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

By Oliver Faust, Yuki Hagiwara, T. Hong, Shu Lih, Rajendra Acharya. Comput. Methods Programs Biomed., 2018

935 citations recorded by the source corpus.

BACKGROUND AND OBJECTIVE We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. METHODS An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. RESULTS During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. CONCLUSIONS 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.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/06ec154c2622fc2d6828e315da15210dd807684b

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In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

U-Sleep: resilient high-frequency sleep staging (2021, methodology): “Deep learning 23 is becoming increasingly popular for the analysis of physiological time-series in general 24 and has already been successfully applied to sleep staging 25 – 27 .”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, methodology): “DL handles complex, raw data by employing artificial neural networks (ANNs; computer programs that resemble the way a human brain thinks) that process data through multiple “ hidden ” layers [13, 38, 46].”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “Uses of deep learning, including ANNs, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), in biological signal processing, including EEG, electromyography, and electrocardiography signals, were also reviewed in [FHH + 18].”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “They were initially presented for image processing applications but have recently been adopted to one and two-dimensional architectures for diagnosis and prediction of diseases using biological signals [27].”
Deep learning for time series classification: a review (2018, background): “Finally, a recent review of deep learning for physiological signals classification revealed that CNNs were the most popular architecture (Faust et al., 2018) for the considered task.”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “Traditional approaches to various similarity measures are ineffective when compared to deep learning [63].”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, methodology): “Deep learning is becoming increasingly popular for the analysis of physiological time-series [Faust et al., 2018] and has already been applied to sleep staging [Robert et al., 1998, Ronzhina et al., 2012, Faust et al., 2019].”
AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators (2020, background): “Advances in deep learning (DL) have become the main drivers of revolutions in various commercial and enterprise applications, such as computer vision [1–3], social network [4– 6], ﬁnancial data analysis [7–9], healthcare [10–12] and sci-entiﬁc computing [13–15].”