Key result
Multistage deep learning model using biosignal spectrograms improves accuracy over other approaches.
Why the study?
Shallow learning feature extraction techniques depend on user experience, whereas deep learning can automatically extract features from raw data to save time and effort for healthcare practitioners.
A proposed multistage deep learning model using spectrograms and data augmentation improves the classification accuracy of biosignal datasets.
Should not yet alter clinical practice; extends preliminary deep learning approaches for biosignal classification.
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.
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Ibrahim et al. (2021) studied Biosignal classification. Multistage deep learning model based on biosignal spectrogram and tensor representation vs. Different approaches was evaluated on Testing and training accuracy. A multistage deep learning model based on biosignal spectrograms and tensor representation improved testing and training accuracy compared to other approaches.
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