Multi-channel ECG data compression using compressed sensing in eigenspace

Singh, Anurag; Sharma, L. N.; Dandapat, Samarendra

doi:10.1016/j.compbiomed.2016.03.021

Cited by 23 publications

(6 citation statements)

References 38 publications

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“…The time domain sampling of signal is transformed into its DFT frequency domain sampling [West, Harwell and McCall (2017)]. Principal Component Analysis (PCA) can be used to deal with the data of high dimension, noise and high correlation by projecting the data into the low dimensional space and the most possible features of the original data [Singh, Sharma and Dandapat (2016)]. In compressive sensing, many complex signals in real life can expressed in a more concise way, or can be more succinctly expressed under some orthogonal basis transforms.…”

Section: Sparse Representationmentioning

confidence: 99%

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

Sun¹,

Yuan

Wang³

et al. 2019

Computers, Materials &Amp; Continua

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The signal processing problem has become increasingly complex and demand high acquisition system, this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal. The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals. According to the principles of the compressive sensing and signal processing method, non-sparse ultrasonic signals are converted to sparse signals by using sparse transform. The sparse coefficients are obtained by sparse decomposition of the original signal, and then the observation matrix is constructed according to the corresponding sparse coefficients. Finally, the original signal is reconstructed by using basis pursuit algorithm, and error analysis is carried on. Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

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Section: Sparse Representationmentioning

confidence: 99%

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

Sun¹,

Yuan

Wang³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Sparse signals can be represented as a blend of a small number of projections on a certain basis (That must be incoherent to the original basis). So the same signal can be represented with a lesser amount of data because of sparsity, still permitting the precise reconstruction [10]. One would obtain a large amount of data in uncompressed sensing techniques, calculates a suitable basis and projections on it and then transfer these projections and the basis used.…”

Section: Compressive Sensing Of Ecg Signalsmentioning

confidence: 99%

A Survey on different Compression Techniques for ECG Data Reduction

Sakshi¹,

Neeru²

2017

IJCA

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Electrocardiogram (ECG) is the technique that is used to record the electrical signal of the heart over a time interval by using the electrodes, positioned on a patient's body. The signals collected from the body needs to be processed and compressed before directing to monitoring center. Electrocardiogram (ECG) data compressions minimize the necessities of storage to generate a more proficient telecardiology system for the cardiac exploration and diagnosis. This paper focus on the evaluation of several compression schemes for ECG data compression and also provides the comparison of the various ECG compression techniques such as Turning Point, Delta Coding, AZTEC, CORTES, DCT etc. in terms of different performance metrics like Compression Ratio (CR), Percent Mean Square Difference (PRD) and Quality Score (QS).

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“…These are usually linked to the cloud, and their data are logged and further processed via cloud-based applications. The decision support outcome is then shared with a monitoring center [17][18][19][20]. Such an approach allows healthcare specialists to make timely decisions and offer emergency care for patients with chronic disorders.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Diagnosis by Using Level-Crossing ECG Sampling and Sub-Bands Features Extraction for Mobile Healthcare

Qaisar

Hussain

2020

Sensors

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Mobile healthcare is an emerging technique for clinical applications. It is usually based on cloud-connected biomedical implants. In this context, a novel solution is presented for the detection of arrhythmia by using electrocardiogram (ECG) signals. The aim is to achieve an effective solution by using real-time compression, efficient signal processing, and data transmission. The system utilizes level-crossing-based ECG signal sampling, adaptive-rate denoising, and wavelet-based sub-band decomposition. Statistical features are extracted from the sub-bands and used for automated arrhythmia classification. The performance of the system was studied by using five classes of arrhythmia, obtained from the MIT-BIH dataset. Experimental results showed a three-fold decrease in the number of collected samples compared to conventional counterparts. This resulted in a significant reduction of the computational cost of the post denoising, features extraction, and classification. Moreover, a seven-fold reduction was achieved in the amount of data that needed to be transmitted to the cloud. This resulted in a notable reduction in the transmitter power consumption, bandwidth usage, and cloud application processing load. Finally, the performance of the system was also assessed in terms of the arrhythmia classification, achieving an accuracy of 97%.

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Multi-channel ECG data compression using compressed sensing in eigenspace

Cited by 23 publications

References 38 publications

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

A Survey on different Compression Techniques for ECG Data Reduction

Arrhythmia Diagnosis by Using Level-Crossing ECG Sampling and Sub-Bands Features Extraction for Mobile Healthcare

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