Narrow Results

Shockable ventricular arrhythmias (VAs) such as ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening conditions requiring immediate attention. Cardiopulmonary resuscitation (CPR) and defibrillation are the significant immediate recommended treatments for these shockable arrhythmias to obtain the return of spontaneous circulation. However, accurate classification of these shockable VAs from nonshockable ones is the key step during defibrillation by automated external defibrillator (AED). Therefore, in this work, we have proposed a novel algorithm for an automated differentiation of shockable and nonshockable VAs from electrocardiogram (ECG) signal. The ECG signals are segmented into 5, 8 and 10s. These segmented ECGs are subjected to four levels of discrete wavelet transformation (DWT). Various nonlinear features such as approximate entropy ($E_a^x)$, signal energy (${\mathrm{\Omega }}^x$), Fuzzy entropy ($E_f^x$), Kolmogorov Sinai entropy ($E_{\mathrm{\text{ks}}}^x)$, permutation entropy ($E_p^x$), Renyi entropy ($E_r^x$), sample entropy ($E_s^x$), Shannon entropy ($E_{\mathrm{\text{sh}}}^x$), Tsallis entropy ($E_t^x$), wavelet entropy ($E_w^x$), fractal dimension ($F_D^x$), Kolmogorov complexity ($C_k^x$), largest Lyapunov exponent ($E_{\mathrm{\text{LLE}}}^x$), recurrence quantification analysis (RQA) parameters ($R{Q}_i^x$), Hurst exponent ($H^x$), activity entropy ($E_{\mathrm{\text{ac}}}^x$), Hjorth complexity ($H_c^x$), Hjorth mobility ($H_m^x$), modified multi scale entropy ($E_{{\mathrm{\text{mms}}}_y}^x$) and higher order statistics (HOS) bispectrum ($B{i}_i^x$) are obtained from the DWT coefficients. Later, these features are subjected to sequential forward feature selection (SFS) method and selected features are then ranked using seven ranking methods namely, Bhattacharyya distance, entropy, Fuzzy maximum relevancy and minimum redundancy (mRMR), receiver operating characteristic (ROC), Student’s $t$-test, Wilcoxon and ReliefF. These ranked features are supplied independently into the $k$-Nearest Neighbor (kNN) classifier. Our proposed system achieved maximum accuracy, sensitivity and specificity of (i) 97.72%, 94.79% and 98.74% for 5s, (ii) 98.34%, 95.49% and 99.14% for 8s and (iii) 98.32%, 95.16% and 99.20% for 10s of ECG segments using only ten features. The integration of the proposed algorithm with ECG acquisition systems in the intensive care units (ICUs) can help the clinicians to decipher the shockable and nonshockable life-threatening arrhythmias accurately. Hence, doctors can use the CPR or AED immediately and increase the chance of survival during shockable life-threatening arrhythmia intervals.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.

The developments in computer-aided diagnostic technology have promoted the advancement of the Medical Internet of Things. Because electrocardiogram (ECG) signals can reflect the health of the human heart, their classification is conducive to diagnosing heart disease. In this study, a novel classification method for ECG signals based on the continuous wavelet transform (CWT) and a convolutional neural network (CNN) is proposed. The proposed method can construct the time-frequency image of an ECG signal using CWT. Subsequently, the image is input to a CNN model. By leveraging the GoogLeNet network model for image recognition, the model establishes an accurate mapping for recognizing different ECG signals. Multiple public datasets are used to validate the effectiveness of the proposed algorithm. In addition, this method exhibits a higher recognition accuracy than other methods.

In this study, we are concerned with the concept of fuzzy logic networks and their use to logic-based classification of ECG signals. The networks under discussion are treated as homogeneous architectures consisting of OR/AND neurons introduced by Hirota and Pedrycz. Regarded as generic processing units, OR/AND neurons are neurofuzzy constructs that exhibit well-defined logic characteristics come with a high level of parametric flexibility and exhibit significant interpretation abilities. The composite logic nature of the logic neurons becomes instrumental in covering a broad spectrum of characteristics between plain and and or logic descriptors (connectives). From the functional standpoint, the developed network realizes a logic approximation of multidimensional mappings between unit hypercubes, that is, transformations from [0,1]ⁿ to [0,1]^m. The way in which the structure of the network has been formed leads directly to the logic characterization of ECG signals by aggregating logically selected inputs with the use of and and or operators. The design of the logic description consists of two main development phases that is (a) structural optimization which is focused on the identification of the most “descriptive” (discriminating) features (inputs) and (b) ensuing parametric refinement of the logic description that is realized by adjusting the connections (weights) of fuzzy neurons of the structure. The design is carried out in the framework of genetic optimization and genetic algorithms, in particular. The experimental part of the study concerns illustrative synthetic data and a series of QRS complexes coming from the MIT-BIH database.

A method for improving electrocardiogram (ECG) signal classification in time domain is presented. The main idea is to preprocess the segmented waveforms in order to obtain an alignment of the ECG with respect to the maximum value of the R beat while keeping the information on its initial position as a feature. We propose two simple preprocessing methods basically consisting in the alignment of the R-waves either by translation or by a slight nonlinear time scaling. These techniques are also used to improve the validation of ECG compression.

Supervised machine learning methods were used to predict patient heart disease in this work. The dataset was extracted from an open, traceable dataset for research purposes. 1600 electrocardiogram (ECG) records were extracted, and only single label records were used. ECG analysis was performed by determining particular fiducial points for all the ECG signals from which seven intervals and five amplitudes were obtained. Furthermore, these features were individually calculated for each of the 12 ECG leads, except for the RR interval, which was calculated only once using all 12 leads. However, it was included in the feature vectors for all leads. The amplitude and interval features were used to form the feature vectors. The accuracy scores for each lead were calculated using two supervised machine learning methods and using five-fold cross-validation. Taking into account that the dataset was balanced, the results were studied using the accuracy score. The results were represented by confusion matrices and diagrams. All captured results were compared. Finally, the sensitivity of the machine learning methods was reviewed to consider variations in the classification performances.