期刊
IEEE ACCESS
卷 9, 期 -, 页码 123146-123164出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3109806
关键词
Electrocardiography; Medical services; Stroke (medical condition); Biomedical monitoring; Medical diagnostic imaging; Big Data; Hospitals; Cyber-physical systems; electrocardiography; biomedical monitoring; big data applications; biomedical informatics
资金
- National Research Council of Science and Technology (NST) Grant through Korean Government
- Ministry of Science and ICT [CRC-15-05-ETRI]
- National Research Council of Science & Technology (NST), Republic of Korea [CRC-15-05-ETRI] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The study assessed the feasibility of a cyber-physical cardiac monitoring system for classifying stroke patients and healthy adults, proposing the Big-ECG system for stroke management. Analysis of ECG data revealed distinctive biomarkers for stroke patients compared to healthy controls, with the Random Trees model showing the best classification performance.
Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.
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