1 research outputs found
Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network
Objective: A novel structure based on channel-wise attention mechanism is
presented in this paper. Embedding with the proposed structure, an efficient
classification model that accepts multi-lead electrocardiogram (ECG) as input
is constructed. Methods: One-dimensional convolutional neural networks (CNN)
have proven to be effective in pervasive classification tasks, enabling the
automatic extraction of features while classifying targets. We implement the
Residual connection and design a structure which can learn the weights from the
information contained in different channels in the input feature map during the
training process. An indicator named mean square deviation is introduced to
monitor the performance of a particular model segment in the classification
task on the two out of the five ECG classes. The data in the MIT-BIH arrhythmia
database is used and a series of control experiments is conducted. Results:
Utilizing both leads of the ECG signals as input to the neural network
classifier can achieve better classification results than those from using
single channel inputs in different application scenarios. Models embedded with
the channel-wise attention structure always achieve better scores on
sensitivity and precision than the plain Resnet models. The proposed model
exceeds the performance of most of the state-of-the-art models in ventricular
ectopic beats (VEB) classification, and achieves competitive scores for
supraventricular ectopic beats (SVEB). Conclusion: Adopting more lead ECG
signals as input can increase the dimensions of the input feature maps, helping
to improve both the performance and generalization of the network model.
Significance: Due to its end-to-end characteristics, and the extensible
intrinsic for multi-lead heart diseases diagnosing, the proposed model can be
used for the real-time ECG tracking of ECG waveforms for Holter or wearable
devices