1 Department of Computer Science and Engineering, Seoul National University , Seoul, Korea.
2 Department of Computer Engineering, Kwangwoon University , Nowon-gu, Seoul, Korea.
Telemed J E Health. 2018 Oct;24(10):753-772. doi: 10.1089/tmj.2017.0250. Epub 2018 Feb 8.
Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods.
The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions.
The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method.
We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.
使用心电图（ECG）信号进行压力识别需要棘手的长期心率变异性（HRV）参数提取过程。本研究提出了一种新的深度学习框架，即 Deep ECGNet，可使用超短期原始 ECG 信号进行识别，而无需任何特征工程方法。
通过对 ECG 波形的各种实验和分析，开发了 Deep ECGNet。我们提出了最优的递归和卷积神经网络架构，以及最优的卷积滤波器长度（与 ECG 的 P、Q、R、S 和 T 波持续时间有关）和池化长度（与心跳周期有关），基于对 ECG 信号的波形特征的优化实验和分析。还使用 HRV 参数和频率特征作为基准测试，与传统方法进行了实验比较。本研究使用的数据来自韩国光云大学（13 名受试者，案例 1）和比利时鲁汶天主教大学（9 名受试者，案例 2）。根据各种实验方案设计了实验，以诱发紧张条件。
提出的识别压力条件的框架，即 Deep ECGNet，在案例 1 中的准确率最高为 87.39%，在案例 2 中的准确率最高为 73.96%，分别比传统 HRV 方法提高了 16.22%和 10.98%。
我们提出了一种用于压力识别的最优深度学习架构及其参数，以及基于原始 ECG 数据的周期性模式设计深度学习结构的理论考虑。本研究的实验结果证明，所提出的深度学习模型 Deep ECGNet 是一种使用超短期 ECG 数据识别压力条件的最优结构。
