Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
The electrocardiogram (ECG) is a fundamental tool in the everyday practice of clinical medicine, with more than 300 million ECGs obtained annually worldwide, and is pivotal for diagnosing a wide spectrum of arrhythmias. In a study published in Nature Medicine, we developed a deep neural network to classify 10 arrhythmias as well as sinus rhythm and noise from a single-lead ECG signal, and compared its performance to that of cardiologists.
Can you identify the heart arrhythmia in the above example? The neural network is able to correctly detect AVB_TYPE2. Below, you can see other rhythms which the neural network is successfully able to detect.
We developed a convolutional DNN to detect arrhythmias, which takes as input the raw ECG data (sampled at 200 Hz, or 200 samples per second) and outputs one prediction every 256 samples (or every 1.28 s), which we call the output interval. The network takes as input only the raw ECG samples and no other patient- or ECG-related features. The network architecture has 34 layers; to make the optimization of such a network tractable, we employed shortcut connections in a manner similar to the residual network architecture.
Distinct from some other recent DNN approaches, no significant preprocessing of ECG data, such as Fourier or wavelet transforms, is needed to achieve strong classification performance.
