Factor Analysis for the Performance Impacts of Real-Time Ventricular Fibrillation Detection on Microcontroller

Early research focused on developing effective algorithms for Ventricular fibrillation (VF) detection; while most of the evaluations have been conducted offline with prefiltered data sets, practical application requires these tests to be performed in real time. Because there are many factors that may impact detection effectiveness, it is important to understand the impact of factors that improve detection accuracy. In this study, we developed an integrated simulated environment using IAR Embedded Workbench software to build an embedded system using a MSP430 microcontroller and Visual studio tool for S/W build; we then used this system to conduct real-time experiments for evaluating five lightweight VF detection algorithms and to examine factors that may impact their performance in terms of sensitivity, specificity, positive-predictivity, accuracy and computational time. The results were cross-validated using a prototype of a wearable Electrocardiogram (ECG) system developed by this study. The study showed that 1) the chosen detection algorithm, data filtering, and window size all have a significant impact on the performance of real-time VF detection; among these, the detection algorithm had the greatest impact so it must be carefully selected; 2) it is important to select the proper threshold value that affects tradeoffs in performance metrics. Among the five algorithms that this study evaluated, the Time Delay (TD) algorithm outperformed the others independent of window size or filtering method. Considering the tradeoff between robustness and efficiency, TD is preferable because detection accuracy and robustness are more critical.