Sparse Encoding Algorithm for Real-Time ECG Compression
TL;DR: 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.
••01 Mar 2020
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.
Abstract: Wireless Body Sensor Nodes (WBSN) are frequently used for real time IoT-based health monitoring of patients outside the hospital environment. These WBSNs involve bio-sensors to capture signals from a patient’s body and wireless transmitters to transmit the collected signals to a server located in private/public cloud in real time. These WBSNs include hardware for processing of signals before being transmitted to the cloud. Simultaneous occurrence of all these processes inside energy constrained WBSNs results in considerable amount of power consumption, thus limiting their operational lifetime. Due to the inherent error-resilience in signal processing algorithms, most of these data reaching the servers are redundant in nature and hence of not much clinical importance. Transmission and storage of these excess data result in inefficient usages of transmission bandwidth and storage capabilities. In this paper, we develop a real time encoding scheme 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. The encoding scheme compresses bio-signals (ECG signals), while still maintaining the clinically important features. We optimize various process parameters to model a low power hardware prototype for the implementation of our algorithm on a real time microcontroller based IoT platform that operates as an end-to-end WBSN system in real time. Experimental results show a system-level energy improvement of 96% with a negligible impact on signal quality (2%).
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.
Abstract: Over the years, researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years systematically. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG, beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations for future research directions.
••01 Nov 2018
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.
Abstract: An electrocardiogram (ECG) is an electrical record of heart activity. ECG compression is the biggest concern for many applications in the biomedical community. ECG lossless compression (ECG LC) is data recovery for diagnostic and analysis purposes. The aim here develops an efficient algorithm ECG LC. This algorithm uses the transform based on wavelet followed by the arithmetic coding (AC) on the residual. The parameters of performance measurement for the ECG signal such as CR (Compression Ratio), PRD (Percent Root mean square Distortion). The proposed algorithm achieves high compression ratios compared to other compressing algorithms. Outcomes display that this algorithm works well for various kinds of patient recordings and is even able to provide lossless compression for event-related potentials. According the outcomes, the higher CR. The highest CR is obtained is 75.5, and the lowest PRD is obtained is 0.18 according to the patient records that have the highest CR.
01 Nov 2019
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.
Abstract: In order to ensure the safe and reliable operation of the aircraft, improve the efficiency of aviation engine maintenance and improve the prediction accuracy of the remaining life of the aeroengine, a prediction method of the remaining life of the aeroengine based on the stacked sparse self- coding neural network is proposed. 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 stack self-encoding learning. model. The BP residual neural network is used to classify the remaining life of the engine as a result of engine residual life prediction. Finally, the algorithm is validated by the PHM2008 aeroengine degradation data. The results show that the method can effectively predict the remaining life of the aeroengine.
••23 Sep 2020
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.
Abstract: The Internet of Things (IoT) healthcare framework is a new trend. In this context, biomedical wearable devices are linked to the cloud. This work contributes to the development of efficient Electrocardiogram (ECG) wearables by redesigning their signal processing chain. The emphasis is on developing a system for effective and precise QRS selection. QRS complexes of heartbeats contain most important arrhythmia related information. The proposed system uses an analog band-pass filter to reduce the impact of Baseline Wander (BW) and aliasing. The band-limited signal is then digitized by using a 5-Bit resolution level-crossing A/D converter. Onward, an original activity selection algorithm is used for an effective selection of the QRS complexes. Results show that the proposed solution attains an average QRS detection sensitivity of 98.4% and positive predictive value of 100% while securing on average 4.77-fold and 2.1-fold compression gains respectively in terms of count of data samples and bits over the classical counterparts.
01 Jan 1987
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.
Abstract: List of symbols and acronyms List of iterative algorithm templates List of direct algorithms List of figures List of tables 1: Introduction 2: A brief tour of Eigenproblems 3: An introduction to iterative projection methods 4: Hermitian Eigenvalue problems 5: Generalized Hermitian Eigenvalue problems 6: Singular Value Decomposition 7: Non-Hermitian Eigenvalue problems 8: Generalized Non-Hermitian Eigenvalue problems 9: Nonlinear Eigenvalue problems 10: Common issues 11: Preconditioning techniques Appendix: of things not treated Bibliography Index .
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.
Abstract: Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify 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. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.
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.
Abstract: Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. 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 ECG's are clinically useful.
••22 May 2011
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.
Abstract: Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals that enables sampling rates significantly below the classical Nyquist rate. Based on the fact that electrocardiogram (ECG) signals can be approximated by a linear combination of a few coefficients taken from a Wavelet basis, we propose a compressed sensing-based approach for ECG signal compression. ECG signals generally show redundancy between adjacent heartbeats due to its quasi-periodic structure. We show that this redundancy implies a high fraction of common support between consecutive heartbeats. The contribution of this paper lies in the use of distributed compressed sensing to exploit the common support between samples of jointly sparse adjacent beats. Simulation results suggest that compressed sensing should be considered as a plausible methodology for ECG compression.
26 May 2013
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.
Abstract: In this paper, we investigate the performance of compressive sampling (CS) for ECG compression in telecardiology, when the signal acquisition is noisy and unavoidable body movements lead to varying heartbeat rate and sparsity of the signal. We show analytically that CS recovery noise does not scale linearly with the input noise. Hence, it is not easy to reduce the adverse impact of noise in CS. Additionally, any variation in the heartbeat rate changes the sparsity and can adversely affect compression. We compare the performance of CS with thresholding discrete wavelet transform (TH-DWT), which is the best technique for real-time ECG compression. We show that CS is quite sensitive to sparsity and compression ratio, while the reconstruction quality of TH-DWT is quite stable. Our results suggest that while CS is an attractive option for telecardiology due to its encoder simplicity, caution should be exercised in applying it for ECG signal compression.
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