ECG-based Deep Convolutional Recurrent Network with Attention Mechanism for Sleep Apnea Detection

Sleep apnea syndrome (SAS) is a nocturnal respiratory disorder that can be associated with long-term cardiovascular complications.Alternative screening solutions are currently being developed to overcome the limitations of reference in-lab polysomnography.As the respiratory signal can be reconstructed from the electrocardiogram (ECG), the latter is all the more interesting as its recording is easy and non-invasive for the patient.The application of deep learning algorithms using ECGs has proved effective in classifying sleep-related pathological events.In this paper, we propose a novel hybrid architecture to detect apneic episodes using single-lead ECGs.Following a preprocessing step, morphological and temporal components of interest are extracted through convolutional and recurrent blocks, respectively.Additional mechanisms are further integrated to enhance the classification.Models were trained and validated on a dataset derived from STAGES and Apnea-ECG databases.Influence of patient phenotype on classification was estimated by comparing the performance between several groups of patients with different clinical information.Overall, a model we have developed performs competitively with the best current methods by accurately classifying patients to different degrees of severity with average sensitivity, specificity and accuracy of 93.38%, 75.46% and 86.66%, respectively.