ECG classification algorithm based on feature synthetic input and multi-resolution neural networks
Authors: 李飞鹏
Date: 2022-03-13 00:00:00
Proceeding: 无 2022 3
Tasks: 2D Object Detection, Classification, ECG Classification

Heart disease is a serious threat to human health. As most heart diseases are 
characterized by intermittent attacks with low frequency, long-term electrocardiogram 
(ECG) analysis has become an important means of detecting heart disease clinically. 
With the popularity of smart mobile devices and the development of artificial 
intelligence, it has become possible to use mobile devices to automatically classify ECG 
in real-time.
With the smart mobile devices as the carrier, high-performance inter-patient and 
patient-specific real-time ECG classification methods based on Convolutional Neural 
Network (CNN) are proposed, which focus on solving the problems of high complexity 
of data preprocessing and low detection accuracy of arrhythmia. Furthermore, 
considering that the calculation amount and computational energy consumption of 
CNN are generally large, this dissertation studies and proposes a CNN accelerator 
architecture, which effectively improves the operation performance of the algorithms
and reduces the power consumption. Besides, to fully fit the multi-application scenario 
of smart mobile devices, the proposed accelerator architecture also supports the 
acceleration of a variety of common CNN models.
The main contents and characteristics of this dissertation are as follows:
1. Research of ECG Classification method based on feature-synthesis inputs 
and multi-resolution neural network. For inter-patient ECG classification, a CNN￾based method exploiting multi-resolution using atrous convolution is proposed. ECG 
morphological features, RR-interval and historical classified information are combined 
to form the informative network inputs, and then filters with different receptive fields 
are used to extract multi-resolution features automatically, thus helping the network to 
recognize different classes of heartbeats. Evaluated with MIT-BIH arrhythmia database, 
this method achieves a detection accuracy of 92.9% in arrhythmia classes.
2. Research of ECG Classification method based on folding morphological 
V
浙江大学博士学位论文 Abstract
features and channel-wise attentional neural network. For patient-specific ECG 
classification, a method exploiting CNN with channel-wise attention is proposed. Twochannel ECG segments are folded into six-channel segments as network inputs, and 
parallel convolution layers are used to discretize and combine multi-channel features. 
Furthermore, the channel-wise attention model automatically emphasize the 
informative features that help distinguish different classes of heartbeats and thus 
significantly enhance the classification performance. Validated on the MIT-BIH 
arrhythmia database, this method demonstrates a classification accuracy of 98.0%.
3. Research of energy-efficient CNN accelerator architecture suitable for 
smart mobile devices. TruthSeek, an energy-efficient instruction-driven accelerator 
architecture, is proposed, which can effectively support the acceleration of various 
CNN models including the ECG classification algorithms. TruthSeek realizes the 
merging operation of convolution and pooling layers by customizing the novel 
instruction set and register, thus effectively reducing the power consumption of memory 
access. At the same time, network sparsity is exploited to further reduce operation 
cycles and improve performance. Validated on 5 different types of CNN models with 
calculations ranging from 0.4M to 3.8G, TruthSeek accelerator only requires an average 
of 0.057 to 65.6ms for every inference, and obtains a task efficiency of 0.06 to 0.42 
GOPS/mW, which indicates its application potential in the field of mobile devices.
The key methods and technologies proposed in this dissertation have positive 
research significance and application value for solving the real-time ECG classification 
problem required by heart disease monitoring in mobile scenes. And the proposed CNN 
acceleration architecture has certain reference significance for accelerators in other 
fields.
key words: feature-synthesis; multi-resolution; morphological features folding;
channel-wise attention; ECG classification; CNN; energy-efficient acceleration 
architecture; instruction set