Real-Time Discrimination of Multiple Cardiac Arrhythmias for Wearable Systems Based on Neural Networks

This paper aims at developing a wearable system able to recognize the most significant cardiac arrhythmias through an efficient algorithm, in terms of low computational cost and memory usage, implementable in a portable, real-rime hardware. In addition, it must respect the specifications of good specificity and sensitivity, in order to permit a positive clinical validation. The hardware is constituted of a general propose microcontroller, which is able to acquire electro-cardiogram signal (ECG), perform analog to digital conversion and extract QRS complex. The algorithm classifies QRS complexes as normal or pathologic by means of selected features obtained from discrete fourier transform (DFT). Furthermore, a spatial wavelet pre-filter is also investigated to obtain an enhanced QRS complex discrimination. In particular, pattern recognition of QRS complex is performed from binding minimal architecture of neural network as Kohonen self organizing map (KSOM). Experimental results were validated by means of MIT-BIH arrhythmias database obtaining specificity and sensitivity up to 98%.