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

Hwang, Byung Keun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

doi:10.1089/tmj.2017.0250

Cited by 102 publications

(87 citation statements)

References 12 publications

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

171

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

171

View full text Add to dashboard Cite

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“…In the experiment, subjects were given vigilance tasks, and if the response time was longer than 500 ms, it was labeled as lack of vigilance [30]. Meanwhile, several methods using HRV parameter and CNN analysis for raw-ECG signals have been applied [25].…”

Section: Related Workmentioning

confidence: 99%

“…Conventionally, the HRV has been utilized as a feature of ECG signals [19,25]. However, it is not easy to extract meaningful HRV parameters with short-term, 10 s ECG data.…”

Section: Feature Equationmentioning

confidence: 99%

“…In healthcare studies, the convolution neural network (CNN) and autoencoder approach have been widely used to extract features from physiological signals [23,24]. Hwang et al proposed ultrashort-term segmentation of the ECG signal to estimate stressful conditions [25]. While deep learning approaches have outperformed conventional methods for healthcare data analytics, a critical limitation has been encountered.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

et al. 2020

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A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources.

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“…The naive Bayes algorithm, which has been utilized to predict sleep apnea severity and sleepiness [20], was trained based on demographics and polysomnogram and electrocardiogram EEG signals. However, several conventional machine learning algorithms depend on feature engineering, which is based mostly on predefined and handcrafted models and thus could be suboptimal for nonstationary and nonlinear biosignal data [21].…”

Section: Introductionmentioning

confidence: 99%

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

et al. 2019

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Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.

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

Cited by 102 publications

References 12 publications

Deep Learning in Physiological Signal Data: A Survey

Deep Learning in Physiological Signal Data: A Survey

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy

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