Amyotrophic lateral sclerosis (ALS) is a disease, affects the nerve cells in brain and spinal cord that controls the voluntary action of muscles, which identification can be possible by processing electromyogram (EMG) signals
. This study focuses on the extraction of features based on tunable-Q factor wavelet transform (TQWT) for classifying ALS and healthy EMG signals
. TQWT decomposes EMG signal into sub-bands and these sub-bands are used for extraction of statistical features namely mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy
. The obtained features are tested on k-Nearest Neighbour and least squares support vector machines classifiers for the classification of ALS and healthy EMG signals
. The proposed method obtained better classification results as compared to other existing methods.
Amyotrophic lateral sclerosis (ALS) is a disease, affects the nerve cells in brain and spinal cord that controls the voluntary action of muscles, which identification can be possible by processing electromyogram (EMG) signals
. This study focuses on the extraction of features based on tunable-Q factor wavelet transform (TQWT) for classifying ALS and healthy EMG signals
. TQWT decomposes EMG signal into sub-bands and these sub-bands are used for extraction of statistical features namely mean absolute deviation (MAD), interquartile range (IQR), kurtosis, mode, and entropy
. The obtained features are tested on k-Nearest Neighbour and least squares support vector machines classifiers for the classification of ALS and healthy EMG signals
. The proposed method obtained better classification results as compared to other existing methods.
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