ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network

Abstract

Objective: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g. atrial or ventricular activity-based analyses).

Approach: We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8528 single-lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15 × 1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training.

Main results: We evaluated our algorithm on 3658 testing data and obtained an F 1 accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5th in the 2017 CinC Challenge.

Significance: Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF.

Conflict of interest statement

The authors declare no conflicts of interests.

Figures

Figure 1.

A schematic to illustrate the basic operations for residual blocks. In residual blocks, the skip connections provide an alternative pathway for information to be propagated without introducing additional parameters. The original input data is merged with its respective transformed version via the use of an element-wise sum.

Figure 2.

A recurrent layer with a single node. A) A compressed representation of the recurrent node. B) An unfolded version illustrating the cycling of weights inside a recurrent node at different time steps. Note that the weights within each layer are shared but applied to different time steps. The first node li,t=12 simply inputs the information from the previous layer as there is no previous time step.

Figure 3.

The data used in this study. A) Typical examples of single-lead ECG recordings for each of the four classes: normal rhythm (N), atrial fibrillation (AF), other rhythm (O) and noisy (~), in the provided dataset in the 2017 CinC Challenge. B) An illustration of the distribution of the four classes within the training set and within the test set hidden to the public.

Figure 4.

The architecture of the proposed residual recurrent convolutional neural network (RhythmNet). The ECG signal is inputted in windows of 5 seconds and then passed through 16 repeated residual blocks containing convolutions of different depth. The output of the residual blocks is then flattened into a 1D vector, and fed through three recurrent layers to process the successive windows sequentially, 5 seconds at a time. The output of the recurrent layer is then mapped onto a fully connected layer with four nodes denoting the probabilities of the four classes to predict for.

Figure 5:

Diagnostics of RhythmNet during cross-validation on the training set. A) A bar plot to visualize the proportions of the predictions vs ground truths. B) The corresponding confusion matrix with the proportions in %. The noisy class is in grey as it was not considered in the score.