Development of Computer Aided Prediction Technology for Paroxysmal Atrial Fibrillation in Mobile Healthcare

Abstract

Atrial fibrillation (AF) is one of the major health risks for strokes, and has been found to increase the risk of ischemic stroke fivefold (Wolf et al., 1987). Strokes with AF are more severe, cause greater disability and have worse outcomes than those without (Jorgensen et al., 1996; Lin et al., 1996). Approximately 15–20% of all strokes are due to AF but the percentage increases up to 40% for patients over the age of 90 (Marini et al., 2005). As the risk of initial strokes in these patients can be reduced up to 64% by long-term treatment with anti-coagulants (Hart et al., 2007), accurate and timely detection of AF is essential in highrisk elderly populations. Paroxysmal AF (PAF) is defined as an AF episode that lasts longer than 2 min but less than 7 days. Most PAF episodes terminate spontaneously within 24 h. Although PAF is short-lived, it shares similar stroke risks with long-lasting persistent AF (Friberg et al., 2010). Furthermore, the rare and asymptomatic manifestation of AF episodes makes a definitive diagnosis of PAF even harder to provide. Holter or event recorder devices are used conventionally to capture actual episodes of AF. Using 24-h Holter monitoring, only 23% of cases were detected among PAF patients whose diagnosis was confirmed by continuous bedside electrocardiograph (ECG) monitoring (Rizos et al., 2010), which indicates an underestimated detection of PAF leading to inadequate treatment. Using 7-day event-loop recording, 5.7% of cases that were missed by 24-h Holter monitoring were revealed to be PAF (Jabaudon et al., 2004). Thus, prolonged ECG monitoring by event recorders improves the detection rates of PAF cases. However, event recorders may not be appropriate for all patients, and depend on patient compliance. The extended use of Holter monitors is not commonly practiced in clinics, and few technical guidelines are currently available for the improvement of PAF detection by a short-term use of Holter monitors. Prediction methods have been studied in order to suppress PAF episodes through atrial pacing techniques and to provide the clinical benefit of maintaining normal sinus rhythm (NSR) in, for example, drug-refractory AF patients (Delfaut et al., 1998). Most of the methods developed were inspired by earlier physiological findings that were associated with the triggering mechanisms of PAF, namely, activated ectopic nodes in abnormal atrial tissue and the imbalance of the autonomic nervous system (ANS) via increased sympathetic