![]() ![]() In this study, a system was designed and built that measures changes in blood pressure non-invasively and by an oscillometric method with high sampling frequency. With the advancement of technology in the field of medical engineering, analog blood pressure monitors have gradually been replaced by digital devices, such that the digital blood pressure monitors are used today. The first biomedical parameter taken from clients in health centers is blood pressure changes. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.Ĭonsidering the prevalence of hypertension in communities, the measurement of blood pressure is one of the most important parameters in the field of health. The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. ![]() This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.Īccurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. The accuracy of the training set and validation set are 100% and 93.4%. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The two-dimensional time-frequency matrices are obtained. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. ![]() Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. In this way, the proposed method can be effective in differentiating the ECG signals of normal subjects and cases with AF.Ītrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Our results indicate that the area criterion of IMFs in the complex plane in the normal cases are greater than those of the AF cases. The area of this circle in the complex plane has been used as a feature in order to categorize normal ECG signals from the AF ECG signals. EMD is used as a proper technique for analyzing and decomposing non-stationary data. Although traditional processing tools such as short-time Fourier or wavelet transforms are applied for studying biological signals, they are unable to probe completely the ECG signals. ECG signals like other biological signals are generally non-stationary. Using a Central Tendency Measure (CTM), the radius of a circle in which 95% of analytic function points are located within the circle is determined. In this paper, analytic functions corresponding to IMFs are obtained using the Hilbert transform. The decomposition is based on the direct extraction of components related to various intrinsic time scales. EMD generates a limited and small number of Intrinsic Mode Functions (IMFs). ![]() This paper presents the application of empirical mode decomposition (EMD) for analyzing electrocardiograph (ECG) signals and detecting Atrial Fibrillation (AF) cases. ![]()
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