Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers

Abstract

Background: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover, we aimed to expand our knowledge on novel ECG features in these patients.

Methods: A DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Gradient Class Activation Mapping++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features.

Results: The DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% CI, 0.91-0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (eg, R- and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (eg, increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features (P<0.001).

Conclusions: A DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.

Keywords: arrhythmogenic right ventricular dysplasia; cardiomyopathies; deep learning; mutation.

Figure 1.

Flowchart of the patient selection and model development process. HTx indicates heart transplantation; LVAD, left ventricular assist device; and PLN, phospholamban.

Figure 2.

Output of the Guided Grad-CAM visualization algorithm for all PLN (phospholamban) mutation carriers and their controls. Left: Mean of temporally normalized median 12-lead ECGs of both the PLN mutation carriers (blue) and control patients (red) with their respective standard deviations. Right: The same median ECG beat with the Guided Gradient Class Class Activation Mapping output of the deep neural network (DNN) superimposed to indicate the importance of a specific temporal segment for the classification of the DNN. The colormap represents the proportion of patients where that region was important (ie, had a Guided Gradient Class Class Activation Mapping value above the threshold).

Figure 3.

Representative examples of an ECG of a PLN (phospholamban) mutation carrier (top) and a control subject (bottom) with their respective deep neural network (DNN) probability score for having the PLN mutation. Note that the control subject ECG also exhibits the established PLN features (low QRS voltages and the presence of inverted T-waves in the left precordial leads) but is classified correctly as a control subject. The features as detected by the DNN (decreased R- and T-wave voltage in V3) can be used to distinguish the PLN mutation carriers and control subject.

Figure 4.

Relationship of the mean gradient class activation mapping ++ (Grad-CAM++) importance value of the T-wave area with the human interpretation of the T-wave and of the QRS-complex area with the human classification of low QRS voltage in PLN (phospholamban) patients. In the temporally aligned Grad-CAM++ curves, the mean is taken for the area of the QRS-complex and the T-wave. A boxplot of the importance values (between 0 and 1) of that region for the network for predicting PLN are shown in relationship with the human interpretation of the corresponding segments.