Assessment of ECG Signal Quality After Compression

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

BioMed Research International ◽

10.1155/2018/1868519 ◽

2018 ◽

Vol 2018 ◽

pp. 1-26 ◽

Cited By ~ 13

Author(s):

Andrea Němcová ◽

Radovan Smíšek ◽

Lucie Maršánová ◽

Lukáš Smital ◽

Martin Vítek

Keyword(s):

Comparative Analysis ◽

Quality Assessment ◽

Quality Evaluation ◽

Signal Transmission ◽

Signal Quality ◽

Ecg Signal ◽

Extensive Review ◽

Compression Process ◽

Compression Efficiency ◽

Compression Performance

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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Assessment of ECG Signal Quality

Learning and Analytics in Intelligent Systems - Advances in Decision Sciences, Image Processing, Security and Computer Vision ◽

10.1007/978-3-030-24322-7_2 ◽

2019 ◽

pp. 9-15

Author(s):

Shashikant ◽

P. Chetankumar

Keyword(s):

Signal Quality ◽

Ecg Signal

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Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality

Journal of Electrical and Computer Engineering ◽

10.1155/2015/563915 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Yatao Zhang ◽

Shoushui Wei ◽

Yutao Long ◽

Chengyu Liu

Keyword(s):

Signal To Noise Ratio ◽

Stress Test ◽

Low Frequency ◽

Scale Factor ◽

Multiscale Entropy ◽

Signal Quality ◽

Ecg Signal ◽

The Real ◽

Ecg Signals ◽

Test Database

This study explored the performance of multiscale entropy (MSE) for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF) noise, low frequency (LF) noise, and power-line (PL) noise, was analyzed. The sensitivity of MSE to the signal to noise ratio (SNR) of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could reflect content level of various noises contained in the ECG signals. For the synthetic ECG plus LF noise, the MSE was sensitive to SNR within higher range of scale factor. However, for the synthetic ECG plus HF noise, the MSE was sensitive to SNR within lower range of scale factor. Thus, a recommended scale factor range within 5 to 10 was given. Finally, the results were verified on the real ECG signals, which were derived from MIT-BIH Arrhythmia Database and Noise Stress Test Database. In all, MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.

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