Portable Measurement Methods Combined With Artificial Intelligence in Detection of Atrial Fibrillation
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Arrhythmia detection in rhythm change with single-lead ECG and wearable PPG
研究概览
简要总结
In Western countries, every sixth person in their lifetime and 15,000 people in Finland have a new stroke each year. About every fourth stroke is based on cardiac embolism. Atrial fibrillation (AF) is the most common arrhythmia that increases the risk of thromboembolic complications, such as stroke. It may cause formation of thrombi in the left atrium with ensuing embolization in the cerebral and peripheral circulation. AF is often asymptomatic and paroxysmal. Thus, the diagnosis of AF is often challenging.
A new onset AF is usually treated with cardioversion (CV), in which the abnormal rhythm is converted back to sinus rhythm (SR). However, a long-lasting AF (>48 hours) is associated with risk of stroke. Therefore, the duration of AF needs to be known before a CV can be performed. This study evaluates the ability of novel customer-targeted heart measuring devices to detect rhythm change and short AF episodes. Moreover, novel biomarkers will be analyzed from the blood samples of AF patients and their suitability to estimate the duration of AF will be evaluated.
The research will be accomplished in cooperation with the Kuopio University Hospital Emergency Department, the Heart Center, the Department of Applied Physics of the University of Eastern Finland and Heart2Save Ltd.
The results of the research project will be published in the scientific journals of medicine and medical technology and will be presented at scientific conferences of the respective fields. The research results of the project can be utilized by all companies in the medical technology industry, in particular companies that produce ECG measuring instruments and companies that produce rhythm recognition software.
详细描述
The research aims to solve the following medical problems:
- To study the feasibility of wrist worn PPG devices and single-lead ECG chest band. Special interest will be the use of AI in data analysis and its impact on arrhythmia detection.
- Develop state of the art PPG and ECG based methods for long term AF monitoring.
The main research questions are:
- Can a single-lead ECG and PPG measurement be used to detect atrial fibrillation?
- Can artificial intelligent (AI) arrhythmia analysis reliable detect rhythm changes from customer-targeted PPG and ECG recording?
- Are biomarkers measured from blood sample suitable for the estimation of recent-onset AF duration?
Specific methodological aims are:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients with early-onset atrial fibrillation treated by cardioversion during the treatment period in the emergency department of Kuopio University Hospital.
排除标准
- •Body mass index (BMI) over 35, implanted heart pacemaker device and a medical condition requiring immediate treatment that would be delayed by the study measurements.
结局指标
主要结局
Arrhythmia detection in rhythm change with single-lead ECG and wearable PPG
时间窗: 2 hours
Sensitivity and specificity of paroxysmal atrial fibrillation detection in short detection time frame during controlled rhythm change during cardioversion.
Evaluation the various blood-based biomarkers in the estimation the duration of atrial fibrillation episode
时间窗: 2 weeks
Kinematic models in various specific biomarkers during atrial fibrillation to predict the time domain of the arrhythmia.
次要结局
未报告次要终点
