Electrocardiogram Analysis by Means of Empirical Mode Decomposition-Based Methods and Convolutional Neural Networks for Sudden Cardiac Death Detection

Centeno-Bautista, Manuel A.; Rangel-Rodriguez, Angel H.; Perez-Sanchez, Andrea V.; Amezquita-Sanchez, Juan P.; Granados-Lieberman, David; Valtierra-Rodríguez, Martín

doi:10.3390/app13063569

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…To address the fixed basis function issue for a decomposing signal, a data-driven method, known as empirical mode decomposition (EMD), has been developed [26]. EMD is an adaptive method for analyzing nonstationary signals; thus, it is adequate and used for analyzing ECG signals [27][28][29]. It produces a sum of amplitude and frequency-modulated functions, namely, intrinsic mode functions (IMFs).…”

Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Oh,

Choi

2024

Electronics

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Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF.

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Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Oh,

Choi

2024

Electronics

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“…As a result of its different approach and the advantages over the analysis of complex and nonlinear time-series signals, the use of the EMD technique has gained increasing attention and has been adopted for a variety of biomedical signals, such as the electroencephalogram for epileptic seizure classification [25,26], emotion recognition [27,28], and the identification of autism severity level [29], electromyography for the analysis of amyotrophic lateral sclerosis [30,31] or for the classification of neuromuscular disorders [32,33], and ECG signals for the classification of cardiovascular diseases [34], ECG denoising [35,36], the classification of ventricular arrhythmias [37], the prediction of sudden cardiac death [38], and the detection of hypertension [39] or the extraction of fetal ECG [40].…”

Section: Introductionmentioning

confidence: 99%

The Use of Empirical Mode Decomposition on Heart Rate Variability Signals to Assess Autonomic Neuropathy Progression in Type 2 Diabetes

et al. 2023

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In this study, we investigated the use of empirical mode decomposition (EMD)-based features extracted from electrocardiogram (ECG) RR interval signals to differentiate between different levels of cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). This study involved 60 participants divided into three groups: no CAN, subclinical CAN, and established CAN. Six EMD features (area of analytic signal representation—ASRarea; area of the ellipse evaluated from the second-order difference plot—SODParea; central tendency measure of SODP—SODPCTM; power spectral density (PSD) peak amplitude—PSDpkamp; PSD band power—PSDbpow; and PSD mean frequency—PSDmfreq) were extracted from the RR interval signals and compared between groups. The results revealed significant differences between the noCAN and estCAN individuals for all EMD features and their components, except for the PSDmfreq. However, only some EMD components of each feature showed significant differences between individuals with noCAN or estCAN and those with subCAN. This study found a pattern of decreasing ASRarea and SODParea values, an increasing SODPCTM value, and a reduction in PSDbpow and PSDpkamp values as the CAN progressed. These findings suggest that the EMD outcome measures could contribute to characterizing changes associated with CAN manifestation in individuals with T2DM.

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“…Additionally, there have been studies on the use of machine learning techniques for heart disease prediction, such as the ANN-based approach [2] for the detection and identification of congenital heart disease in pediatric patients and the cardiovascular disease prediction model based on the improved deep belief network [11]. To improve the generalization and robustness against noise, the use of a deep learning network model [12] and a complete ensemble empirical mode decomposition method [13] have also been proposed. A large annotated dataset and a very deep convolutional network [14,15], which can map a sequence of ECG samples to a sequence of arrhythmia annotations, are key to the performance of these models.…”

Section: Introductionmentioning

confidence: 99%

Heart Murmur Classification Using a Capsule Neural Network

Tsai,

Liu,

Zheng

et al. 2023

Bioengineering

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The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset.

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Electrocardiogram Analysis by Means of Empirical Mode Decomposition-Based Methods and Convolutional Neural Networks for Sudden Cardiac Death Detection

Cited by 9 publications

References 24 publications

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

The Use of Empirical Mode Decomposition on Heart Rate Variability Signals to Assess Autonomic Neuropathy Progression in Type 2 Diabetes

Heart Murmur Classification Using a Capsule Neural Network

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