Event-Driven ECG Classification using Functional Approximation and Chebyshev Polynomials

Level-crossing ADCs reduce the size of data streams in wearable devices. However, in the context of electrocardiogram (ECG) signals, such an event-driven data source results in a variable length two-dimensional (time-amplitude tuples) data vector for each ECG beat. It is difficult to apply many standard signal processing techniques to this data making classifiers more complex. In this paper we resolve these difficulties by mapping the variable length 2D vectors to a fixed length feature vector comprising the first 81 coefficients of a Chebyshev polynomial expansion of the ECG beat. We show that beat reconstruction based on these 81 coefficients results in an average RMS difference to the original beat of only ≈ 3.08%. Using these coefficients as the feature set input to a simple three-layered ANN binary (Normal / Abnormal) ECG classifier and we demonstrate 98.15% average accuracy and 96.07% average sensitivity. Using the same simple ANN structure we also constructed a 4-class ANN structure which achieved 98.80% average accuracy and 91.5% average sensitivity. Both these networks have only 20k parameters and outperform the state-of-the-art classifiers, enabling low-power edge computing.