Quality Aware Compression of Multilead Electrocardiogram Signal using 2-mode Tucker Decomposition and Steganography
TL;DR: A quality controlled compression of multilead electrocardiogram (MECG) is proposed, based on tensor analysis, and implemented upon 3D beat tensor of MECG, and has provided superior result as compared to recently published works on M ECG data compression.
About: This article is published in Biomedical Signal Processing and Control.The article was published on 2021-02-01. It has received 9 citations till now. The article focuses on the topics: Tucker decomposition & Data compression.
Citations
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TL;DR: The Modified Sunflower Optimization (MSFO) algorithm is hybrid along with LBM which optimally selects the parameters that maximize the performance of tumour growth model.
15 citations
TL;DR: Bidirectional long short-term memory recurrent neural network based prediction of missing segment of ECG signal is accomplished, governed by reinforcement learning (RL) using multiagent, applicable to any single channel ECG signals.
14 citations
TL;DR: In this paper , the Modified Sunflower Optimization (MSFO) algorithm is hybrid along with Lattice Boltzmann Method (LBM) which optimally selects the parameters that maximize the performance of tumour growth model.
13 citations
TL;DR: In this article, the Monte Carlo filter (MCF)-based MA removal from single-channel ECG signal is proposed, assisting in real-time telecardiology systems, and the proposed algorithm was tested on the IEEE Signal Processing Cup Challenge 2015 ECG database and MIT-BIH arrythmia records, with an improvement of signal-to-noise ratio between 10 and 15 dB.
Abstract: Motion artifact (MA) contamination with electrocardiogram (ECG) signal is a common issue caused by body movement or sensor loosening, resulting in distortion of clinical features of ECG. In this work, the Monte Carlo filter (MCF)-based MA removal from single-channel ECG signal is proposed, assisting in real-time telecardiology systems. Initially, after R-peak detection and beat extraction, principal component (PC) analysis was performed upon clean ECG beats, and PC, with the highest energy, was assumed to be the feature beat. Using this feature beat, MA corrupted beats were denoised successively to achieve a clean pattern of ECG using MCF. A new approach of weight calculation and resampling was also proposed for better performance of the MCF. Performance of the proposed algorithm was tested on the IEEE Signal Processing Cup Challenge 2015 ECG database and MIT-BIH arrythmia records, with an improvement of signal-to-noise ratio between 10 and 15 dB, after MA removal. The proposed work was also tested on real-time ECG data collected from ten healthy volunteers using the AD8232 ECG module and Raspberry Pi, resulting in correlation coefficient higher than 0.99, between the original and denoised signals. The proposed algorithm was able to remove MA from any single-channel MA corrupted ECG signal, irrespective of lead category, using features of clean beats. A comparative study of the obtained result with previously published works ensured the superior performance on MA removal from ECG in the proposed work, along with real-time data collection, processing, and transmission.
7 citations
TL;DR: In this paper, a combination of tunable-Q wavelet transform (TQWT) and adaptive Fourier decomposition (AFD) was used for ECG signal compression.
Abstract: Long-term electrocardiogram (ECG) signal monitoring necessitates a large amount of memory space for storage, which affects the transmission channel efficiency during real-time data transfer. Using a combination of tunable-Q wavelet transform (TQWT) and adaptive Fourier decomposition (AFD), the proposed work develops a new single-channel ECG signal compression algorithm. The input parameters of TQWT were selected so that the lowest frequency subband contained highest energy along with minimal loss. A new Mobius transform-based AFD was introduced to improve the fidelity, by computing highest energy coefficients using Nevanlinna factorization, with suitable decomposition level. Finally, the lossless compression was performed in polar coordinate of final complex coefficients that significantly improved the compression ratio (CR). The algorithm was tested on “python” programming platform, tested in Raspberry Pi (R-Pi), for real-time data processing, and wireless transmission to cloud server and smartphone devices. The suggested work yielded CR, percent root mean square error (PRD), and PRD normalized (PRDN) of 30.06, 7.80, and 11.62, respectively, after testing on 48 Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ECG data with 30-min duration. A rigorous quality assessment of the reconstructed signal ensured that there was minimal impact on various characteristic domains in the ECG signal, enhancing its acceptability in medical applications.
5 citations
References
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01 Jan 2020TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Abstract: The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.
16,983 citations
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.
9,227 citations
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
3,439 citations
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01 Jan 1995
1,613 citations