Parkinson's Disease Detection Research Articles

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD.

Model Touch Pointing and Detect Parkinson's Disease via a Mobile Game

Touch pointing is one of the primary interaction actions on mobile devices. In this research, we aim to (1) model touch pointing for people with Parkinson's Disease (PD), and (2) detect PD via touch pointing. We created a mobile game called MoleBuster in which a user performs a sequence of pointing actions. Our study with 40 participants shows that PD participants exhibited distinct pointing behavior. PD participants were much slower and had greater variances in movement time (MT), while their error rate was slightly lower than age-matched non-PD participants, indicating PD participants traded speed for accuracy. The nominal width Finger-Fitts law showed greater fitness than Fitts' law, suggesting this model should be adopted in lieu of Fitts' law to guide mobile interface design for PD users. We also proposed a CNN-Transformer-based neural network model to detect PD. Taking touch pointing data and comfort rating of finger movement as input, this model achieved an AUC of 0.97 and sensitivity of 0.95 in leave-one-user-out cross-validation. Overall, our research contributes models that reveal the temporal and spatial characteristics of touch pointing for PD users, and provide a new method (CNN-Transformer model) and a mobile game (MoleBuster) for convenient PD detection.

Automatic Diagnosis of Parkinson’s Disease using Handwriting Patterns

Parkinson's Disease (PD) is a neuro-degenerative syndrome characterized by motor and non-motor signs, and early detection is crucial for effective intervention. This paper presents a novel approach for PD detection using computer vision and machine learning techniques applied to Spiral-Wave handwriting analysis. The dataset comprises frontal handwritten images obtained through the Spiral-Wave test, capturing subtle motor control differences. Our methodology involves resizing images to a standardized 200x200 pixels, converting them to grayscale, and applying thresholding for improved feature abstraction. Histogram of Oriented Gradients (HOG) is employed to capture shape and texture information. The development of a strong approach for deriving significant features from Spiral-Wave handwriting patterns and the usage of machine learning classifiers for precise PD analysis are the two main goals of this work. The emphasis is on using Random Forest and K-Nearest Neighbours (KNN) classifiers for Spiral and Wave pictures, respectively, in conjunction with the Histogram of Oriented Gradients (HOG) approach for feature extraction. For Spiral images, a Random Forest Classifier is utilized, achieving an accuracy of 86.67%. The classifier's interpretability is enhanced through an analysis of feature importance, revealing critical HOG features for distinguishing between healthy and PD-afflicted patterns. The Wave images are classified using a K-Nearest Neighbours (KNN) model, attaining an accuracy of 76.67%. Performance metrics, including precision, recall, and F1-score, offer a nuanced assessment of the KNN model's capabilities.

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning

Introduction/ Background This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease. Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection. Materials and Methods We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics. Results The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis. Discussion This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare. Conclusion The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.

Correlations among serum alpha-(1,6)-fucosyltransferase and early symptoms associated with Parkinson's disease: A cross-sectional retrospective study

Alpha-(1,6)-fucosyltransferase (FUT8) has been found to play a role in modulating the central immune system and inflammatory responses. Limited studies have assessed the correlations between serum FUT8 levels and various non-motor symptoms associated with early Parkinson's disease (PD). Therefore, our research aims to investigate the associations between serum FUT8 levels and symptoms such as smell dysfunction, sleep duration, sleep problems, and MMSE scores in PD patients. FUT8 and neurofilament light chain (NfL) levels were measured using enzyme-linked immunosorbent assays (ELISA). We analyzed the correlations between serum FUT8 levels, NfL, and early symptoms of PD using Spearman's correlation, multiple linear regression, and logistic regression models. The expression of FUT8 in CSF samples from PD patients was significantly upregulated, with its protein levels in CSF being positively associated with serum levels. Furthermore, there were significant positive associations between serum FUT8 levels with NfL levels, smell dysfunction, short sleep duration, and long sleep duration. However, a significant inverse relationship was observed between FUT8 levels and MMSE scores. Additionally, we explored gender and age differences in the correlations of FUT8 levels and early symptoms in patients. This study reveals that increased FUT8 levels are positively correlated with a higher risk of early PD-associated symptoms. These findings suggest that serum FUT8 could serve as a promising biomarker for the early detection of PD.

A Survey on AI-based Parkinson Disease Detection: Taxonomy, Case Study, and Research Challenges

Parkinson Disease (PD) is most common diseases from majority of disease encountered all over the world, with more than 7 million individuals being affected. PD is a type of progressive nervous system disease, causing deterioration in health or function. The timely identification of PD is a significant challenge because it rarely shows symptoms in the early stages. Moreover, it is typically encountered in older people where the symptoms sometimes coincide with age-related issues. Deep Learning (DL) can be integrated into many methodologies in diagnosing PD, such as Magnetic Resonance Imaging (MRI) and Single-Photon Emission Computed Tomography (SPECT). DL algorithms can detect PD based on observing some common symptoms. Moreover, it can also be detected using brain MRI images. So, in this study, we reviewed existing DL algorithms for timely identification of PD. We also have developed a CNN model for the timely identification of PD. We used 3D brain MRI images of PPMI datasets and achieved the 88% accuracy.

MELPD-Detector: Multi-level ensemble learning method based on adaptive data augmentation for Parkinson disease detection via free-KD

MELPD-Detector: Multi-level ensemble learning method based on adaptive data augmentation for Parkinson disease detection via free-KD

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

471 Using Computer Vision and Wearable Devices to Improve Care of Parkinson’s Disease

OBJECTIVES/GOALS: Inexpensive, accurate home monitoring is the standard-of-care in many diseases like hypertension or diabetes; however, it has yet to be widely used in neurodegenerative diseases. We used wearable activity monitors and computer-vision evaluated assessments to estimate Parkinson’s disease (PD)-related disease burden. METHODS/STUDY POPULATION: We recruited 22 people from the University of Iowa Movement Disorders Clinic. Each person completed a standardized set of 3 fine motor tasks using their hands. We recorded a video of this activity, which was evaluated using MediaPipe - an open-source pose classification program from Alphabet - as well as had an nurse-practitioner evaluate the performance on a validated scale (UPDRS). Participants wore a Fitbit Inspire 3 activity tracker at home for the next two weeks. We quantified disease burden using the Parkinson’s Disease Questionnaire 39 - a validated 39-item survey about the intensity of PD-related impairment. Using data from the videos and activity trackers, we estimated 1) the standardized UPDRS assessment of motor impairment and 2) the total PDQ-39 score. RESULTS/ANTICIPATED RESULTS: We found observationally recorded fastest sustained (at least 5 minutes) walking speed was a strong predictor of PDQ-39, explaining over one third of the variability in the measure. Range of motion in the videos was a significant predictor of UPDRS scores; however, was only weakly related to the overall PDQ-39 score. Further processing of the signals from the video, including wavelets and frequency domain analysis, may provide better predictive capabilities. PDQ-39 subscores (e.g., cognition, social support, mobility) will be the subject of further analysis. DISCUSSION/SIGNIFICANCE: Home monitoring has become the standard in other fields because of the better generalizability of home measurements. Improving the detection and evaluation of PD using home monitoring will lead to more timely and accurately changes in medication and less need for clinic visits - especially off levodopa.

Dynamic Prediction of Excessive Daytime Sleepiness Through Random Survival Forest: An application of the PPMI data

Parkinson disease (PD) is the second most widespread neurodegenerative disease worldwide. Excessive daytime sleepiness (EDS) has a significant correlation in de novo PD patients. Identifying predictors is critical in order for early detection of disease diagnosis. We investigated clinical and biological markers related with time-dependent variables in sleepiness for early detection of PD. Data were obtained from the Parkinson’s Progression Markers Initiative study, which evaluates the progression markers in patients. The dataset also includes various longitudinal endogenous predictors. The measures of EDS were obtained through the Epworth Sleepiness Scale (ESS). Random survival forest method which can be deal with multivariate longitudinal endogenous predictors was used to predict the probability of having EDS in PD. The rate of having EDS among PD disease was 0.452. The OOB rate was 0.186. The VIMP and minimal depth indicated that the most important variables are stai state, JLO and the presence of ApoE4 Allele. In early PD, EDS is good indicator of the diagnosis of the PD and it increases over time and has association with several predictors.

Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements

INTRODUCTION: Parkinson's Disease (PD), a progressively debilitating neurological disorder impacting a substantial global population, stands as a significant challenge in modern healthcare. The gradual onset of motor and non-motor symptoms underscores the criticality of early detection for optimal treatment outcomes. In response to this urgency, novel avenues for early diagnosis are being explored, where the amalgamation of biomedical voice analysis and advanced machine learning techniques holds immense promise. Individuals afflicted by PD experience a nuanced deterioration of bodily functions, necessitating interventions that are most effective when initiated at an early stage. The potential of biomedical voice measurements to encode subtle health indicators presents an enticing opportunity. The human voice, an intricate interplay of frequencies and patterns, might offer insights into the underlying health condition.
 OBJECTIVES: This research embarks on a comprehensive journey to delve into the intricate connections between voice attributes and the presence of PD, with the aim of expediting its detection and treatment.
 METHODS: At the heart of this exploration is the Support Vector Machine (SVM) model, a versatile machine learning tool [1-2]. Functioning as a virtual detective, the SVM model learns from historical data to decipher the intricate patterns that differentiate healthy individuals from those with PD [3-4].
 RESULTS: Through the power of pattern recognition, the SVM becomes a predictive instrument, a potential catalyst in unravelling the latent manifestations of PD using the unique patterns harbored within the human voice. Embedded within this research are the practical demonstrations showcased through code snippets [5-7]. By synergizing the intricate voice measurements with the SVM model, we envision the emergence of a diagnostic paradigm where early PD detection becomes both accessible and efficient. This study not only epitomizes the synergy of voice and machine interactions but also attests to the transformative potential of technology within the domain of healthcare. .
 CONCLUSION: Ultimately, this research strives to harness the intricate layers of voice data, as exemplified through the provided model code [8-11], to contribute to the evolution of an advanced tool for PD prediction. By amalgamating the principles of machine learning and biomedical analysis, we aspire to expedite early PD diagnosis, thereby catalyzing more efficacious treatment strategies. In traversing this multidimensional exploration, we aspire to pave the path toward a future where technology plays an instrumental role in enhancing healthcare outcomes for individuals navigating the challenges of PD, ultimately advancing the pursuit of early diagnosis and intervention.

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.

Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech

BackgroundTimely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. Materials and methodsIn this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. ResultsTo validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. ConclusionsOur results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.

Modified VGG-19 Deep Learning Strategies for Parkinson's Disease Diagnosis - A Comprehensive Review and Novel Approach

Parkinson's Disease (PD) poses a significant risk to clinical experts due to its rapid progression, adversely affecting individuals afflicted by the condition. This neurological disorder manifests through a spectrum of motor and non-motor symptoms, ranging from motor impairments like stiffness and bradykinesia to mental health issues and stress-related illnesses. Early and accurate diagnosis is pivotal for effective treatment, yet traditional diagnostic methods present challenges in detection and management. Leveraging Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), holds promise for enhancing PD diagnosis. This study provides an extensive literature review of recent advancements in DL-based PD diagnosis, encompassing various approaches and developments. Specifically, the proposed modification to the VGG19 model, incorporating additional layers and regularization techniques, demonstrates remarkable performance in distinguishing PD patients from healthy individuals. Experimental results indicate a 98% accuracy rate, underscoring the model's potential as a reliable tool for early PD detection. Moreover, the study evaluates performance metrics, including accuracy, precision, recall, and F1-score, across different DL models, with the modified VGG19 model outperforming alternatives. The findings suggest that DL algorithms, particularly the proposed model, exhibit superior capability in identifying relevant cases and minimizing misclassifications. In conclusion, this research presents a novel DL approach for PD diagnosis using MRI images, offering substantial advancements in accuracy and efficiency. The model's ability to detect PD with high precision non-invasively underscores its significance in facilitating timely treatment and improving patient outcomes. Ultimately, early and accurate diagnosis facilitated by DL methodologies has the potential to revolutionize PD management, marking a significant stride towards enhancing patient care in neurology.

A Triplet Multimodel Transfer Learning Network for Speech Disorder Screening of Parkinson’s Disease

Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention. The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice. Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability. The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08. The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson's Disease: A Study on Speaker Diarization and Classification Techniques.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King's College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

Quantum mayfly optimization based feature subset selection with hybrid CNN for biomedical Parkinson’s disease diagnosis

Parkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum Mayfly Optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.

AUTOMATED DETECTION OF PARKINSON’S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES

Parkinson’s disease (PD) is a long-term neurological condition that causes severe neuronal degeneration in the motor cortex. Because of the constraints of medical applications determining the severity of PD, forecasting its development can be difficult. Recently research has focused on artificial intelligence to automatically diagnose PD from MRI images. The proposed study aims (a) to incorporate automated method for augmentation of the MRI images using the deep convolutional generative adversarial networks (DCGAN), (b) to perform feature extraction and classification by employing the pre-trained models such as VGG16, Xception, and InceptionV3 models, (c) to develop a hybrid model by fusing the InceptionV3 features and classification using QSVM model. A total of 60 real-time MRIs of normal (N = 30) and PD (N = 30) patients were included in this study. To increase the dataset, the proposed study employed the DCGAN approach. Thus, the dataset for this study was increased to 1000 (normal and PD) images. Automatic feature extraction and classification were performed utilizing different pre-trained models such as VGG16, Xception, and InceptionV3. Among the pre-trained models InceptionV3 architecture provided high accuracy of 74% compared to other models. The proposed work developed a hybrid model by fusing InceptionV3 features and a quantum support vector machine (QSVM) for the detection of PD and healthy groups. The proposed model attained a prediction accuracy of 87.5% and high precision of 95%, respectively. Additionally, the hybrid architecture provided high recall and F1-measure of 84% and 89%, respectively. The hybrid model provided lowest false negative and false positive of 4 and 1, respectively. Therefore, the hybrid model could be an effective diagnostic tool for the automated prediction of PD.