Diagnosis of Parkinson's disease from electroencephalography signals using linear and self‐similarity features

Bhurane, Ankit A.; Dhok, Shivani; Sharma, Manish; Yuvaraj, Rajamanickam; Murugappan, M.; Acharya, U. Rajendra

doi:10.1111/exsy.12472

Cited by 33 publications

(28 citation statements)

References 57 publications

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“…These CAD tools can perform automated detection using the biomarkers of PD, such as Electroencephalogram (EEG) signals, posture analysis in the gait cycle, voice aberration, or brain imaging such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) [14]. In a conventional machine learning model, it is mandatory to extract the features from the biomarkers and then select the most salient features in order to train the model [15][16][17][18][19]. This is a required step because machine learning models by itself are not capable of learning the high dimensional data in their raw forms, otherwise, the model is likely to overfit the dataset [20].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

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Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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“…SVM is widely used classifier for biomedical signal applications (Bhurane et al, 2019; Qu, Liu, & Liu, 2019). SVM works by finding a best hyperplane in high‐dimensional space to separate data belonging to different classes.…”

Section: Methodsmentioning

confidence: 99%

“…SVM is widely used classifier for biomedical signal applications (Bhurane et al, 2019;Qu, Liu, & Liu, 2019…”

Section: Classification -Support Vector Machinesmentioning

confidence: 99%

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

et al. 2021

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Heart diseases are a major cause of human casualties each year. An accurate and efficient diagnosis is essential to minimize their risk. This paper presents a system for the classification of multiple cardiac disorders based on pulse plethysmographic (PuPG) signal analysis. In particular, the work focuses on the detection and classification of ischemic and rheumatic heart diseases using proposed 1D local ternary patterns of PuPG signals. The proposed methodology is applied on a self‐collected dataset consisting of 250 PuPG signals from 50 normal/healthy subjects, 140 signals from 28 ischemic patients, and 180 signals from 36 rheumatic patients. The effectiveness of proposed feature descriptors to precisely represent different classes of data is verified by several classifiers namely K‐nearest neighbours (KNN), support vector machines (SVM), and decision tree (DT) with 10‐fold cross‐validation. The proposed methodology achieves the best detection performance with 99% accuracy, 100% sensitivity, and 98% specificity using an SVM classifier with cubic kernel. The comparative analysis demonstrates that the proposed method is more accurate and reliable as compared to several existing works on cardiac disorders classification.

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“…We have used orthogonal filter with length 12 and 4 vanishing moments [46] in this study. The significance of this method is that, it can give more accurate and thorough results including all local and global minimum [47,48].…”

Section: Orthogonal Filter Bank and Wavelet Decompositionmentioning

confidence: 99%

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

et al. 2021

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Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

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Diagnosis of Parkinson's disease from electroencephalography signals using linear and self‐similarity features

Cited by 33 publications

References 57 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

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