TY - JOUR
T1 - Augmentation in healthcare
T2 - Augmented biosignal using deep learning and tensor representation
AU - Ibrahim, Marwa
AU - Wedyan, Mohammad
AU - Alturki, Ryan
AU - Khan, Muazzam A.
AU - Al-Jumaily, Adel
N1 - Publisher Copyright:
© 2021 Marwa Ibrahim et al.
PY - 2021/1/27
Y1 - 2021/1/27
N2 - In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.
AB - In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85100640732&partnerID=8YFLogxK
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U2 - 10.1155/2021/6624764
DO - 10.1155/2021/6624764
M3 - Article
C2 - 33575018
AN - SCOPUS:85100640732
SN - 2040-2295
VL - 2021
SP - 1
EP - 9
JO - Journal of Healthcare Engineering
JF - Journal of Healthcare Engineering
M1 - 6624764
ER -