Class Activation Mapping Method Research Articles

Toward an Interpretable CNN Model for the Classification of Lightning‐Produced VLF/LF Signals

AbstractAn interpretable convolutional neural network model is proposed for the classification of very low frequency and low frequency lightning electric field waveforms. This model adopts multi‐scale convolutional kernels and shortcut connections to enhance the ability of lightning waveform classification. Based on the data recorded from five provinces in China, the proposed model achieves an accuracy of 98.56% for a four‐type classification task including return strokes, the intra‐cloud lightning, preliminary breakdown, and narrow bipolar events. The proposed model is validated with another open‐source data set from Argentina with an accuracy of 98.45%, which shows good robustness. To ensure the classification, the features learned by the model are visualized. The class activation mapping (CAM) method is adopted to visualize the class‐specific contribution of different waveform parts by using the feature maps of the final convolutional layer. It is highlighted by the CAM method that the proposed model focuses on waveform parts that align with those areas of interests identified by human experts. The high‐contribution waveform parts are furtherly analyzed, which indicate that the proposed model possesses the capability to associate waveform features with the corresponding lightning discharge processes.

Classification of Sprain and Non-sprain Motion using Deep Learning Neural Networks for Ankle Sprain Prevention

A smart wearable ankle sprain prevention device would require an intelligent monitoring system that can classify data from the sensors as sprain or non-sprain motion. This paper aims to explore Deep Neural Network method, specifically the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) for classifying sprain and non-sprain motion. A study is conducted on 11 participants to record sprain and non-sprain motions, which are used to train and test the LSTM-FCN model and previously used Support Vector Machine (SVM) model. It has been demonstrated that the LSTM-FCN model is more accurate at classifying sprain and non-sprain motion. The LSTM-FCN also proved to be more useful as its architecture allows for the Class Activation Mapping (CAM) method to be employed. The CAM method allows for the identification of temporal regions of the time series that contribute most or least to the classification decision of the LSTMFCN. Visualizing the regions of high or low contribution makes it easy to see patterns in the data correlation with sprain motion and better understand why certain non-sprain data can be misclassified as sprain motion. Overall, LSTM-FCN is found to be a viable method for the classification of sprain and non-sprain motion.

Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping

Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (short-time Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.