is proposed, based on tensor analysis, and implemented upon 3D beat tensor of MECG. To reduce computational complications and execution time, a new approach of principal component analysis (PCA), based on 2-mode Tucker Decomposition, is introduced. In order to maintain relevant features of MECG after reconstruction, multi agent supervised learning system (MASLS) based optimal quantization of each fiber of core tensor is introduced, to limit the percentage root mean squared difference (PRD) within a specified value, maintaining high compression ratio (CR). The MASLS is previously trained offline, using features of tensor fibers, along with optimized quantization levels of those fibers, obtained from a particle swarm optimization (PSO), as reference. In addition, to hide patient's confidential information, steganography is performed within the core tensor followed by generation of a' secret key', which is necessary, while decrypting those information during reconstruction. The whole algorithm is implemented on several MECG records, available in PTB Diagnostic ECG database, and compression result is compared by formation of n -beat tensor separately, using ' n ' number of successive (' n ' = 5, 10 and 15) beats. After testing on 547 data, average CR of 22, 41.5, 55.4, PRD of 3.62, 4.96, 5.59 and PRD normalized (PRDN) of 3.61, 4.94, 5.57 are achieved for 5, 10, 15-beat tensor, respectively. This proposed algorithm has provided superior result as compared to recently published works on MECG data compression.