COMPRESSED SENSING SYSTEM FOR EFFICIENT ECG SIGNAL COMPRESSION (original) (raw)
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Compressed sensing based method for ECG compression
2011
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 sup port between samples of jointly sparse adjacent beats. Simulation results suggest that compressed sensing should be considered as a plausible methodology for ECG compression.
Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems
IEEE Journal of Biomedical and Health Informatics, 2014
Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.
International Journal of Distributed Sensor Networks, 2019
In the past decades, compressed sensing emerges as a promising technique for signal acquisition in low-cost sensor networks. For prolonging the monitoring duration of biosignals, compressed sensing is also exploited for simultaneous sampling and compression of electrocardiogram signals in the wireless body sensor network. This article presents a comprehensive analysis of compressed sensing for electrocardiogram acquisition. The performances of involved important factors, such as wavelet basis, overcomplete dictionaries, and the reconstruction algorithms, are comparatively illustrated, with the purpose to give data reference for practical applications. Drawn from a bulk of comparative experiments, the potential of compressed sensing in electrocardiogram acquisition is evaluated in different compression levels, while preferred sparsifying basis and reconstruction algorithm are also suggested. Relative perspectives and discussions are also given.
Ecg Compressed Sensing Based on Classification in Compressed Space and Specified Dictionaries
2011
An electrocardiographic signal (ECG) compressed sensing (CS) method, its reconstruction using specific dictionaries of cardiac pathologies and method evaluation testing using classical measures as well as by classification error of the reconstructed patterns based on the K-Nearest Neighbour classifier (KNN) are presented. For compressed sensing, a random matrix with standard normal distribution was used, followed by a classification of compressed signals in one of eight possible pathological classes. For each class a specific dictionary was created, and the signals were reconstructed using the Basis Pursuit algorithm according to the result of the classification.
A real-time compressed sensing-based personal electrocardiogram monitoring system
2011 Design, Automation & Test in Europe, 2011
Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric mobilecardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. 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 significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we propose a novel real-time energy-aware ECG monitoring system based on the emerging compressed sensing (CS) signal acquisition/compression paradigm for WBSN applications. For the first time, CS is demonstrated as an advantageous real-time and energy-efficient ECG compression technique, with a computationally light ECG encoder on the state-of-the-art Shimmer TM wearable sensor node and a realtime decoder running on an iPhone (acting as a WBSN coordinator). Interestingly, our results show an average CPU usage of less than 5% on the node, and of less than 30% on the iPhone.
Abnormal ECG signal detection based on compressed sampling in Wearable ECG sensor
2011 International Conference on Wireless Communications and Signal Processing (WCSP), 2011
Nowadays to diagnose cardiac arrhythmias Holter device is used to record 1 or 2 ECG leads during 24 or 48h. Power consumption limitations determine that the amount of data needs to be diminished without damaging the quality of information. To get a solution, we introduce a novel method based on Compressed Sensing (CS) technique to the Wearable ECG sensor (WES). The main principle underlying this framework is to sample analog signals at sub-Nyquist rate at the analog-digital converters (ADCs) and to classify directly compressed measurement into normal and abnormal state. Those compressed measurements which imply a risk of cardiac anomaly will be stored in a multimedia flash memory card or be transferred to the terminal of the network for a cardiologist to make an off-line diagnosis of cardiac arrhythmias using the reconstructed signals from the compressed measurements. In this paper we propose a scheme to directly classify compressed ECG samples into normal or abnormal states, thus avoiding reconstruction of the entire signal to perform this task. Our algorithm takes advantage of estimating parameters directly from the compressed measurements; thereby eliminating the reconstruct stage and reducing the computational complexity in WES. Direct cardiac arrhythmia detection based on CS reduces 34% energy consumption and 90% storage in WES for the reconstructed performance of 41dB.
Implementation of compressed sensing in telecardiology sensor networks
International Journal of Telemedicine and Applications, 2010
Mobile solutions for patient cardiac monitoring are viewed with growing interest, and improvements on current implementations are frequently reported, with wireless, and in particular, wearable devices promising to achieve ubiquity. However, due to unavoidable power consumption limitations, the amount of data acquired, processed, and transmitted needs to be diminished, which is counterproductive, regarding the quality of the information produced. Compressed sensing implementation in wireless sensor networks (WSNs) promises to bring gains not only in power savings to the devices, but also with minor impact in signal quality. Several cardiac signals have a sparse representation in some wavelet transformations. The compressed sensing paradigm states that signals can be recovered from a few projections into another basis, incoherent with the first. This paper evaluates the compressed sensing paradigm impact in a cardiac monitoring WSN, discussing the implications in data reliability, energy management, and the improvements accomplished by in-network processing.
A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?
IEEE Signal Processing Letters, 2015
When transmission or storage costs are an issue, lossy data compression enters the processing chain of resourceconstrained sensor nodes. However, their limited computational power imposes the use of encoding strategies based on a small number of digital computations. In this case study, we propose the use of an embodiment of compressed sensing as a lossy digital signal compression, whose encoding stage only requires a number of fixed-point accumulations that is linear in the dimension of the encoded signal. We support this design with some evidence that for the task of compressing ECG signals, the simplicity of this scheme is well-balanced by its achieved code rates when its performances are compared against those of conventional signal compression techniques.
Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors
IEEE Transactions on Biomedical Circuits and Systems, 2000
Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.
ECG signal compression using compressive sensing and wavelet transform
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
Compressed Sensing (CS) is a novel approach of reconstructing a sparse signal much below the significant Nyquist rate of sampling. Due to the fact that ECG signals can be well approximated by the few linear combinations of wavelet basis, this work introduces a comparison of the reconstructed 10 ECG signals based on different wavelet families, by evaluating the performance measures as MSE (Mean Square Error), PSNR (Peak Signal To Noise Ratio), PRD (Percentage Root Mean Square Difference) and CoC (Correlation Coefficient). Reconstruction of the ECG signal is a linear optimization process which considers the sparsity in the wavelet domain. L1 minimization is used as the recovery algorithm. The reconstruction results are comprehensively analyzed for three compression ratios, i.e. 2∶1, 4∶1, and 6∶1. The results indicate that reverse biorthogonal wavelet family can give better results for all CRs compared to other families.