{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang Z"],"funding":["Government of Japan Ministry of Education Culture Sports Science and Technology","NHLBI NIH HHS","Japan Science and Technology Agency","Kayamori Foundation of Informational Science Advancement"],"pagination":["e35623"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11337694"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(15)"],"pubmed_abstract":["Electrocardiogram (ECG) is a powerful tool to detect cardiovascular diseases (CVDs) and health conditions. We proposed a new method for evaluating ECG for efficient medical diagnosis in daily life. By splitting the signal according to the cardiac activity cycle, the periodic split attractor reconstruction (PSAR) method is proposed with time embedding, including three types of splitting methods to show its chaotic domain characteristics. We merged the CVDs dataset and the obstructive sleep apnea syndrome (OSAS) first-lead ECG signal dataset to validate the performance of PSAR for diagnosis and health monitoring using PSAR density maps as SE-ResNet input features. PSAR under 3 split methods showed different sensitivities for different CVDs. While in OSAS monitoring, PSAR showed good ability "],"journal":["Heliyon"],"pubmed_title":["A periodic split attractor reconstruction method facilitates cardiovascular signal diagnoses and obstructive sleep apnea syndrome monitoring."],"pmcid":["PMC11337694"],"funding_grant_id":["P01 HL141084","JPMJMI21I1","20H02108","R01 HL149349","R01 HL121059"],"pubmed_authors":["Umezu S","Sato D","Zhang Z","Hirose K","Uchida K","Yamada K"],"additional_accession":[]},"is_claimable":false,"name":"A periodic split attractor reconstruction method facilitates cardiovascular signal diagnoses and obstructive sleep apnea syndrome monitoring.","description":"Electrocardiogram (ECG) is a powerful tool to detect cardiovascular diseases (CVDs) and health conditions. We proposed a new method for evaluating ECG for efficient medical diagnosis in daily life. By splitting the signal according to the cardiac activity cycle, the periodic split attractor reconstruction (PSAR) method is proposed with time embedding, including three types of splitting methods to show its chaotic domain characteristics. We merged the CVDs dataset and the obstructive sleep apnea syndrome (OSAS) first-lead ECG signal dataset to validate the performance of PSAR for diagnosis and health monitoring using PSAR density maps as SE-ResNet input features. PSAR under 3 split methods showed different sensitivities for different CVDs. While in OSAS monitoring, PSAR showed good ability ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Aug","modification":"2025-04-04T07:54:03.527Z","creation":"2024-11-09T05:37:22.786Z"},"accession":"S-EPMC11337694","cross_references":{"pubmed":["39170365"],"doi":["10.1016/j.heliyon.2024.e35623"]}}