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- Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification?

SJR uses a similar algorithm as the Google page rank; it provides a quantitative and qualitative measure of the journal's impact. SNIP measures contextual citation impact by wighting citations based on the total number of citations in a subject field. This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection.

In , around Thereby, cardiac health research has acquired significative importance for medical researchers, mainly for those focused on technological, preventive and medical advances. Accordingly, traditional technologies for cardiovascular-diagnosis used at home, clinics and hospitals have been of main interest for researchers in order to improve them.. Electrocardiogram ECG analysis is the most common clinical cardiac examination, which is a useful detection tool for several cardiac abnormalities, mainly because it is inexpensive, simple and risk-free Dilaveris et al.

Hence, ECG analysis has been widely investigated during the last two decades. Mostly, because an ECG signal records a vital sign for heart functional investigation because it represents the electrophysiological events that coincide with the sequence of depolarization and repolarization of the atria and ventricles Elgendi et al. Each event contains its own peak, making this important to analyze their morphology, amplitude, and duration for cardiac arrhythmias detection Bashour et al. Also, their analysis can be critical for detecting breathing disorders such as obstructive sleep apnea syndrome Trinder et al.

Other functional or structural cardiac disorders can be monitored too.. Nevertheless, this is a non-easy task since a real ECG signal usually faces muscular noise, motion artifacts, and baseline drifts changes Benitez et al. This increases the complexity of QRS detection Benitez et al. In some researches, this stage is described as pre-processing or feature extraction. One of these techniques is the amplitude threshold which has been used by Morizet-Mahoudeaux et al.

With this technique, the signal noise is not properly removed and it is usually followed by the first derivative of the ECG signal Morizet-Mahoudeaux et al. Digital filters have been applied by other authors for QRS enhancement. The applied digital filter can increase the SNR ratio; it depends on the order of the filter and its nature.

In Pan and Tompkins , authors applied a bandpass filter to an ECG signal followed by its first derivative, and threshold. With the mathematical morphology algorithms the signal noise is partially addressed. In Tang et al. However, the Hilbert transform does not improve the SNR; hence it is common for investigators to filter the signal before applying the Hilbert transform. Other technique applied for QRS enhancement is the filter banks, which significantly improve the SNR for Gaussian noise and for muscle noise in comparison with the median or mean averaging methods Afonso et al.

In Afonso et al. Furthermore, the wavelet transform technique has also been used by other related work. Several techniques have been applied in the literature for QRS detection. Matched filters have been applied to ECG signal by Kaplan In Liang-Yu et al. It is important to note that neural networks are highly sensitive to noise Clifford et al.

However, the singularity method is sensitive to noise Ayat et al. In addition, zero-crossing technique has been applied in the literature for QRS complex detection. According to related work, methods based on Hilbert transform have the ability to discriminate the dominant peaks from other peaks. These methods have been capable to improve the results for R-wave detection.

Normally, a threshold is needed for the detection of the R-wave in an electrocardiogram signal; a fixed threshold for detecting R-waves can be efficient and simple for ECG signals with normal beat morphology Elgendi et al. However, several researchers have reported that ECG signal waveforms may vary drastically from each other, due to movement of patients, or severe baseline drifting. Accordingly to this, there is a high probability that QRS complexes may be missed.

Otherwise, adaptive thresholding has been proven to reduce the probability of missing QRS complex detection Elgendi et al. Usually, adaptive thresholding makes empirical use of many thresholds.

## Rule Based Identification of Cardiac Arrhythmias from Enhanced ECG Signals Using Multi-Scale PCA

In Li et al. Kadambe et al. Their algorithm is based on wavelet transform too. In Burte and Ghongade , and Xu and Liu , authors have shown that adaptive thresholding provides interesting results for R wave peak detection. In their case, the thresholds have been detected automatically.. This paper is focused on the analysis of ECG signals by applying the Hilbert transform and the adaptive threshold technique to detect the real R-peaks from an ECG signal.

In addition, the application of the PCA for feature extraction from electrocardiogram signals is presented as well. Feature extraction is applied to three types of heartbeats normal heartbeats, premature ventricular contraction, and atrial premature contraction. Obtained results show that the performance of the proposed method reported a sensitivity of The rest of the paper is organized as follows: section 2 presents a brief description of the methodology used for QRS complex detection from an ECG, including the band pass filter, the first derivative differentiation, the use of the Hilbert transform, and the adaptive threshold technique.

Section 3 discusses PCA for extracting the feature vector. Section 4 shows the obtained results after applying the proposed methodology. Finally, section 5 presents conclusions and future work.. Electrocardiogram signal is one of the most important biological signals used to diagnose heart diseases. ECG signals allow the representation of the cyclical contraction and relaxation of human heart muscles. Heart muscle activity is controlled by electrical pulses which are transmitted through a nerve network; such electrical pulses are strong enough to be sensed by electrodes placed on the human skin Asirvadam et al.

Sometimes a U-wave may also be present after the T-wave Elgendi et al. The QRS complex represents the depolarization of heart ventricles which have greater muscle mass and hence its consumption of electrical activity is higher. The detection of R waves is easier than in other ECG signal wave detection due to its high amplitude and its structural form.

There are some difficulties in QRS complex detection. These difficulties can be summarized as follows: a presence of non-stationarity, i. Figure 2 shows an R-peak with negative polarity. This can happen when some extrasystoles lead to a sudden polarity change. However, an algorithm for detecting QRS with R-peaks of both positive and negative polarity is desired.. Figure 3 shows an R-peak with low amplitude. Therefore, ECG signals may vary drastically from one heartbeat to the next due to the movement of the patients and to severe baseline drifting, as depicted in Figure 4.

Accordingly, it can also be noticed that a big fixed threshold can lead to missing detections.

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Moreover, a small fixed threshold can easily lead to inaccurate detections. The fixed threshold might also affect the detection of T and P waves. In another case, an adaptive threshold algorithm mainly implements multiple thresholds empirically, decreasing the possibility of missing QRS complexes.. Our proposed method for QRS complex detection is based on combining both Hilbert transform and the adaptive threshold technique.

The steps of this method are explained next.. The first stage of our proposed method is the ECG filtering.

## Analysis of Intracardiac ECG Measured in the Coronary Sinus

Firstly, the band pass filter is applied to maximize the QRS complex, and also for removing muscular noise from the ECG signal. A 6 th order band-pass Butterworth filter has been applied. The band stop frequencies were set from 5 to 15 Hz. The 5 Hz is the starting frequency and 15 is the stopping frequency. The first derivative is applied to indicate the minimum slope of the ECG signal i. Also, the first derivative indicates the high slope points i. The first derivative differentiation using 2-point central difference is calculated using Eq.

Initial conditions are set to minimize the error at the boundaries, i. The derivative output of the filtered ECG signal allows removing baseline drifts and motion artifacts.. For a discrete time series y k , the Hilbert transform is defined as in Eq. Therefore, the analytic signal z k is given in Eq. It is also considered as the pre-envelope of the original signal y k. The envelope a k of y k is described in Eq. It is also considered as the instantaneous magnitude of z k.

Adaptive threshold is a technique carried out for detecting the R wave peak. This technique is performed by using a pair of threshold limits called upper limited threshold u th and lower limited threshold l th.. The upper threshold is defined by Eq. The lower threshold is defined by Eq. The threshold values are updated in iteration time, where the number of detected peaks above the l th threshold is obtained, and also, the number of detected peaks above u th is calculated.

The thresholds are updated per iteration, meanwhile the number of detected peaks by the up and down limits is different. The value of u th is updated using Eq. The value of is l th updated by using Eq. This process continues until the same QRS number i. The PCA is a technique for linear dimensionality reduction that provides projection of the data in the direction of the highest variance Monasterio et al. This technique is carried out to extract relevant features from the ECG data set.

The signal segment of a heartbeat is represented by y k , as in Eq. Thus, the heartbeats y 1 , y 2… , y N are N observations of heartbeats —as in Eq. The PCA consists of the following steps:. Calculate the mean vector. The mean vector of each heartbeat is calculated as in Eq.

### Automatic ECG Analysis using Principal Component Analysis and Wavelet Transformation

Compute the mean adjusted data —see Eqs. Compute the covariance matrix, as shown in Eq. Calculate the eigenvectors and eigenvalues of the covariance matrix. Choosing components and forming a feature vector. The eigenvector with the highest value is the principal component. Then, the eigenvectors are ordered by eigenvalues from highest to lowest, which returns the components in order of significance. Subsecuently, the dimensionality is reduced by selecting K -principal components that retain the physiological information.

Thus, the percentage of variance, r k, of each eigenvalue is obtained by applying Eq. Furthermore, we select the principal components whose percentage of variance is higher than the percentage threshold,, that is 0. Deriving the new data set. The final dataset is obtained by Eq. Each record contains a duration of 30 min with 5.

For QRS detection, only the first channel of each record has been considered. A total of 19 records have been considered. The sensitivity parameter Se indicates the percentage of heartbeats that were correctly detected by the algorithm. Table 1 presents the results of the adaptive threshold method applied to the nineteen records extracted from the MIT-BIH arrhythmia database. The algorithm detects the R-wave that is even very close to the end of the record, e. The method achieved fairly good results also for very noisy records, e. New articles by this author. New citations to this author.

New articles related to this author's research. Email address for updates. My profile My library Metrics Alerts. Sign in. Get my own profile Cited by All Since Citations h-index 28 26 iindex 42 Andrea Petznick Alcon Laboratories Verified email at alcon. Chandan Chakraborty Associate Professor. Articles Cited by Co-authors. Biomedical Signal Processing and Control 8 5 , , Expert Systems with Applications 39 14 , — , However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

Alfarhan et al. Request Permissions. Ge, N. Srinivasas, and S. Abibullaev, W. Kang, S. Rabee and I. Yu and Y. Chen, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network, Pattern Recognit. Polat and S. Gunes, Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine, Appl. Kaur, B. Raghavendra, D. Bera, A. Bopardikar, and R. Tawfik, H. Selim, and T.

Chouhan and S. Xiao, C. Wei, and G. Khorrami and M. Tamil, N.

Kamarudin, R. Salleh, M. Idris, M. Noor, and A. Kania, M. Fereniec, and R. Gargour, M. Gabrea, V. Ramachandran, and J. Martis, U. Acharya, K. Mandana, A.

## CN104305992B - An interactive fetal ECG quick automatic extraction method - Google Patents

Ray, and C. Chakraborty, Application of principal component analysis to ECG signals for automated diagnosis of cardiac health, Expert Syst. Jolliffe, Principal Component Analysis, Ghodsi, Dimensionality reduction a short tutorial. Waterloo, Ontario: Univ.