The specific objectives and methods of this project are: (1) To test the feasibility and accuracy of integrating EEG, MECG and EMG for detecting the severity of diseases such as aortic stenosis, heart failure and ischemic stroke. (2) Improve the accuracy of this multi-channel brain-heart-muscle device by using an artificial intelligence auxiliary system. (3) Provide tailor-made interdisciplinary treatment strategies for patients with different disease states.
Age≥20 year-old
Healthy subjects or patients with A. Metabolic syndrome (MetS) ；B. HF with reduced ejection fractions (HFrEF, defined by left ventricular ejection fraction≤40%) and current or previous symptoms of HF；C. Severe aortic stenosis (defined aortic valve area < 1.0 cm2 or mean pressure gradient ≥ 40 mmHg), preparing to receive transcatheter aortic valve replacement ；D. Acute ischemic stroke and admitted to stroke intensive care unit (ICU), within 24 hours after onset
Abdominal obesity: waist circumference (for Asians) ≥ 90 cm in men and ≥ 80 cm in women;
Hyperglycemia: serum fasting glucose level ≥ 100 mg/dl, or receiving drug treatment for elevated blood sugar;
Under treatment for malignancy
Currently receiving immunosuppressants
Modified Rankin Scale (mRS) ≥2 prior to the stroke
Symptomatic HF in patients with acute ischemic stroke
In this project, seven hundred and fifty male or female patients with healthy subjects or meet the inclusion conditions of the following four diseases over 20 years of age will be enrolled. A. Patients with metabolic syndrome B. Patients with heart failure C. Patients with severe aortic valve stenosis who have been evaluated as suitable for transcatheter aortic valve replacement D. Acute ischemic stroke patients admitted to stroke intensive care unit
Case-only
Cross-sectional
Not listed
6
Trial groupsSee each trial group below.
Composite cardiovascular outcome
Composite endpoint: Major adverse cardiac events (MACE): myocardial infarction, stroke, hospitalization for heart failure, or cardiovascular death)
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