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Deep Learning Methods for Healthcare
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Deep Learning Methods for Healthcare

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 23088

Special Issue Editor

Prof. Dr. U Rajendra Acharya

grade E-Mail Website1 Website2
Guest Editor
1. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
4. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
5. School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, Australia
Interests: biomedical signal processing; bioimaging; data mining; visualization; biophysics for better health care design; drug delivery and therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Different biosignals, namely electrocardiogram (ECG), heart rate variability (HRV), electroencephalogram (EEG), electromyogram (EMG), phonocardiogram (PCG), blood pressure, and speech and photoplethysmogram (PPG), indicate the functioning of particular organs. Various medical images are used to decipher the health of the organ. Many machine learning algorithms have been developed to automatically detect diseases using various feature extraction methods from 1D and 2D signals.

Deep learning techniques like convolution neural networks (CNN), long short-term memory (LSTM), autoencoder, deep generative models, and deep belief networks have been applied for big data efficiently. The application of such novel methods to the medical data can aid clinicians in making an accurate and fast diagnosis. Thus, this Special Issue, entitled “Deep Learning Methods for Healthcare”, focuses on the application of new deep learning techniques that can be used to improve healthcare using big data.

Prof. U Rajendra Acharya
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Electrocardiogram
  • Deep learning
  • Convolutional neural network
  • Long short-term memory
  • Autoencoder
  • Heart rate variability
  • Electroencephalogram
  • Image
  • Healthcare

Published Papers (3 papers)

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Research

15 pages, 3250 KiB  
Article
Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning
by Zhichao Li, Jilin Huang and Zhiping Hu
Int. J. Environ. Res. Public Health 2019, 16(10), 1688; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16101688 - 14 May 2019
Cited by 12 | Viewed by 4909
Abstract
Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and [...] Read more.
Chronic pharyngitis is a common disease, which has a long duration and a wide range of onset. It is easy to misdiagnose by mistaking it with other diseases, such as chronic tonsillitis, by using common diagnostic methods. In order to reduce costs and avoid misdiagnosis, the search for an affordable and rapid diagnostic method is becoming more and more important for chronic pharyngitis research. Speech disorder is one of the typical symptoms of patients with chronic pharyngitis. This paper introduces a convolutional neural network model for diagnosis based on the typical symptom of speech disorder. First of all, the voice data is converted into a speech spectrogram, which can better output the speech characteristic information and lay a foundation for computer diagnosis and discrimination. Second, we construct a deep convolutional neural network for the diagnosis of chronic pharyngitis through the design of the structure, the design of the network layer, and the description of the function. Finally, we perform a parameter optimization experiment on the convolutional neural network and judge the recognition efficiency of chronic pharyngitis. The results show that the convolutional neural network has a high recognition rate for patients with chronic pharyngitis and has a good diagnostic effect. Full article
(This article belongs to the Special Issue Deep Learning Methods for Healthcare)
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21 pages, 3676 KiB  
Article
A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals
by Ozal Yildirim, Ulas Baran Baloglu and U Rajendra Acharya
Int. J. Environ. Res. Public Health 2019, 16(4), 599; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16040599 - 19 Feb 2019
Cited by 175 | Viewed by 10730
Abstract
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages [...] Read more.
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data. Full article
(This article belongs to the Special Issue Deep Learning Methods for Healthcare)
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9 pages, 807 KiB  
Article
Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data
by Seok-Jae Heo, Yangwook Kim, Sehyun Yun, Sung-Shil Lim, Jihyun Kim, Chung-Mo Nam, Eun-Cheol Park, Inkyung Jung and Jin-Ha Yoon
Int. J. Environ. Res. Public Health 2019, 16(2), 250; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16020250 - 16 Jan 2019
Cited by 71 | Viewed by 6671
Abstract
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models [...] Read more.
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both p < 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process. Full article
(This article belongs to the Special Issue Deep Learning Methods for Healthcare)
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