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

Abstract

Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared. Approach. Seven different databases with 12-lead ECGs were provided during the PhysioNet/Computing in Cardiology Challenge 2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system’s performance. Main results. Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric ( C M ). Also, we obtained a mean G -score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation. Significance. We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

“…Different electrophysiological features have been extracted from 12-lead ECG data to identify cardiac arrhythmias with varying classification accuracy. Jimenez et al [83] extracted 81 features per ECG-lead based on heart rate variability, QRST patterns, and spectral domain and applied one-versus-rest classification from independent binary classifiers for each cardiac condition. A classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was then selected for each class.…”

Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs

“…Li et al [ 12 ] used leads II and V1 from 12-lead ECG signals to develop a 31-layer 1-D residual convolutional neural network for arrhythmia classification. However, using single or reduced ECG leads for the automatic and accurate diagnosis of heart diseases still presents a challenge [ 13 , 14 ]. The mentioned methods select several leads from 12 leads relying only on experience or random selection.…”

Diagnostic Performance Of Single-Lead Electrocardiograms For Arterial Hypertension Diagnosis: A Machine Learning Approach

“…So far, wearable ECGs have consistently demonstrated their value in detecting arrhythmias such as atrial brillation [19]. Although still challenging, detecting cardiac diseases using a single or reduced number of leads has appeared in the literature [20]. In recent studies, single-lead ECGs were used to detect T-wave (due to ventricular repolarisation) morphology abnormalities [21], to develop an automatic mental stress detection system based on ECG signals from smart T-shirts [22], and to identify patients with atrial brillation-induced heart failure [23].…”