Low computational cost classifiers for ECG diagnosis using neural networks

This work investigates and compares a number of computationally efficient classifiers for categorising the Frank lead ECG as normal or one of six disease conditions using a neural network expert system and results are comparable to those derived from neural nets trained with over 229 scalar parameters.

Abstract

We investigate and compare a number of computationally efficient classifiers for categorising the Frank lead ECG as normal or one of six disease conditions using a neural network expert system. These include a power spectral density estimate, and two discrete wavelets, the Daubechies wavelet of order 10 (db 10) and the Symlet wavelet of order 8 (sym8) applied to a single beat of the X, Y and Z Frank leads. Simple statistical parameters derived from these transforms and from reconstructed filtered signals were used as inputs to a neural network with no hidden units and a softmax output stage. We used multiple runs of 10 fold cross validation to obtain estimates of classifier performance. Best results were obtained for the db 10 parameters when age and sex were also added. Overall accuracy was 68.8/spl plusmn/0.6%. These results are comparable to those derived from neural nets trained with over 229 scalar parameters (70.9/spl plusmn/0.6%) and were derived at much lower computational cost. The methods derived can be easily implemented in real time using a DSP processor.