Smart-phone based electrocardiogram wavelet decomposition and neural network classification

7th Sensors & their Applications

Jannah, N.^1 ; Hadjiloucas, S.^1 ; Hwang, F.^1 ; Galvão, R.K.H.^2

School of Systems Engineering, University of Reading, United Kingdom^1

Divisão de Engenharia Eletrônica, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, 12228-900, Brazil^2

关键词: Classification algorithm;    Continuous time recurrent neural networks;    Ecg classifications;    Generalization ability;    Mobile applications;    Neural network classification;    Neural network classifier;    Reduced memory requirements;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/450/1/012019/pdf
DOI  :  10.1088/1742-6596/450/1/012019

来源: IOP

【 摘 要 】

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.