An efficient QRS detection method for ECG signal captured from fingers

We propose a novel QRS detection method for ECG signal captured from fingers (briefly finger-ECG). In this work, we use curvature as an estimation of energy of ECG signal. We need only three computationally simple steps for the detection of QRSs. A single threshold is used to determine QRSs from local minima of energy making the method linear i.e. O(n) in computational time which is O(n2) for slope based methods for a signal of n samples. The method is tested on 224 records of finger-ECG signal, and we have obtained superior performance than the state-of-the-arts slope-based method. Average time error of detection is also significantly lower by this method. This method has a single parameter of adaptive threshold, which required no tuning for our database whereas the slope based method required tuning of parameters. This method is potentially suitable for real time applications of finger-ECG in e-health and biometrics.