{"doi":"10.1101/2022.12.03.22283065","title":"Detection of Left Ventricular Systolic Dysfunction from Single-Lead Electrocardiography Adapted for Wearable Devices","abstract":"ABSTRACT Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. Overall, 385,601 ECGs were used for development of a standard and noise-adapted model. For the noise-adapted model, ECGs were augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models performed comparably on clean ECGs with an AUROC of 0.90. The noise-adapted model performed significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models had an AUROC of 0.72 and 0.87, respectively when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable adapted tools from clinical ECG repositories.","journal":"medRxiv","year":2022,"id":305029,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"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":0.9549,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":807985,"name":"Veer Sangha","orcid":"0000-0002-8524-1203","position":1,"is_corresponding":false},{"id":89683,"name":"Evangelos K. Oikonomou","orcid":"0000-0003-4362-0720","position":2,"is_corresponding":false},{"id":534990,"name":"Lovedeep Singh Dhingra","orcid":"0000-0002-5664-4126","position":3,"is_corresponding":false},{"id":75766,"name":"Arya Aminorroaya","orcid":"0000-0003-3197-2657","position":4,"is_corresponding":false},{"id":391161,"name":"Bobak J. Mortazavi","orcid":"0000-0002-2655-2095","position":5,"is_corresponding":false},{"id":614790,"name":"Andreas Coppi","orcid":"0000-0002-5243-552X","position":6,"is_corresponding":false},{"id":1077,"name":"Harlan M. Krumholz","orcid":"0000-0003-2046-127X","position":7,"is_corresponding":false},{"id":74880,"name":"Rohan Khera","orcid":"0000-0001-9467-6199","position":8,"is_corresponding":false},{"id":985597,"name":"Akshay Khunte","orcid":"0000-0003-3812-3260","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:37.185846Z","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":[]}