[Identification of patients with atrial fibrillation using HRV parameters]

Area of Science:

• Cardiology
• Biomedical Engineering
• Medical Diagnostics

Background:

• Atrial fibrillation is a common cardiac arrhythmia.
• Embolism, especially stroke, is a severe complication of atrial fibrillation.
• Early identification of atrial fibrillation is crucial for timely intervention and stroke prevention.

Purpose of the Study:

• To develop and validate a secure and reliable algorithm for detecting atrial fibrillation.
• To assess the algorithm's effectiveness using single-channel ECG data.
• To evaluate the algorithm's performance in identifying patients with varying degrees of atrial fibrillation burden.

Main Methods:

• Utilized a 60-minute single-channel electrocardiogram (ECG).
• Detected R-peaks and calculated RR intervals, normalizing them to 60 bpm.
• Calculated time-domain heart rate variability (SDSD) and generated Poincaré plots for automated image analysis.

Main Results:

• The algorithm provides a risk level indicating the probability of atrial fibrillation.
• It can identify patients with atrial fibrillation even when not overtly present in the ECG during analysis.
• Achieved a sensitivity of nearly 83%, even with a 0% atrial fibrillation burden.

Conclusions:

• The developed algorithm offers a secure and reliable method for atrial fibrillation detection.
• The approach is effective in identifying at-risk patients through heart rate variability analysis.
• The algorithm demonstrates satisfactory sensitivity, even in cases with low atrial fibrillation burden, aiding in early diagnosis and stroke prevention.