Book ChapterDOI
Sparse Encoding Algorithm for Real-Time ECG Compression
Rohan Basu Roy,Arani Roy,Amitava Mukherjee,Alekhya Ghosh,Soham Bhattacharyya,Mrinal Kanti Naskar +5 more
- pp 31-38
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TLDR
A sparse encoding algorithm consisting of two schemes namely geometry-based method (GBM) and the wavelet transform-based iterative thresholding (WTIT) that converts the sparse matrices into compressed, transmittable matrices.Abstract:
In this paper, we propose a sparse encoding algorithm consisting of two schemes namely geometry-based method (GBM) and the wavelet transform-based iterative thresholding (WTIT). The sub-algorithm GBM reduces the minimal ECG voltage values to zero level. Subsequently, WTIT encodes the ECG signal in time-frequency domain, obtaining high sparsity levels. Compressed Row Huffman Coding (CRHC) algorithm converts the sparse matrices into compressed, transmittable matrices. The performance of the algorithms is validated in terms of compression ratio (CR), percentage RMS difference (PRD), and time complexity.read more
Citations
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Journal ArticleDOI
Energy-Efficient IoT-Health Monitoring System using Approximate Computing
TL;DR: A real time encoding scheme is developed that performs iterative thresholding and approximation of wavelet coefficients for sparse encoding of bio-signals (ECG signals), thereby reducing the energy and bandwidth consumption of the WBSN.
Journal ArticleDOI
Origins of ECG and Evolution of Automated DSP Techniques: A Review
Neha Arora,Biswajit Mishra +1 more
TL;DR: In this paper, a review of the evolution of the ECG and the most recent signal processing schemes with milestones over the last 150 years systematically is presented, focusing on the detection of cardiac anomalies and the history of the development of ECG monitors.
Proceedings ArticleDOI
A Proposed Technique Based on Wavelet Transform for Electrocardiogram Signal Compression
Azmi Shawkat Abdulbaqi,Saif Al Din M. Najim Saif,Falath M. Mohammad Falath,Nawar Ahmed Irsan Nawar +3 more
TL;DR: The aim here develops an efficient algorithm ECG LC that uses the transform based on wavelet followed by the arithmetic coding (AC) on the residual to achieve high compression ratios compared to other compressing algorithms.
Proceedings ArticleDOI
Prediction Method of Aeroengine Residual Life Based on Stacked Sparse Automatic Encoder
TL;DR: The method firstly constructs a plurality of self-encoding networks to form a deep stack self- encoding network, selects the state data of the engine as the training input of the network, and enables the network to extract the distributed rules between the data layer by layer intelligently, thereby constructing the engine degraded stackSelf-Encoding learning.
Proceedings ArticleDOI
An Effective QRS Selection Based on the Level-Crossing Sampling and Activity Selection
TL;DR: In this article, an analog band-pass filter was used to reduce the impact of baseline Wander (BW) and aliasing in ECG signal processing chain, and the band-limited signal was then digitized by using a 5-bit resolution level-crossing A/D converter.
References
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Book
Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide
TL;DR: This book discusses iterative projection methods for solving Eigenproblems, and some of the techniques used to solve these problems came from the literature on Hermitian Eigenvalue.
Journal ArticleDOI
Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes
TL;DR: This paper quantifies the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote and shows that CS represents a competitive alternative to state- of- the-art digital wavelet transform (DWT)-basedECG compression solutions in the context of WBSn-based ECG monitoring systems.
Journal ArticleDOI
Wavelet and wavelet packet compression of electrocardiograms
TL;DR: Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECGs are clinically useful.
Proceedings ArticleDOI
Compressed sensing based method for ECG compression
TL;DR: Simulation results suggest that compressed sensing should be considered as a plausible methodology for ECG compression because it implies a high fraction of common support between consecutive heartbeats.
Proceedings ArticleDOI
Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition
TL;DR: It is shown that CS is quite sensitive to sparsity and compression ratio, while the reconstruction quality of TH-DWT is quite stable, which suggests that while CS is an attractive option for telecardiology, caution should be exercised in applying it for ECG signal compression.
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