Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

BACKGROUND 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. 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. RESULTS 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. CONCLUSIONS 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.

[1]  J. Woo,et al.  Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[2]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[3]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[4]  Sylvia D. Kreibig,et al.  Autonomic nervous system activity in emotion: A review , 2010, Biological Psychology.

[5]  S. Huffel,et al.  Influence of Mental Stress on Heart Rate and Heart Rate Variability , 2009 .

[6]  Yixin Chen,et al.  Multi-Scale Convolutional Neural Networks for Time Series Classification , 2016, ArXiv.

[7]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[8]  S. Gepshtein,et al.  EEG Gamma Band Oscillations Differentiate the Planning of Spatially Directed Movements of the Arm Versus Eye: Multivariate Empirical Mode Decomposition Analysis , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[10]  C. Hoh,et al.  Impact of acute mental stress on sympathetic nerve activity and regional blood flow in advanced heart failure: implications for 'triggering' adverse cardiac events. , 1997, Circulation.

[11]  Yann LeCun,et al.  Learning Invariant Feature Hierarchies , 2012, ECCV Workshops.

[12]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[13]  Xiaoli Li,et al.  Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition , 2015, IJCAI.

[14]  Xiang Bai,et al.  An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[16]  Sethuraman Panchanathan,et al.  Multimodal emotion recognition using deep learning architectures , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[17]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[18]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[19]  Sazali Yaacob,et al.  DETECTION OF HUMAN STRESS USING SHORT-TERM ECG AND HRV SIGNALS , 2013 .

[20]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.