Deep Learning in Physiological Signal Data: A Survey

Rim, Beanbonyka; Sung, Nak-Jun; Min, Se Dong; Hong, Min

doi:10.3390/s20040969

Cited by 143 publications

(80 citation statements)

References 148 publications

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“…Deep learning that is one of AI methods help us obtain a 100 higher accuracy on these classification. Latest scientific research referring to deep learning in physiological 1D signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG) shows its high potential [12]. Hou integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for ECG arrhythmias classification.…”

Section: Related Workmentioning

confidence: 99%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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Abstract The displacement can be calculated based on the integrated value of the acceleration signal waveform obtained by the acceleration sensor or gyroscope. However, this method is not effective in accurate measurement. Although some studies have improved the method of calculating accurate distance values by overcoming the effects of sensor noise or integration delay, the evaluation is still affected by sensor accuracy and environment. However, there are some special displacements, such as the chest compression. The displacement is a reciprocating motion and will return to the starting point again. Therefore, the acceleration waveform changes have obvious characteristics in the two stages from moving to the equilibrium position and returning to the starting point. Therefore, we propose an embedded classification method based on one-dimensional Convolutional Neural Network (CNN), which directly learns from the data of chest compressions and performs the signal formed by the Classification, distinguish the signal waveform under the standard pressing distance, so as to replace the calculation of distance measurement, and is not affected by factors such as pressure occlusion and electromagnetic wave interference, and has certain practical value on site. We tagged compressions and collected data from the simulator. The experiment evaluates the proposed CNN structure, and compares the classification results of the sample data with several CNN networks and SVMs with different structures in the literature. The results show that with sufficient training, the proposed 1D-CNN method can achieve an accuracy rate of more than 95%, and balances the accuracy rate and the hardware requirements.

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Section: Related Workmentioning

confidence: 99%

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Zhao¹,

Yu²,

Ye³

et al. 2020

Preprint

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show abstract

“…The DL models are capable of learning representations of the key features and interactions from the data itself, through direct feature learning in a supervised manner [28]. We hypothesize that applying DL approaches may allow the unlocking of information in PSM signals that is key to the detection of CSAs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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“…Currently, there is an increasing interest in deep learning models that are composed of multiple processing layers of inference using data representations with multiple levels of abstraction. In discriminating physiological signals, Convolutional Neural Networks (CNN) become the leading deep learning architectures due to their regularization structure and degree of translation invariance [7], yielding an outstanding ability in transferring knowledge between apparently different tasks of classification [8,9]. Thus, CNN models are useful in learning features related to brain imaging and neuroscience discovery [10].…”

Section: Introductionmentioning

confidence: 99%

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

et al. 2020

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Interpretation of brain activity responses using motor imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra-and inter-subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with an enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. We evaluate two labeled scenarios of MI tasks: bi-class and three-class. Obtained results in an MI database show that the thresholding strategy combined with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with a differentiated behavior of rhythms µ and β.

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Deep Learning in Physiological Signal Data: A Survey

Cited by 143 publications

References 148 publications

A Classification Method for Judging the Depth of Chest Compression Based on CNN

A Classification Method for Judging the Depth of Chest Compression Based on CNN

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification

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