Shannon’s Energy Based Algorithm in ECG Signal Processing

Beyramienanlou, Hamed; Lotfivand, Nasser

doi:10.1155/2017/8081361

Cited by 46 publications

(19 citation statements)

References 28 publications

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“…Sensitivity (Se), positive predictability (+P), detection error rate (DER), and accuracy (Acc) can be computed by using TP, FN, and FP by using the following equations, respectively, as widely used in the literatures [1, 2, 4, 13, 24–26]:

\begin{matrix} S e = \frac{T P}{T P + F N} \times 100 %, \\ + P = \frac{T P}{T P + F P} \times 100 %, \\ D E R = \frac{F P + F N}{T P} \times 100 %, \\ A c c = \frac{T P}{T P + F P + F N} \times 100 %, \end{matrix}

where TP are the correctly detected beats (true positive), FP are falsely detected beats (false positive), and FN are the undetected beats (false negative). The sensitivity is used for evaluating the ability of the algorithm to detect true beats, the positive predictability is used for evaluating the ability of the algorithm to discriminate between true and false beats, and the error rate is used for evaluating the accuracy of the algorithm [25]. …”

Section: Resultsmentioning

confidence: 99%

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

Park

Lee

Park

2017

Journal of Healthcare Engineering

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Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

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\begin{matrix} S e = \frac{T P}{T P + F N} \times 100 %, \\ + P = \frac{T P}{T P + F P} \times 100 %, \\ D E R = \frac{F P + F N}{T P} \times 100 %, \\ A c c = \frac{T P}{T P + F P + F N} \times 100 %, \end{matrix}

Section: Resultsmentioning

confidence: 99%

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

Park

Lee

Park

2017

Journal of Healthcare Engineering

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“…To increase the small amplitude, square root is used:

Here MA is the moving average obtained from the previous step. To decrease the baseline signal's value below zero, the following formula is used [ 16 ]:

where σ is the standard deviation and μ is defined as signal average. The process of peak detection includes the following step:

where baseline (0) is the threshold level for peak detection and R peaks are found in an ECG signal by searching the maximum peak within ±50 samples (length of window = min (RR interval)) of the recognized location of the candidate R peak in the previous step (Equation ( 9 )).…”

Section: Methodsmentioning

confidence: 99%

“…To decrease the baseline signal's value below zero, the following formula is used [ 16 ]:

where σ is the standard deviation and μ is defined as signal average.…”

Section: Methodsmentioning

confidence: 99%

“…Shannon energy computes the average signal energy in a signal spectrum. In other words, it reduces the high intensity to balance out with the low intensity [ 16 – 18 ]. The Adaptive Double Threshold Filter (ADTF) and Discrete Wavelet Transform (DWT) are used to reduce the noise in the ECG signal to improve the ECG signal filtering [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Teager Energy Operator-Based Automated QRS Complex Detection

Beyramienanlou

Lotfivand

2018

Journal of Healthcare Engineering

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Database The efficiency and robustness of the proposed method has been tested on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), and MIT-BIH Normal Sinus Rhythm Database (MIT-NSD). Aim Because of the importance of QRS complex in the diagnosis of cardiovascular diseases, improvement in accuracy of its measurement has been set as a target. The present study provides an algorithm for automatic detection of QRS complex on the ECG signal, with the benefit of energy and reduced impact of noise on the ECG signal. Method The method is basically based on the Teager energy operator (TEO), which facilitates the detection of the baseline threshold and extracts QRS complex from the ECG signal. Results The testing of the undertaken method on the Fanatasia Database showed the following results: sensitivity (Se) = 99.971%, positive prediction (P+) = 99.973%, detection error rate (DER) = 0.056%, and accuracy (Acc) = 99.944%. On MIT-AD involvement, Se = 99.74%, P+ = 99.97%, DER = 0.291%, and Acc = 99.71%. On MIT-NSD involvement, Se = 99.878%, P+ = 99.989%, DER = 0.134%, and Acc = 99.867%. Conclusion Despite the closeness of the recorded peaks which inflicts a constraint in detection of the two consecutive QRS complexes, the proposed method, by applying 4 simple and quick steps, has effectively and reliably detected the QRS complexes which make it suitable for practical purposes and applications.

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“…2c, the EEMD reconstruction successfully eliminates the baseline drift in this example. This achievement is vital because the baseline drift occurs frequently in medical data or data containing Gaussian white noise [61,62]. Furthermore, the most important finding in the model study is that the MHHS (Fig.…”

Section: Model Studymentioning

confidence: 99%