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The most challenging issue in diagnosing and treating neurological disorders is gene identification that causes the disease. Classification of the genes that cause or initiate different genes leading to diseases with neurological disorders like Parkinson’s disease, is a grave challenge in biomedical research. Detecting neurological disorders has a...
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... PD is characterized by its progressive nature and is a condition that gradually advances over time. Early detection of the illness offers an opportunity for effective management with appropriate medicine, perhaps leading to its eradication [16,17]. Figure 1 displays the symptoms of PD. ...
... Symptoms of PD[17]. ...
Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.
... The data of unknown class is compared with other data in the training set and a distance measurement is made. According to the calculated distance, the most optimal class for the data that has not yet been assigned to a class is found by looking at the nearest "k" neighbor [27,28]. The distance between these two data is calculated using various distance functions: Manhattan, Minkowski, and Euclidean. ...
Neuromarketing relies on brain-computer interface technology to understand consumer preferences for products and services. Marketers spend approximately 400 billion dollars each year on advertising and promotion in traditional marketing. Traditional marketing approaches cannot fully explain or capture consumers' real-time decision-making. On the other hand, neuromarketing promises to get around these limitations. In this study, we presented a multi-channel electroencephalography (EEG)-based deep learning approach to classify consumers' preferences. The EEG signals were recorded from 25 subjects using 14 channels. The channels were categorized according to the frontal, parietal, temporal, and occipital brain regions. The multitaper spectral analysis approach was then used to extract the feature vectors. Using the extracted feature vectors, the performances of bidirectional long-short-term memory (Bidirectional-LSTM) deep learning, support vector machine (SVM), and k-nearest neighbors (k-NN) machine learning algorithms were compared. The performance of the algorithms was analyzed using frontal, central, parietal, temporal, and occipital brain regions and all channels. Bidirectional-LSTM deep learning algorithm attained the highest accuracy among the other experiments. According to the placement of the channels in the brain regions, the highest accuracy value was 96.83% using Bidirectional-LSTM deep learning algorithm and this was achieved by using electrodes in the frontal region. The performance results analysis was found to be 0.99 recall, 0.95 precision, 0.94 specificity, and 0.97 f1-measure. As a result, this study offers proof of deep learning algorithms' effectiveness in neuromarketing applications.
... To detect PD in an ecologically valid data-gathering setting at the subjects' homes, Tripathi et al. (2022) suggested a new set of characteristics based on keystroke dynamics, i.e. the time necessary to press and release keyboard keys during typing. On a sizable keystroke dynamics PD dataset obtained by observing participants for 22 months and extracting around 5 months' worth of active typing data in an uncontrolled setting at the subjects' homes, they have presented a benchmark of published approaches (Arora et al., 2022). ...
Parkinson's Disease (PD) is a disorder of the nervous system that is chronic and progressive, and affects millions of people around the globe. PD often manifests through symptoms like tremors or shaking, slowness of movement (Bradykinesia), freezing of gait, impaired posture, muscle stiffness, and others. The key focus is the early diagnosis of PD symptoms, which, if handled in their initial stage, can improve the quality of life for the patients. The fine motor control of PD-affected persons, particularly handwriting (Micrographia), can be used for PD diagnosis in patients. Deep Learning (DL) approaches, a subfield of machine learning research represent a useful tool for unsupervised feature learning because they employ a succession of layers, each of which is responsible for extracting different sorts of data. This research work utilises Convolutional Neural Networks (a deep learning algorithm) and focuses on Micrographia as the main diagnostic feature of PD. This work has achieved two main goals, i.e. utilizing CNN for PD diagnosis by learning features from handwriting, thereby improving, and assisting in PD detection, and enhancing overall diagnostic accuracy. The proposed system has achieved the following metrics: Accuracy of 96.67%, Precision of 96.67%, and Recall of 96.67%.
... According to [25], the most challenging aspect of identifying and treating a neurological condition is locating the gene that is responsible for causing the disorder. In the field of biomedical research, it is very challenging to identify the specific genes that are responsible for the onset or progression of many disorders that impact the nervous system, such as Parkinson's disease. ...
... However, there hasn't been nearly as much research conducted to compare different Machine Learning methods that make use of protein sequences to assess Parkinson's disease. In the article [25], a comparison is made between the many methods that may be used to categorize Parkinson's disease. These methods include examining the hydrophobicity of proteins as well as the amino acid composition of their proteins to extract characteristics. ...
... Most of the research observes that the interaction of nucleotide triplets with amino acids is the most important aspect to consider when attempting to anticipate the P-RNA interaction [23][24][25]. [50][51] In addition, we measured the mC, dC, and PseTNC [26] for every RNA sequence by using a database that included 696 P-RNA complexes. The value of PseTNC was determined by the use of three physiochemical parameters, namely hydrophobicity (H), hydrophilicity (HP), and side-chain mass (SCM), respectively [27], [28], and [29]. ...
Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.
Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson’s disease, Essential Tremor, Dystonia, Essential Tremor, Huntington’s disease, and Tourette’s syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder – BGOOSE . This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.
Parkinson’s disease is the second most common neurological disorder that causes significant physical disabilities, decreases the quality of life, and does not have a cure. Because it is a nervous system disorder, it impacts people in different ways, affecting movement and speech and causing muscle stiffness. Approximately, 90% of people with Parkinson’s disease have speech disorders. Machine Learning (ML) algorithms can mostly be employed for the early detection of diseases to enhance the lifespan and improve the lifestyle of people with chronic diseases such as Parkinson’s disease. In this paper, we have employed an Artificial Neurons Network (ANN) and nineteen ML algorithms to predict people with Parkinson’s disease using two different acoustic datasets. Contrary to the train-test split approach, this work aims to utilize the cross-validation technique to estimate the performance. The objective is to ensure that each sample in these small and unbalanced acoustic datasets contributes to both the training and testing processes to provide accurate estimations for the performance of the classifiers on unseen dataset, and to provide a clear insight into the effectiveness of ML algorithms in diagnosing Parkinson's disease via voice disorder. To enhance the performance of the prediction, we employed several techniques such as Optimal Hyperparameters Tuning and Cross-Validation to obtain the best performance and results, and we have provided a detailed explanation of these algorithms' performance and the Optimal Hyperparameters used for each of them. Based on the results and performance, the best classifiers have been selected to build two independent ensemble voting classifiers for the two different datasets. We calculated and represented the accuracy, sensitivity, specificity, precision and AUC. They reached 96.41% and 97.35% of accuracy, respectively.
