Key result
A proposed one-dimensional small convolutional neural network (1D-SCNN) implemented automatic detection of four types of arrhythmias for use in wearable ECG devices.
Why the study?
Hardware configurations of wearable ECG monitoring devices limit the application of large deep learning models, hindering their intelligence.
Does a 1D-SCNN accurately detect arrhythmias in ECG signals for use in wearable devices?
Does a 1D-SCNN accurately detect arrhythmias in ECG signals for use in wearable devices?
A proposed 1D-SCNN enables automatic detection of four types of arrhythmias, facilitating the development of intelligent wearable ECG devices.
Wearable ECG monitoring devices have been used in clinical applications, but the hardware configuration of wearable ECG monitoring devices limits the application of large deep learning models, which hinders the development of their intelligence. Research on the use of small convolutional neural networks for the detection of ECG signals is of interest for the development of wearable ECG devices as well as smart medicine. In this study, a one-dimensional small convolutional neural network (1D-SCNN) including five convolutional layers, four max pooling layers, a two-layer pooling module, and two fully connected layers is proposed. This study implements automatic detection of four types of arrhythmias and discusses the effects of single-path multilayer networks (depth) and multi-path networks (width) on the performance of the small convolutional neural network.
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Li et al. (2022) studied Arrhythmia. One-dimensional small convolutional neural network (1D-SCNN) was evaluated on Automatic detection of four types of arrhythmias. A proposed one-dimensional small convolutional neural network (1D-SCNN) implemented automatic detection of four types of arrhythmias for use in wearable ECG devices.
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