{"doi":"10.22489/cinc.2024.109","title":"ECG-based Deep Convolutional Recurrent Network with Attention Mechanism for Sleep Apnea Detection","abstract":"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.","journal":"Computing in cardiology","year":2024,"id":492099,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9545,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1337929,"name":"El‐Hadi Djermoune","orcid":"0000-0001-6605-2928","position":1,"is_corresponding":false},{"id":1337930,"name":"Laurent Bougrain","orcid":"0000-0001-6794-0505","position":2,"is_corresponding":false},{"id":1338389,"name":"Pauline Guyot","orcid":null,"position":3,"is_corresponding":false},{"id":1338388,"name":"Faustine Faccin","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:08:49.768795Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}