Deep learning for healthcare applications based on physiological signals: A review

Faust, Oliver; Hagiwara, Yuki; Hong, Tan Jen; Lih, Oh Shu; Acharya, U. Rajendra

doi:10.1016/j.cmpb.2018.04.005

Cited by 833 publications

(462 citation statements)

References 101 publications

(68 reference statements)

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“…Most previous EMG studies using deep learning, however, have approached the model selection and parameter optimization processes without statistical methods (i.e., a single run trial instead of cross validation [116]). Caution should therefore be taken when comparing the classification performances of proposed deep learning algorithms with more shallow learning conventional algorithms (e.g., LDA and SVM) which require a smaller training dataset and whose presentation has more commonly employed cross validation.…”

Section: Discussionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

198

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: Discussionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

198

105

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“…The synthesis of data was conducted in a simple way. Some of the methods used for synthesis were in accordance with other similar studies [63][64][65]. According to the gathered data, the most widely used deep learning method is convolutional neural networks (CNNs).…”

Section: Discussing the Resultsmentioning

confidence: 73%

“…These applications were not so represented in the past. Traditional approaches to various similarity measures are ineffective when compared to deep learning [63]. Based on these findings, it can be suggested that deep learning and deep neural networks will prevail, and that they will find many other uses in the near future.…”

Section: Research Questionsmentioning

confidence: 99%

Deep Learning and Medical Diagnosis: A Review of Literature

Bakator

Radosav

2018

MTI

398

159

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Abstract:In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

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“…field of image processing [3][4][5][6][7]. Similar performances have been achieved in the field of 25 speech recognition [8,9] and natural language processing [10,11]. 26 A steadily growing amount of work has been exploring the application of deep 27 learning approaches on physiological signals.…”

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confidence: 84%

“…Most of the reported 49 approaches consist of first transforming the processed EMG signal into a two 50 dimensional (time-frequency) visual representation (such as a spectrogram or a 51 scalogram) and subsequently using a deep CNN architecture to proceed with the 52 classification. A similar procedure has been used in [24] for the analysis of deep learning approaches applied to physiological signals can be found in [25] and [26]. 56 The current work focuses on the application of deep learning approaches for 57 nociceptive pain recognition based on physiological signals (EMG, ECG and 58 April 23, 2019 2/16 electrodermal activity (EDA)).…”

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confidence: 99%

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T 0 vs.T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

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Deep learning for healthcare applications based on physiological signals: A review

Abstract: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis.

Cited by 833 publications

References 101 publications

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Deep Learning and Medical Diagnosis: A Review of Literature

Exploring Deep Physiological Models for Nociceptive Pain Recognition

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