Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs

Abstract

Background - Twelve lead ECGs are a core diagnostic tool for cardiovascular diseases. Here, we describe and analyse an ensemble deep neural network architecture to classify 24 cardiac abnormalities from 12 lead ECGs. Method - We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions. The deep features were augmented with age and gender features in the final fully connected layers. Output thresholds for each class were set using a constrained grid search. To determine why the model made incorrect predictions, two expert clinicians independently interpreted a random set of 100 misclassified ECGs concerning Left Axis Deviation. Results - Using the bespoke weighted accuracy metric, we achieved a 5-fold cross-validation score of 0.684, and sensitivity and specificity of 0.758 and 0.969, respectively. We scored 0.520 on the full test data, and ranked 2nd out of 41 in the official challenge rankings. On a random set of misclassified ECGs, agreement between two clinicians and training labels was poor (clinician 1: $\kappa = -0.057$, clinician 2: $\kappa = -0.159$). In contrast, agreement between the clinicians was very high ($\kappa = 0.92$). Discussion - The proposed prediction model performed well on the validation and hidden test data in comparison to models trained on the same data. We also discovered considerable inconsistency in training labels, which is likely to hinder development of more accurate models.

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

“…The number of categories considered in the reviewed studies generally ranges from 2 to 9. As the number of categories to be classified in the DL models increases, learning the mapping for arrhythmia classification becomes more challenging [20]. In MITDB, the total number of categories is 26.…”

“…This mechanism of training and testing DL models with ECG signals from two non-overlapping groups of patients, respectively, is a typical case of inter-patient diagnosis. However, as those datasets have different attributes such as categories and numbers of channels, a smaller number of classification categories, such as categories of Atrial fibrillation (AF)/non-AF and categories of AF, Normal, Premature Atrial Contractions (PAC), Premature Ventricular Contractions (PVC), Ventricular fibrillation (VF), and Noise are often considered [19,20]. In [21], ECG signals from MITDB, MIT-BIH AFDB, CUDB, and MIT-BIH VFDB are fused to form one dataset where the training and testing datasets are obtained by randomly selecting ECG data from the combined dataset.…”

“…The discrete wavelet transform (DWT) could project ECG signals onto the time-frequency domain based on wavelet basis functions [36]. To remove the noise, the wavelet coefficients at high-frequency bands can be simply set to zero or apply a thresholding process to set the modest wavelet coefficients to zero [19][20][21][22] based on the assumption that the useful ECG signal is similar to the selected wavelet basis function. A combination of different types of denoising methods can be applied for noise removal, e.g., [37,38] combines DWT, median filters, or S-G filters for denoising.…”

“…Binary classification (19%, 73 out of 368) is mostly used to identify one certain arrhythmia type, such as AF [79] and left ventricular dysfunction [80]. The conclusions from many studies suggest that accurate multi-class arrhythmia classification is more challenging [19,20].…”

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

“…Other works aim to detect as many as nine cardiac conditions in large ECG datasets, mostly using CNNs (Jo et al 2021, Hua et al 2021, Dai et al 2021. Besides this, recent works have reported results for the detection of 24-27 cardiac abnormalities in databases containing a maximum of 66 361 samples, using 12 leads and different deep learning frameworks (Zhaowei et al 2021, Giovanni et al 2021, Zhao et al 2022.…”