Electrocardiogram (ECG) heart disease diagnosis using PNN, SVM and Softmax regression classifiers

El-Saadawy, Hadeer; Tantawi, Manal M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1109/intelcis.2017.8260040

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…In [75], ECG signal data were de-noised using a wavelet transform, and beat characteristics including RR intervals, morphological features, and statistical features were combined and used as input features for random forest classifiers, which achieved an average accuracy of 99.08%. El-Saadawy et al [76] extracted features from ECG heartbeats, applied PCA to remove unwanted features, and classified the ECG signals based on an SVM, with an average accuracy rate of 88.7%. Sahoo et al [77] used a PNN and radial basis function neural network (RBF-NN) to estimate six types of arrhythmias from an ECG signal, reporting an accuracy of 99.54% and 99.89%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Khairuddin et al [78] implemented the Haar wavelet transform and k-nearest neighbor classifier to detect arrhythmias and achieved an average accuracy of 97.30%. The authors in [75][76][77] successfully used different machine learning methods to classify arrhythmias with high accuracy. Machine learning algorithms require less training and classification time, less processing power, and less data than CNNs but still take a lot of time during the preprocessing stage.…”

Section: Discussionmentioning

confidence: 99%

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ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Mathunjwa

Lin²,

Lin³

et al. 2022

Sensors

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In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Mathunjwa

Lin²,

Lin³

et al. 2022

Sensors

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“…To simplify the computation when using features to diagnose heart diseases, PCA, a linear dimensionality reduction technique for finding principal components and replacing high-dimension data, is employed. PCA has been used in studies of heart arrhythmias classification [32], heart disease classification [33,2], emotion recognition [34], respiratory rate extraction [35], electrocardiogram heart disease diagnosis [36], and real-time magnetoencephalography neurofeedback [37]. A few major principal components are used to characterize HS features and diagnose heart diseases.…”

Section: Diagnostic Feature Determinationmentioning

confidence: 99%

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

Sun¹,

Huang

Zhang

et al. 2021

Preprint

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Background: As an eﬃcient method, heart sounds (HSs) analysis by classifying the features extracted from the four-stage sequence consisting of the ﬁrst heart sound (S1), second heart sound (S2), duration from S1 to S2 and duration from S2 to S1, has been widely used to diagnose heart disease and evaluate heart functions. However, the feature is diﬃcult to be extracted with high accuracy due to the four stages segmented from HSs with low accuracy; the ﬁxed classiﬁers achieved by training the features from the old samples cannot better ﬁt the features from the new ones because they are not adjusted with the incremental features. Thus, a novel intelligent diagnostic system, the innovations of which are primarily reﬂected in the automatic feature extraction and adjustable classiﬁer models, is proposed to realize the diagnosis of heart diseases with higher accuracy. Methods: The three stages of the proposed system are summarized as follows. In stage 1, the short time modiﬁed Hilbert transform (STMHT)-based curve is used to segment and extract the ﬁrst complex sound (CS1) and second complex sound (CS2). In stage 2, the envelopes CS1FE and CS2FE for periods CS1 and CS2 are obtained via a novel method, and the frequency features are automatically extracted from CS1FE and CS2FE by setting diﬀerent threshold value (Thv) lines. Finally, the principal component analysis (PCA)-based ﬁrst three principal components γ1, γ2, and γ3 are determined as the diagnostic features. In stage 3, a Gaussian mixture model (GMM)-based objective function fet(x) is generated. Then, the χ2 distribution for component k is determined by calculating the Mahalanobis distance from x to the class mean µk for component k, and the conﬁdence region of component k is determined by adjusting the optimal conﬁdence level βk and used as the criterion to diagnose HSs. Results: The performance evaluation was validated by sounds from online HS databases and clinical heart databases. The accuracy of the proposed method was compared to the accuracies of other well-known classiﬁers, and the highest classiﬁcation accuracies of 99.43%, 98.93%, 99.13%, 99.85%, 98.62%, 99.67% and 99.91% in the detection of MR, MS, ASD, NM, AS, AR and VSD sounds were achieved by setting βk(k = 1,2,...,7) to 0.87,0.65,0.67,0.65,0.67,0.79 and 0.87, respectively. Conclusions: This proposed intelligent diagnosis system provides an eﬃcient way to diagnose seven types of heart diseases. In addition, methods to manage the sounds (such as some ASD sounds) when CS1 and CS2 cannot be segmented and extracted via the STMHT method will be explored in the future to characterize the physical meanings of the frequency components and to build a model of the secondary curve in the frequency domain.

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Machine Transfer Learning Deep Softmax Regression Neural Network for Image Classification

Praveenkumar¹,

Muthusamy²

2022

Lecture Notes in Networks and Systems

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Electrocardiogram (ECG) heart disease diagnosis using PNN, SVM and Softmax regression classifiers

Cited by 11 publications

References 13 publications

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

A Novel Intelligent System Based on Adjustable Classifier Models for Diagnosing Heart Sounds

Machine Transfer Learning Deep Softmax Regression Neural Network for Image Classification

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