scholarly journals An optimized RNN-LSTM approach for parkinson’s disease early detection using speech features

2021 ◽  
Vol 10 (5) ◽  
pp. 2503-2512
Author(s):  
Hadeel Ahmed Abd El Aal ◽  
Shereen A. Taie ◽  
Nashwa El-Bendary
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Short Term Memory ◽  
Neurodegenerative Disorder ◽  
Classification Performance ◽  
The Body ◽  
Testing Dataset ◽  
Benchmark Datasets ◽  
Speech Features

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.

Emerging targets for the diagnosis of Parkinson’s disease: examination of systemic biomarkers

2021 ◽  
Author(s):  
Lara Cheslow ◽  
Adam E Snook ◽  
Scott A Waldman
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Neuronal Damage ◽  
Unmet Need ◽  
Multiple Organ ◽  
The Body ◽  
Organ Systems ◽  
Diagnostic Approaches ◽  
New Biomarkers

Parkinson’s disease (PD) is a highly prevalent and irreversible neurodegenerative disorder that is typically diagnosed in an advanced stage. Currently, there are no approved biomarkers that reliably identify PD patients before they have undergone extensive neuronal damage, eliminating the opportunity for future disease-modifying therapies to intervene in disease progression. This unmet need for diagnostic and therapeutic biomarkers has fueled PD research for decades, but these efforts have not yet yielded actionable results. Recently, studies exploring mechanisms underlying PD progression have offered insights into multisystemic contributions to pathology, challenging the classic perspective of PD as a disease isolated to the brain. This shift in understanding has opened the door to potential new biomarkers from multiple sites in the body. This review focuses on emerging candidates for PD biomarkers in the context of current diagnostic approaches and multiple organ systems that contribute to disease.

Early Detection of Parkinson's Disease

2019 ◽  
pp. 61-95
Author(s):  
Debashree Devi ◽  
Saroj K. Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Neural Signals ◽  
Learning Machine ◽  
Preprocessing Technique ◽  
Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

scholarly journals Forced exercise activates the NrF2 pathway in the striatum and ameliorates motor and behavioral manifestations of Parkinson's disease in rotenone-treated rats

2020 ◽  
Author(s):  
Dina Monir ◽  
Motamed Mahmoud ◽  
Omyma Galal ◽  
Ibrahim Rehan ◽  
Amany Abdelrahman
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopaminergic Neurons ◽  
Short Term Memory ◽  
Treadmill Exercise ◽  
Neurodegenerative Disorder ◽  
Short Term ◽  
Term Memory ◽  
Nrf2 Pathway

Abstract Background: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by progressive loss of nigrostriatal dopaminergic neurons leading to dopamine depletion and problems of movement, emotions and cognition. While the pathogenesis of PD is not clear, damage of dopaminergic neurons by oxygen-derived free radicals is considered an important contributing mechanism.This study aimed to evaluate the role of treadmill exercise in male Wister rats as a single treatment and as an aid-therapy with L-dopa for rotenone-induced PD. To study the role of NRF2-ARE pathway as a mechanism involved in exercise associated improvement in rotenone rat model of PD.Method: Animals were divided into 5 groups, (Control, rotenone, rotenone\exercise, rotenone\L-dopa, and rotenone\exercise\L-dopa (combination) groups). After the PD induction, rats in the rotenone\exercise and combination groups were daily treadmill exercised for 4 weeks.Results: Treadmill exercise significantly improved behavioral and motor aspects of rotenone model of PD. When treadmill exercise introduced as a single intervention, it amended most behavioral aspects of PD, gait fully corrected, short-term memory, and motor coordination. Where L-dopa corrected locomotor activity and motor co-ordination but failed to improve short-term memory and only partially corrected the gait of rotenone-treated rats. When treadmill exercise was combined with L-dopa, all features of PD were corrected. It was found that exercise upregulated some of its associative genes to NRF2 pathways such as TFAM, NRF2, Noq.1 mRNA expression.Conclusion: This study suggests that forced exercise improved parkinsonian like features by activating NRF2 pathway.

Orally Administered Nanotherapeutics For Parkinson’s Disease: An Old Delivery System Yet More Acceptable

2020 ◽  
Vol 26 (19) ◽  
pp. 2280-2290 ◽  
Author(s):  
Nidhi Aggarwal ◽  
Zufika Qamar ◽  
Saleha Rehman ◽  
Sanjula Baboota ◽  
Javed Ali
Keyword(s):  
Parkinson’S Disease ◽  
Drug Delivery ◽  
Parkinson's Disease ◽  
Drug Delivery Systems ◽  
Neurodegenerative Disorder ◽  
Intestinal Barrier ◽  
Oral Route ◽  
Delivery Systems ◽  
The Body ◽  
Area Of Interest

As per the present global scenario, Parkinson’s disease (PD) is considered to be the second most common neurodegenerative disorder which is a keen area of interest among researchers. The conventional therapies generally employed against PD are associated with serious drawbacks including limited transport across selectively permeable BBB, hepatic metabolism, intestinal barrier, etc. This urges the need to develop novel therapeutic alternatives. The oral route being the most preferred route of administration needs to be explored for new and more intelligent drug delivery systems. Nanotechnology has been proposed to play a promising role in reversing the progression of the disease via the oral route. Nanocarriers, namely nanoparticles, lipid nanoparticles, nanoemulsions, nanocrystals, nanomicellar formulations, self-nanoemulsifying drug delivery systems and alginate nanocomposites have been investigated upon to modulate the fate of drugs inside the human body when administered orally. The development of various nanotherapeutics for the treatment of PD has been reviewed, depicting an enhanced bioavailability to provide a desired therapeutic outcome. The new advances in the therapy have been explored and highlighted through the body of this review. However, a therapeutically effective concentration at the target site remains a challenge, therefore extensive exploration in the field of nanotherapeutics may facilitate superior drug delivery to CNS via oral route thereby improving the state of disease progression.

Early Detection of Parkinson's Disease

2021 ◽  
pp. 295-328
Author(s):  
Debashree Devi ◽  
Saroj K. Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Neural Signals ◽  
Learning Machine ◽  
Preprocessing Technique ◽  
Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

scholarly journals Management of Symptoms of Parkinsonism Through Panchakarma Therapy

2020 ◽  
Author(s):  
Ashok Kumar Pandey ◽  
Saurabh Mishra ◽  
Alka Mishra
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Herbal Medicines ◽  
The Elderly ◽  
The Body ◽  
Lower Back ◽  
Patient Reported ◽  
The Brain

Background: Parkinson's disease is a disabling neurodegenerative disorder, mainly affecting the elderly population. Symptoms of Parkinsonism include motor function abnormalities, tremors in hands and legs, postural instability, etc. Side-effect free, long-term management of Parkinsonism is still a challenge. According to Ayurveda, the disease that resembles the symptoms associated with Parkinson's disease is Kampavata (kampa means tremors), which is primarily caused by the imbalance of the Vata Dosha. Various Panchakarma procedures have been found useful in the treatment of different Vata Vyadhis (diseases caused by the imbalance of Vata Dosha).Methodology: Panchakarma therapy was administered for 19 days to a male patient suffering from symptoms of Parkinsonism (Kampavata) since about nine months, as well as other associated ailments. According to Ayurveda, Kampavata is primarily associated with Vata imbalance. Hence, Vata pacifying herbal medicines, that also provide strengthening and nourishing effect to the degenerative tissues of the body, as well as nourishment to the brain, were used.Results: The patient experienced significant relief in the tremors in B/L hands, numbness in B/L big toes, weakness in lower extremity, and lower back pain. The patient also experienced notable relief in the complaints of Constipation, Gastric upset, and Flatus. Overall, the patient reported a satisfactory experience after taking the therapy.Conclusion: Panchakarma therapy showed encouraging results in the management of symptoms associated with Parkinsonism, as well as other associated ailments, in short duration of time.

scholarly journals Different Classification Approaches for Early Detection of Parkinson’s Disease

2021 ◽  
Author(s):  
Nikita Aggarwal ◽  
Jasleen Saini ◽  
B.S. Saini ◽  
Savita Gupta
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Continuous Monitoring ◽  
Neurodegenerative Disorder ◽  
Diagnostic Process ◽  
Research Directions ◽  
Non Motor Symptoms ◽  
Aided Diagnosis

Parkinson’s disease is perhaps the most well-known neurodegenerative disorder that mainly occurs due to the loss of dopamine-producing neurons and consists of motor/non-motor symptoms. The progression of the symptoms is often varying from one person to another to the diversity of the disease. The condition causes a huge burden both on those affected, as well as their families. Accurate diagnosis is critical and challenging but still, no specific diagnostic process is available. The computer-aided diagnosis techniques of signalling and imaging processing are very helpful in the prediction and classification of PD. This review gives a brief description of different methods of classification for early detection and also highlights the most profitable research directions by focusing on continuous monitoring patterns of daily activities, interactions, and routine that may provide the data on status changes, clinical management, and controlling self-correction

scholarly journals m-ark-Support Vector Machine for Early Detection of Parkinson’s Disease from Speech Signals

2021 ◽  
Vol 15 ◽  
pp. 34-41
Author(s):  
Luca Parisi ◽  
Amir Zaernia ◽  
Renfei Ma ◽  
Mansour Youseffi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Early Diagnosis ◽  
Real Life ◽  
Kernel Functions ◽  
Support Vector ◽  
Margin Width ◽  
Speech Data ◽  
Benchmark Datasets

Recent advances in the state-of-the-art open-source kernel functions for support vector machines (SVMs) have widened the choices of benchmark kernels for Machine Learning (ML)-based classification. However, it is still challenging to achieve margin maximisation in SVM, and further evidence is required to ensure such novel kernel functions can have translational applications with tangible impact. Noteworthily, m-arcsinh, freely available in scikit-learn, was preliminarily proven as a benchmark kernel function on 15 datasets in its seminal paper. Quantifying the benefit from leveraging this kernel in a specific application is essential to provide further evidence of its accuracy and reliability on real-life supervised ML-aided tasks. Thus, the predictive capability of SVM, including that with Lagrange multipliers for the first time coupled with m-arcsinh (m-ark-SVM with soft margin; m-arK-SVM with hard margin), is hereby assessed in aiding early detection of Parkinson’s Disease (PD) from speech data. This is important to leverage the m-arcsinh kernel ‘trick’ to maximise the margin width and, therefore, the linear separability of input speech features via automated pattern recognition. In this study, we demonstrate the accuracy and reliability of m-ark-SVM to aid early diagnosis of PD, evaluated against other gold standard kernel functions. Two benchmark datasets from the University of California-Irvine (UCI) database, pre-processed solely via min-max normalisation, were used to discriminate between speech patterns of 72 healthy subjects and 211 patients with PD. Overtraining was avoided via cross validation and the models were developed and tested in Python 3.7. The supervised model (m-ark-SVM) could detect early Parkinson’s Disease with 87.18% and 86.9% classification accuracy from the two datasets respectively (F1- scores: 85 and 86.2% correspondingly). Furthermore, the model achieved high precision (89.2% and 86.8%) and specificity (87% and 86.8%). Thus, this study validates the application of m-arcsinh to aid real-life supervised ML-based classification, in particular early diagnosis of Parkinson’s Disease from speech data.

SPECT-Befunde bei Hemiparkinson-Syndrom mit 99mTc-HMPAO

1989 ◽  
Vol 28 (03) ◽  
pp. 92-94 ◽  
Author(s):  
C. Neumann ◽  
H. Baas ◽  
R. Hefner ◽  
G. Hör
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Neuronal Activity ◽  
Tracer Uptake ◽  
The Body ◽  
99Mtc Hmpao ◽  
Do So

The symptoms of Parkinson’s disease often begin on one side of the body and continue to do so as the disease progresses. First SPECT results in 4 patients with hemiparkinsonism using 99mTc-HMPAO as perfusion marker are reported. Three patients exhibited reduced tracer uptake in the contralateral basal ganglia One patient who was under therapy for 1 year, showed a different perfusion pattern with reduced uptake in both basal ganglia. These results might indicate reduced perfusion secondary to reduced striatal neuronal activity.

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