Discrete cosine transform (DCT) is used to extract the features from one of the MUAPs having a dynamic range, and the obtained feature set has been given as input to the k-Nearest Neighbor classifier (k-NN) to classify EMG signals [15]
. The coefficients obtained from autoregressive analysis have been given as input to neuro-fuzzy system for classifying EMG signals [16].
In this paper, tunable-Q factor wavelet transform (TQWT) based features are extracted for the classification of ALS and healthy EMG signals
. TQWT decomposes EMG signal into subbands and features such as mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy are extracted from the sub-bands
. The obtained statistical features are fed as inputs to k-NN and LS-SVM classifiers to classify ALS and healthy EMG signals
. The organization of the remaining paper is as follows: Section
. 2 contain, ALS and healthy EMG signals dataset, the proposed methodology- TQWT, and features extraction will present
. The results and discussions of classifying ALS and healthy EMG signals are in Section 3 and Section 4 conclude the paper.
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