• T Ananda Babu
• P Rajesh Kumar

The classification of multichannel uterine magnetomyography signals attempted using discrete wavelet transform and genetic algorithm. The uterine magnetomyographic signals analyzed in this research for the detection of term labor. The MMG signals of Physionet mmgdb database decomposed with six level discrete wavelet transform. The features extracted are energy, waveform length, standard deviation, entropy and variance from the discrete wavelet transform coefficients. Significant features selected using genetic algorithm. The features are fed to different classifiers for the labor assessment. The performance of classifier calculated by using different mother wavelets. The support vector machine classifier trained with GA selected features is good for classifying the pregnancy and labor with an accuracy of 95.9425%. The experimental results obtained will be helpful in term labor monitoring.

Discrete wavelet transform, Labor prediction, Uterine magnetomyography, Genetic Algorithm

"Classification of Magnetomyography Signals using Discrete Wavelet Transform and Genetic Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.563-568, May-2019, Available :http://www.jetir.org/papers/JETIR1905089.pdf

"Classification of Magnetomyography Signals using Discrete Wavelet Transform and Genetic Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp563-568, May-2019, Available at : http://www.jetir.org/papers/JETIR1905089.pdf