A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%

Early Detection of Parkinson Disease using Voice Data

Parkinson’s disease affects over 10 million people worldwide, with approximately 20 percent of patients not being diagnosed. Clinical diagnosis is expensive because there are no specific tests or bio-markers, and it can take days to diagnose because it is based on a comprehensive evaluation of the individual’s symptoms. Existing research either predicts a Unified Parkinson Disease Rating Scale rating, uses other key Parkinsonian features to diagnose an individual, such as tapping, gait, and tremor, or focuses on different audio features. In this paper, we are focusing on using the voice aspect for the early detection of the disease. We use the University of California Irvine (UCI) Parkinson data set. This data set contains various parameters regarding voice jitter. The data set first undergoes preprocessing. We have used a Feedforward Neural Network (FNN) model to acquire early on detection using the above data set. Our model has achieved an efficiency of 97.43 percent. This efficiency can be improved by using even a larger and diverse data set

Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data

Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection

a comparative study of machine learning algorithms for physiological signal classification

Abstract The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented

Speech function in persons with Parkinson\u27s disease: effects of environment, task and treatment

Parkinson’s Disease (PD) is a degenerative neurological disease affecting aspects of movement, including speech. Persons with PD are reported to have better speech functioning in the clinical setting than in the home setting, but this has not been quantified. New methodologies in ambulatory measures of speech are emerging that allow investigation of non-clinical settings. The following questions are addressed: Is speech different between environments in PD and in healthy controls? Can clinical tasks predict speech behaviors in the home? Is treatment proven effective by measures in the home? What can we glean from methods of measurement of speech function in the home? The experiment included 13 persons with PD and 12 healthy controls, studied in the clinical and home environments, and 7 of those 13 persons with PD participated in a treatment study. Major findings included: Spontaneous speech intelligibility, not intensity, was the differentiating factor between persons with PD and healthy controls. Intelligibility and intensity were not related. Both groups presented with higher sentence intensity in the home environment. Spontaneous speech intelligibility in the clinic was related to spontaneous speech intelligibility in the home. The Sentence Intelligibility Test emerged as the best predictor of spontaneous speech intelligibility in the home. Differences between pilot treatment groups measured in the home on intensity and intelligibility were not large enough to make a clinical trial feasible. Individual differences may account for many of these results, for example more severely impaired patients may have shown different data. Drawing conclusions regarding the home environment via measures outside the home should be carefully considered. Ambulatory measures of speech are a viable option for studying speech function in non-clinical settings, and technology is advancing. Further investigation is needed to develop methodologies and normative values for speech in the home

ACOUSTIC SPEECH MARKERS FOR TRACKING CHANGES IN HYPOKINETIC DYSARTHRIA ASSOCIATED WITH PARKINSON’S DISEASE

Previous research has identified certain overarching features of hypokinetic dysarthria associated with Parkinson’s Disease and found it manifests differently between individuals. Acoustic analysis has often been used to find correlates of perceptual features for differential diagnosis. However, acoustic parameters that are robust for differential diagnosis may not be sensitive to tracking speech changes. Previous longitudinal studies have had limited sample sizes or variable lengths between data collection. This study focused on using acoustic correlates of perceptual features to identify acoustic markers able to track speech changes in people with Parkinson’s Disease (PwPD) over six months. The thesis presents how this study has addressed limitations of previous studies to make a novel contribution to current knowledge. Speech data was collected from 63 PwPD and 47 control speakers using an online podcast software at two time points, six months apart (T1 and T2). Recordings of a standard reading passage, minimal pairs, sustained phonation, and spontaneous speech were collected. Perceptual severity ratings were given by two speech and language therapists for T1 and T2, and acoustic parameters of voice, articulation and prosody were investigated. Two analyses were conducted: a) to identify which acoustic parameters can track perceptual speech changes over time and b) to identify which acoustic parameters can track changes in speech intelligibility over time. An additional attempt was made to identify if these parameters showed group differences for differential diagnosis between PwPD and control speakers at T1 and T2. Results showed that specific acoustic parameters in voice quality, articulation and prosody could differentiate between PwPD and controls, or detect speech changes between T1 and T2, but not both factors. However, specific acoustic parameters within articulation could detect significant group and speech change differences across T1 and T2. The thesis discusses these results, their implications, and the potential for future studies

Voice analysis for neurological disorder recognition – a systematic review and perspective on emerging trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression