Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification

Abstract

Background Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately. Methods This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme. Results Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture.

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

“…Automatic analysis of electrocardiograms is a rather complex theoretical problem. First of all, this is due to the physiological origin of the signal [7][8][9] , which is the reason for its indeterminacy, diversity, variability, unpredictability, non-stationarity and susceptibility to numerous types of interference.…”

“…C ξ 1 (i) about the sequence C(i), i = 1, 14 can be obtained by determining the cross-correlation of the elements the array C. Consider this array as a vector random sequence C , each component of which corresponds to a row of the array C . Applying the vector linear canonical decomposition to C gives the following expression for the first component of (7).…”

Predicting Cardiac Issues From Echocardiograms: A Literature Review Using Deep Learning and Machine Learning Techniques

“…Darmawahyuni et al [46] developed a generalization model of deep learning for ECG signal categorization in intra and interpatients' scenarios. This technique reduced artifacts and noise in ECG data by pre-processing them using discrete wavelet transforms.…”

Race, Sex and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

“…Several studies have used machine learning algorithms to identify cardiovascular conditions from electrocardiograms (ECGs) [6][7][8] . Deep neural networks can outperform cardiologists in recognizing several abnormalities from 12-lead ECG recordings, achieving F1scores above 80% and specificity above 99% 9 .…”