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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 (16 November 2020) | Viewed by 43642

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Special Issue Editors

Dr. Oliver Faust

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Guest Editor

College of Business, Technology & Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
Interests: artificial intelligence; formal methods; biomedical signal processing; service based healthcare; intelligent internet of medical things
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. U Rajendra Acharya

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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 such as electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) are indicative of neural system function. Medical images, acquired with computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET), can also be used to gather information about the functioning of brain. Based on this information, it is possible to monitor and diagnose a wide range of neurological disorders, including Parkinson’s disease, Alzheimer’s disease, autism, brain tumors, brain cancer, epilepsy, schizophrenia, mitochondrial dysfunction, attention deficit hyperactivity disorder (ADHD), movement disorders, multiple sclerosis, myopathy, neurodegenerative diseases, neuromuscular disorders, neuropsychiatry, neuropsychology, pain, sleep stages, sleep disorders, stroke, and other neurological diseases. Machine learning algorithms have been developed to the disease detection using various feature extraction methods from 1D and 2D signals.

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

Dr. Oliver Faust
Prof. U Rajendra Acharya
Guest Editors

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Keywords

healthcare
physiological signals
electroencephalography
electrooculography
electromyography
image processing
computed tomography
magnetic resonance imaging
ultrasound
positron emission tomography
deep learning
autoencoder
convolutional neural network
long short-term memory

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Published Papers (5 papers)

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Research

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29 pages, 540 KiB

Open AccessFeature PaperArticle

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

by Manish Sharma, Jainendra Tiwari and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(6), 3087; https://doi.org/10.3390/ijerph18063087 - 17 Mar 2021

Cited by 63 | Viewed by 5667

Abstract

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects. Full article

(This article belongs to the Special Issue Application of Deep Learning for Neural Systems)

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13 pages, 2521 KiB

Open AccessArticle

Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

by Tianqi Zhu, Wei Luo and Feng Yu

Int. J. Environ. Res. Public Health 2020, 17(11), 4152; https://doi.org/10.3390/ijerph17114152 - 10 Jun 2020

Cited by 96 | Viewed by 6607

Abstract

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods. Full article

(This article belongs to the Special Issue Application of Deep Learning for Neural Systems)

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Review

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29 pages, 6270 KiB

Open AccessReview

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

by Vidhya V., Anjan Gudigar, U. Raghavendra, Ajay Hegde, Girish R. Menon, Filippo Molinari, Edward J. Ciaccio and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(12), 6499; https://doi.org/10.3390/ijerph18126499 - 16 Jun 2021

Cited by 38 | Viewed by 6177

Abstract

Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis. Full article

(This article belongs to the Special Issue Application of Deep Learning for Neural Systems)

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33 pages, 35758 KiB

Open AccessFeature PaperReview

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

by Afshin Shoeibi, Marjane Khodatars, Navid Ghassemi, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Maryam Panahiazar, Fahime Khozeimeh, Assef Zare, Hossein Hosseini-Nejad, Abbas Khosravi, Amir F. Atiya, Diba Aminshahidi, Sadiq Hussain, Modjtaba Rouhani, Saeid Nahavandi and Udyavara Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(11), 5780; https://doi.org/10.3390/ijerph18115780 - 27 May 2021

Cited by 236 | Viewed by 19446

Abstract

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated. Full article

(This article belongs to the Special Issue Application of Deep Learning for Neural Systems)

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18 pages, 833 KiB

Open AccessReview

A Smart Service Platform for Cost Efficient Cardiac Health Monitoring

by Oliver Faust, Ningrong Lei, Eng Chew, Edward J. Ciaccio and U Rajendra Acharya

Int. J. Environ. Res. Public Health 2020, 17(17), 6313; https://doi.org/10.3390/ijerph17176313 - 30 Aug 2020

Cited by 23 | Viewed by 4681

Abstract

Aim: In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. Subject and Methods: There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. Results: Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. Conclusion: Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers. Full article

(This article belongs to the Special Issue Application of Deep Learning for Neural Systems)

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