Evaluation of QRS Complex Based on DWT Coefficients Analysis Using Daubechies Wavelets for Detection of Myocardial Ischaemia

Patil, Geeta; Rao, K. Sambasiva; Niranjan, U. C.; Satyanarayan, K.

doi:10.1142/s0219519410003356

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…Choosing a wavelet family, which closely matches the signal to be processed, plays an important role in wavelet applications. 27 As shown in Fig. 2 , the Daubechies wavelet family is similar in shape to the QRS complex.…”

Section: Methodsmentioning

confidence: 84%

The effect of traditional Persian music on the cardiac functioning of young Iranian women

Abedi

Abbasi

Goshvarpour

et al. 2017

Indian Heart Journal

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In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are not similar. Therefore, in the present study, we have sought to examine the effects of traditional Persian music on the cardiac function in young women. Twenty-two healthy females participated in this study. ECG signals were recorded in two conditions: rest and music. For each of the 21 ECG signals (15 morphological and six wavelet based feature) features were extracted. SVM classifier was used for the classification of ECG signals during and before the music. The results showed that the mean of heart rate, the mean amplitude of R-wave, T-wave, and P-wave decreased in response to music. Time-frequency analysis revealed that the mean of the absolute values of the detail coefficients at higher scales increased during rest. The overall accuracy of 91.6% was achieved using polynomial kernel and RBF kernel. Using linear kernel, the best result (with the accuracy rate of 100%) was attained.

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Section: Methodsmentioning

confidence: 84%

The effect of traditional Persian music on the cardiac functioning of young Iranian women

Abedi

Abbasi

Goshvarpour

et al. 2017

Indian Heart Journal

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“…(TemplateM, 106CWT and DedicatedW uses the ECG signal as a template or a mother wavelet; some researchers use the db6 wavelet due to its similarity to the ECG morphology [24,25]. ).…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

“…As shown in Table 3 , the FDRs are large when the templates or the mother wavelets are similar to the ECG morphology. (TemplateM, 106CWT and DedicatedW uses the ECG signal as a template or a mother wavelet; some researchers use the db6 wavelet due to its similarity to the ECG morphology [ 24 , 25 ].). Specifically, TemplateM and DedicatedW, which use the ECG of each subject as a template, show the highest performance.…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

2011

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BackgroundNumerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia.MethodsIn this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm.ResultsA performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%.ConclusionsThe proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

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“…Unlike other approaches, which detect R-peaks first and then determine the QRS-complexes by the R-peaks, this method extracts the QRS region by K-means clustering first and then detects R-peaks by comparing the peaks just in the extracted QRS regions. A wavelet transform is often used to preprocess ECG signals [11][12][13], and we also used this method in this paper. Furthermore, different algorithms are compared based on the MIT-BIH Arrhythmia Database.…”

Section: Introductionmentioning

confidence: 99%

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering

Xia

Han

Wang

2015

BME

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Abstract. Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

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Evaluation of QRS Complex Based on DWT Coefficients Analysis Using Daubechies Wavelets for Detection of Myocardial Ischaemia

Cited by 7 publications

References 13 publications

The effect of traditional Persian music on the cardiac functioning of young Iranian women

The effect of traditional Persian music on the cardiac functioning of young Iranian women

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering

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