An approach of cardiac disease prediction by analyzing ECG signal

Tabassum, Tahmida; Islam, Mohammad Mahfuzul

doi:10.1109/ceeict.2016.7873093

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…The QRS complex wave, with the duration of 60 ms-100 ms, is the most important part in the ECG signal automatic analysis, and represents the electric potential variation of the two ventricular depolarization. Q wave is the first negative waveform, R wave is always the first positive deflection that follows the Q wave, and S wave is the downward deflection after the R wave [46]. T wave represents the electric potential variation of the two ventricular repolarization, and its direction is the same as the QRS main wave.…”

Section: Ecg Signal Processingmentioning

confidence: 99%

Section: Ecg Signal Processingmentioning

confidence: 99%

“…Normal sinus rhythm is the cardiac rhythm that begins with the myocardial contraction of the sinus node. A normal sinus rhythm shows the following five characteristics [46]. a. normal P wave, b. constant P-P and R-R interval, c. constant P wave configuration in a given lead, [48]: left bundle branch block beat, right bundle branch block beat, aberrated atrial premature beat, premature ventricular contraction, fusion of ventricular and normal beat, nodal premature beat, atrial premature beat, premature or ectopic supraventricular beat, ventricular escape beat, nodal escape beat ,and paced beat.…”

Section: Ecg Signal Processingmentioning

confidence: 99%

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Identifying the mislabeled training samples of ECG signals using machine learning

Cui

2019

Biomedical Signal Processing and Control

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The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database.

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Section: Ecg Signal Processingmentioning

confidence: 99%

Section: Ecg Signal Processingmentioning

confidence: 99%

Section: Ecg Signal Processingmentioning

confidence: 99%

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Identifying the mislabeled training samples of ECG signals using machine learning

Cui

2019

Biomedical Signal Processing and Control

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show abstract

“…The most serious disorders are irregular blood flows with a probability of having a heart attack. In total, 17.1 million individuals, or 29% of all deaths worldwide, have resulted from CVDs in 2004, according to a recent study [2]. One estimate has the number of fatalities at 5.7 million from stroke and 7.2 million from coronary heart disease.…”

Section: Introductionmentioning

confidence: 99%