Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit

Urtnasan, Erdenebayar; Kim, Yeewoong; Park, Joung-Uk; Lee, SooYong; Lee, Kyoung-Joung

doi:10.5391/ijfis.2020.20.3.181

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Deep learning algorithms were not applicable due to the small number of observations of sadness and anxiety. After collecting more data, advanced methods such as deep learning algorithms (86)(87)(88)(89)(90) and Bayesian optimization (86) would be applicable in the future to optimize prediction models and the parameters.…”

Section: Limitationsmentioning

confidence: 99%

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

Ono

Warita

et al. 2022

Front. Psychiatry

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In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

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Section: Limitationsmentioning

confidence: 99%

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

Ono

Warita

et al. 2022

Front. Psychiatry

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“…GRU is a variant of LSTM, whose structure is further simplified to show better performance than LSTM in smaller datasets [26,27]. Cao et al use the BiGRU model to solve the problem of distribution discrepancy [28].…”

Section: Prediction Modelmentioning

confidence: 99%

SACGNet: A Remaining Useful Life Prediction of Bearing with Self-Attention Augmented Convolution GRU Network

Duan

Chen³

et al. 2022

Lubricants

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In recent years, the development of deep learning-based remaining useful life (RUL) prediction methods of bearings has flourished because of their high accuracy, easy implementation, and lack of reliance on a priori knowledge. However, there are two challenging issues concerning the prediction accuracy of existing methods. The run-to-failure sequential data and its RUL labels are almost inaccessible in real-world scenarios. Meanwhile, the existing models usually capture the general degradation trend of bearings while ignoring the local information, which restricts the model performance. To tackle the aforementioned problems, we propose a novel health indicator derived from the original vibration signals by combining principal components analysis with Euclidean distance metric, which was motivated by the desire to resolve the dependency on RUL labels. Then, we design a novel self-attention augmented convolution GRU network (SACGNet) to predict the RUL. Combining a self-attention mechanism with a convolution framework can both adaptively assign greater weights to more important information and focus on local information. Furthermore, Gated Recurrent Units are used to parse the long-term dependencies in weighted features such that SACGNet can utilize the important weighted features and focus on local features to improve the prognostic accuracy. The experimental results on the PHM 2012 Challenge dataset and the XJTU-SY bearing dataset have demonstrated that our proposed method is superior to the state of the art.

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“…Deep neural networks (DNNs) have made great successes in various formerly difficult tasks such as vision, speech, natural language, and games [1][2][3][4][5]. They have many layers and parameters and thus require huge computing resources, resulting in high energy consumption.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Spiking Neural Networks

Han¹,

Lee²

2021

IJFIS

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Spiking neural networks (SNNs) have attracted attention as the third generation of neural networks for their promising characteristics of energy-efficiency and biological plausibility. The diversity of spiking neuron models and architectures have made various learning algorithms developed. This paper provides a gentle survey of SNNs to give an overview of what they are and how they are trained. It first presents how biological neurons works and how they are mathematically modelled specially in differential equations. Next it categorizes the learning algorithms of SNNs into groups and presents how their representative algorithms work. Then it briefly describe the neuromorphic hardware on which SNNs run.

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Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit

Cited by 8 publications

References 14 publications

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

SACGNet: A Remaining Useful Life Prediction of Bearing with Self-Attention Augmented Convolution GRU Network

A Survey on Spiking Neural Networks

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