A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal

Malleswari, P. Naga; Bindu, Ch. Hima; Prasad, K. Satya

doi:10.1007/s12652-021-03268-9

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…To confirm the effectiveness of the proposed algorithm, we faced results of our strategy to those of efficiency-proven algorithms [16,17]. concerning the first one [16] and from Tables 3 and 4 one can report nearly same accuracy results, however the average speed-up ratio of our method 4.23:1 makes it evidently, an accelerated version of the faced technique. Under the same optic, from results shown in Tables 5 and 6, it can be seen that obtained average accuracy is same to the one of [17].…”

Section: Resultsmentioning

confidence: 76%

“…Accordingly, it shows the mean computational time and corresponding standard deviation when repeating the test for each ECG record 10 times and the sensitivity (Se) and the accuracy (Ac) which are defined by the following formulas [16]:…”

Section: Resultsmentioning

confidence: 99%

“…In this section the used mathematical tools to improve the original works described in [8,16,17] are presented:…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…Evaluation and comparison between the conventional algorithm[16] and the suggested accelerated algorithm.…”

mentioning

confidence: 99%

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Accelerated Variant of an EMD-Based-R Peaks Detection Algorithm Involving FFT-Based Time-Domain Down-Sampling and up-Sampling

Harkat,

Benzid,

Athamena

2023

JBBBE

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As most of developed empirical mode decomposition (EMD) based R-peaks detection algorithms consume a considerable time of calculation caused by the large length of the input ECG signal, the design of a new technique that allows the acceleration of such methods becomes necessary. Accordingly, a new variant of an EMD-based strategy for R-peaks localization is presented. The new accelerated variant is constituted of three essential parts. The first step is the length reduction of the input signal by means of the truncation in the Fast Fourier Transform (FFT) domain followed by the application of the inverse FFT guaranteeing a suitable time-domain down-sampling. Consequently, the new input signal of a reduced length preserves all medical information contained initially in the original lengthy signal. The second part is dedicated to identify the QRS complex using EMD-based R-peaks detection. This latter comprises a low-pass filter, Empirical Mode Decomposition (EMD) and the Hilbert transform, Finally, the third phase is the time-domain up-sampling using the FFT, the zero padding and the Inverse Fast Fourier Transform (IFFT) to obtain a resulting processed signal which has the same length as the original signal. Next, as a post-processing step, final R-peaks refined localization is achieved. It is noticeable that the new variant ensures same results, in term of accuracy, as the standard method; however, a significant speed-up ratio of 6.95:1 is reported. Additionally, to more prove the effectiveness of the suggested strategy, it has been applied to accelerate two other efficient algorithms and satisfactory speed up ratios of, 7.20:1 and 4.23:1, respectively have been reached.

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Section: Resultsmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 99%

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Accelerated Variant of an EMD-Based-R Peaks Detection Algorithm Involving FFT-Based Time-Domain Down-Sampling and up-Sampling

Harkat,

Benzid,

Athamena

2023

JBBBE

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“…There are some previous studies related to this issue. Malleswari et al [24] proposed the Hybrid Empirical Mode Decomposition-Discrete Wavelet Transform (EMD-DWT) based algorithm for detecting QRS complex in electrocardiogram signals which detect cardiac abnormalities. This algorithm improved the accuracy of QRS detection compared to state-of-art techniques.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction and Infinite Feature Selection based Diagnosis for Cardiovascular Disease Related to Smoking Habit

Banu¹,

Vanjerkhede²

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Electrocardiography (ECG) is a growing study in the realm of patient monitoring systems to detect cardiovascular disease (CVD) by smoking habits. This study investigated the categorization and analysis of CVD related to smoking habits using the ECG dataset from the Physikalisch-Technische Bundesanstalt (PTB). After acquiring ECG data, the feature vectors were extracted using hybrid feature extraction (a mix of statistical, energy, and entropy characteristics). To extract features from obtained ECG signals, nineteen characteristics were merged. Artifacts in the signal are being reduced by using a zero phase butterworth filter, and the peak identification of ECG signal is attained by using the Pom-Tompkins method. Then, infinite feature selection was used to delete unnecessary characteristics or choose the best feature subsets. After choosing the best characteristics, the ECG signals of smokers and non-smokers are classified using a supervised classifier (K-Nearest Neighbor (KNN)). KNN classifier has the advantage of balancing the data for the classification of smoker and non-smokers. This discovery has several benefits, including earlier detection of cardiovascular disorders and great assistance to physicians during surgery. The results of the experiment are evaluated using classification Accuracy, F-Score, Specificity, Sensitivity, and Mathews Correlation coefficient (MCC) for the proposed technique, and the process efficiently discriminated the ECG signals of smokers from non-smokers in comparison to the previous methods; the suggested strategy improved accuracy by 3-40%.

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A review of different ECG classification/detection techniques for improved medical applications

Gupta¹,

Saxena²,

Kanungo³

et al. 2022

Int J Syst Assur Eng Manag

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A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal

Cited by 10 publications

References 24 publications

Accelerated Variant of an EMD-Based-R Peaks Detection Algorithm Involving FFT-Based Time-Domain Down-Sampling and up-Sampling

Accelerated Variant of an EMD-Based-R Peaks Detection Algorithm Involving FFT-Based Time-Domain Down-Sampling and up-Sampling

Hybrid Feature Extraction and Infinite Feature Selection based Diagnosis for Cardiovascular Disease Related to Smoking Habit

A review of different ECG classification/detection techniques for improved medical applications

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