Contribution of Application of Deep Learning Approaches on Biomedical Data in the Diagnosis of Neurological Disorders: A Review on Recent Findings

Recent developments in Artificial intelligence and their applications in Medical health diagnosis have shown promising results especially in the diagnosis of brain disorders, thereby paving the way for better healthcare and early detection of Neural disorders. Several diagnostic tests are routinely performed on humans to evaluate the nature of brain signals and images. These diagnostic tests typically rely on the physiological signals and neuroimaging analysis commonly performed by an expert manually. Large databases of clinical data of recorded brain signals and images are available. Secondary use of these medical records for precision medicine and predictive analysis involves machine learning which has fueled the excitement of medical experts and data scientists equally. For an effective medical diagnosis, intelligent behavior is the prerequisite for learning, and there has been a deep insight in this particular area for small diagnostic problems. Symbolic learning, statistical methods and neural networks in a broader perspective have contributed to the development of machine learning in general and medical diagnosis in particular. Deep learning, a recent trend in machine learning has been able to produce exceptionally accurate results in the diagnosis and treatment of various diseases. This review article covers the exciting contributions of the many researchers involved in this field and highlights the challenges considered critical in this area of research.

[1]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[2]  Ram Bilas Pachori,et al.  Classification of ictal and seizure-free EEG signals using fractional linear prediction , 2014, Biomed. Signal Process. Control..

[3]  Sidong Liu,et al.  Early diagnosis of Alzheimer's disease with deep learning , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[4]  Srishti Grover,et al.  Predicting Severity Of Parkinson’s Disease Using Deep Learning , 2018 .

[5]  Paul Sajda,et al.  Machine learning for detection and diagnosis of disease. , 2006, Annual review of biomedical engineering.

[6]  Mustafa Talha Avcu,et al.  Seizure Detection Using Least Eeg Channels by Deep Convolutional Neural Network , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[8]  Carlo Combi,et al.  Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes , 2015, Artif. Intell. Medicine.

[9]  M. Omair Ahmad,et al.  Deep Structural and Clinical Feature Learning for Semi-Supervised Multiclass Prediction of Alzheimer’s Disease , 2018, 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS).

[10]  Richard Walker,et al.  PD Disease State Assessment in Naturalistic Environments Using Deep Learning , 2015, AAAI.

[11]  Sterling C. Johnson,et al.  Predicting Alzheimer’s disease progression using multi-modal deep learning approach , 2019, Scientific Reports.

[12]  Ihsan Ullah,et al.  An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach , 2018, Expert Syst. Appl..

[13]  Ayman El-Baz,et al.  Alzheimer's disease diagnostics by adaptation of 3D convolutional network , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[14]  Fenglong Ma,et al.  A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[15]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[16]  Seunggyun Ha,et al.  Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging , 2017, NeuroImage: Clinical.

[17]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[18]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[19]  Héléna A. Gaspar,et al.  Generative Topographic Mapping (GTM): Universal Tool for Data Visualization, Structure‐Activity Modeling and Dataset Comparison , 2012, Molecular informatics.

[20]  Robert Koprowski,et al.  Machine learning, medical diagnosis, and biomedical engineering research - commentary , 2014, BioMedical Engineering OnLine.

[21]  M. Breteler,et al.  Epidemiology of Parkinson's disease , 2006, The Lancet Neurology.

[22]  for the Alzheimer’s Disease Neuroimaging Initiative.  Predicting Alzheimer’s disease progression using multi-modal deep learning approach , 2019 .

[23]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[24]  Muralidhar G. Bairy,et al.  Nonlinear Analysis of Physiological Signals: A Review , 2012 .

[25]  U. Raghavendra,et al.  A deep learning approach for Parkinson’s disease diagnosis from EEG signals , 2018, Neural Computing and Applications.

[26]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[27]  Md. Mohaimenul Islam,et al.  Potentiality of deep learning application in healthcare , 2018, Comput. Methods Programs Biomed..

[28]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[29]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[30]  Xiaojun Bi,et al.  Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning , 2019, Neural Networks.

[31]  U. Rajendra Acharya,et al.  Automatic Detection of Epileptic EEG Signals Using Higher Order cumulant Features , 2011, Int. J. Neural Syst..

[32]  Sai-Ho Ling,et al.  An Efficient Diagnosis System for Parkinson's Disease Using Deep Belief Network , 2017 .

[33]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[34]  Pietro Liò,et al.  A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease , 2018, NeuroImage.