An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

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Abstract Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades.However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals.Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by Fiber/Bran Supplements arrhythmia.Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed.We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects.

The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet.A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets.An extreme learning machine was used as a classifier in the proposed algorithm.Results A performance evaluation was conducted with the MIT-BIH arrhythmia database.The results showed a high sensitivity of 97.

51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%.Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no Mountaineering - Homme - Chaussures intrasubject between the training and evaluation datasets.

And it significantly reduces the amount of intervention needed by physicians.