Abstract
Atrial fibrillation (AF) detection is crucial for stroke prevention. We investigated the potential of quantitative analyses of photoplethysmogram (PPG) waveforms to identify AF. Continuous electrocardiogram (EKG) and fingertip PPG were recorded simultaneously in acute stroke patients (n = 666) admitted to an intensive care unit. Each EKG was visually labeled as AF (n = 150, 22.5%) or non-AF. Linear and nonlinear features from the pulse interval (PIN) and peak amplitude (AMP) of PPG waveforms were extracted from the first 1, 2, and 10 min of data. Logistic regression analysis revealed six independent PPG features feasibly identifying AF rhythm, including three PIN-related (mean, mean of standard deviation, and sample entropy), and three AMP-related features (mean of the root mean square of the successive differences, sample entropy, and turning point ratio) (all p < 0.01). The performance of the PPG analytic program comprising all 6 features that were extracted from the 2-min data was better than that from the 1-min data (area under the receiver operating characteristic curve was 0.972 (95% confidence interval 0.951–0.989) vs. 0.949 (0.929–0.970), p < 0.001 and was comparable to that from the 10-min data [0.973 (0.953–0.993)] for AF identification. In summary, our study established the optimal PPG analytic program in reliably identifying AF rhythm.
Highlights
Atrial fibrillation (AF) is an important risk factor for systemic and cerebral embolism[1]
We prospectively collected the continuous waveforms of EKG and PPG signals simultaneously in patients admitted to the stroke intensive care unit (ICU)
We aimed to investigate whether quantitatively analyzing PPG waveforms can clearly identify patients with AF; we especially focused on selecting the PPG features and appropriate data length of PPG for feature extraction to optimize the PPG analytic program for AF identification
Summary
Atrial fibrillation (AF) is an important risk factor for systemic and cerebral embolism[1]. There are some limitations of EKG-based strategies, such as a short monitoring period (24-h Holter EKG), requiring patients to trigger the recorder (the patient-triggered event recorder), and high costs or invasive procedures (the mobile cardiovascular telemetry, the use of external event or loop recorders, or the use of insertable cardiac monitors)[2,8]. Photoplethysmogram (PPG) is an optics-based technology that can detect changes in blood flows during the heart’s activities and has been empirically applied to measure the saturation of oxygen and heart rate as pulse oximetry[9]. If PPG signals associated with AF rhythms can be reliably differentiated from those from non-AF rhythms, monitoring PPG signals may have potential for use in screening and identifying patients with AF, especially for those with paroxysmal AF. We prospectively collected the continuous waveforms of EKG and PPG signals simultaneously in patients admitted to the stroke intensive care unit (ICU). We aimed to investigate whether quantitatively analyzing PPG waveforms can clearly identify patients with AF; we especially focused on selecting the PPG features and appropriate data length of PPG for feature extraction to optimize the PPG analytic program for AF identification
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