5th International Conference on Computing and Informatics (ICCI’2022) : Proccedings, March 09 - 10, 2022, New Cairo, Egypt
Издателство
Future University in Egypt
Местоиздаване
New Cairo, Egypt
Година на издаване
2022
Страници
pp. 374-377
Допълнителна информация
ISBN
ISBN 978-166549972-9
Забележка
Авт.: Nahla F. Abdel-Maboud, Silvia Stoyanova Parusheva, Marco Alfonse, Abdel-Badeeh M. Salem ; DOI: 10.1109/ICCI54321.2022.9756080
Анотация
Machine learning methods can be used to diagnose neuromuscular illnesses using electromyographic (EMG) signals. This research examines the tunable-Q factor wavelet transform (TQWT) for feature extraction and analyses various learning methods for classifying EMG signals in order to detect neuromuscular diseases. TQWT decomposes each type of EMG signal into sub-bands first. From each sub-band, statistical parameters such as mean absolute values (MAV), inter quartile range (IQR), kurtosis, mode, standard deviation, skewness, and ratio are calculated. Finally, the extracted features are fed into classifiers to differentiate between ALS, myopathy, and normal EMG data. The random forest classifier with TQWT achieved higher classification results in neuromuscular disorders diagnosis than the other classifiers tested in this study, according to experimental results. The accuracy of the random forest approach using TQWT was 98.64%, with an F-measure of 0.986 and a kappa value of 0.979.
Abdel-Maboud, N. Comparative Study of Machine Learning Techniques Based on TQWT for EMG Signal Classification. New Cairo, Egypt: Future University in Egypt, 2022 pp. 374-377, ISBN 978-166549972-9.
Abdel-Maboud, N. Comparative Study of Machine Learning Techniques Based on TQWT for EMG Signal Classification. New Cairo, Egypt: Future University in Egypt, 2022 pp. 374-377, ISBN 978-166549972-9.
Abdel-Maboud, N. (2022) Comparative Study of Machine Learning Techniques Based on TQWT for EMG Signal Classification, New Cairo, Egypt: Future University in Egypt pp. 374-377, ISBN 978-166549972-9.
Abdel-Maboud, N. (2022). Comparative Study of Machine Learning Techniques Based on TQWT for EMG Signal Classification. New Cairo, Egypt: Future University in Egypt, pp. 374-377.
Abdel-Maboud N. Comparative Study of Machine Learning Techniques Based on TQWT for EMG Signal Classification. New Cairo, Egypt: Future University in Egypt; 2022. p. pp. 374-377. ISBN: ISBN 978-166549972-9.
