Hospital patients recovering from major cardiac surgery are at risk of paroxysmal atrial fibrillation (AF), which can be life-threatening. Wearable sensors are routinely used for electrocardiogram (ECG) monitoring in patients at risk of AF, providing real-time AF detection. However, wearable sensors could have greater impact if used to identify the subtle changes in P-wave morphology which precede AF. This would allow prophylactic treatment to be administered, potentially preventing AF. ECG signals acquired by wearable sensors are susceptible to several artefacts, making it difficult to distinguish between physiological changes in P-wave morphology, and changes due to noise. The aim of this study was to design and assess the performance of a novel automated P-wave quality assessment tool to identify high quality P-waves, for AF prediction.

We designed a two-stage algorithm which uses P-wave template matching to assess quality. Its performance was assessed using the AFPDB, a database of wearable sensor ECG signals acquired from both healthy subjects and patients susceptible to AF. The algorithm’s quality assessments of 97,989 P-waves were compared to manual annotations. The algorithm identified high quality P-waves with high sensitivity (93%) and good specificity (82%).

This study indicates that the algorithm may have utility for identifying high quality P-waves in wearable sensor data. Measurements of P-wave morphology derived from high quality P-waves could be used to predict AF, improving patient outcomes and reducing healthcare costs. Further studies assessing the clinical utility of the presented tool are warranted for validation.