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
.The dataset comprises of 89 ALS and 133 healthy EMG signals which have been taken from, dataset R002 at http:// www.emglab.net [17]
. It consists of two groups namely ALS and healthy
. The healthy EMG dataset has been taken from 10 people of 21-37 years age, among them, females are 4 and males are 6, they didn’t have any kind of neuromuscular disorders
. ALS dataset has been taken from 8 patients among them 4 are females and 4 are males whose age is between 35-67 years
. Among the 8 patients taken, 5 have died within a few years
. A standard needle electrode has been used for accession
. Audio and Visual feedback has been used for monitoring the signal quality [18]
. The EMG signal has been amplified at a frequency range of 5 Hz-5 kHz filter settings and sampled at a frequency of 10 kHz
. Figure 1 shows the block diagram of proposed method explaining the whole method stepwise. Figure 2 shows the example of each of the ALS and healthy EMG signals.
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