Addressing voice recording replications for tracking Parkinson’s disease progression

Naranjo, Lizbeth; Pérez, Carlos J.; Jd, Martín

doi:10.1007/s11517-016-1512-y

Cited by 27 publications

(28 citation statements)

References 26 publications

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“…Data: We utilized "Parkinson Dataset with Replicated Acoustic Features Data Set" that was donated to University of California Irvine Machine Learning repository by Naranjo, et al [17] in April 2019. The publicly available data we used in this study were rst presented by Goetz, et al [21], and other than sex, individual-level descriptors are not publicly available.…”

Section: Methodsmentioning

confidence: 99%

“…A follow-up study [11] reported that PD duration was 5 years or less for all subjects, with a mean Uni ed Parkinson's Disease Rating Scale (UPDRS) score of 19.6 (SD=8.1). The dataset available to us [17] included 44 acoustic features extracted from voice recordings of 40 patients with PD and 40 controls. Recordings of a sustained phonation of the vowel /a/ for 5 seconds were repeated three times (three runs).…”

Section: Methodsmentioning

confidence: 99%

“…Recordings of a sustained phonation of the vowel /a/ for 5 seconds were repeated three times (three runs). Digital recordings were implemented at a 44.1 KHz sampling rate and 16 bits/sample [17]. [22].…”

Section: Methodsmentioning

confidence: 99%

“…Noninvasive speech tests have been explored as a marker of disease [11,13], since deterioration of speech is consistently observed in patients with PD [14][15][16]. Naranjo et al [17,18] previously showed that patients with PD could be identi ed with moderately high accuracy using acoustic features extracted from a speech test. In this study, we implemented machine learning methods, speci cally Light [19] and Extreme Gradient Boosting [20], to signi cantly improve PD detection accuracy from acoustic features extracted from voice recordings.…”

Section: Introductionmentioning

confidence: 99%

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Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Karabayir

Goldman

Pappu

et al. 2020

Preprint

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Background: Parkinson’s Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings.Method: We used “Parkinson Dataset with Replicated Acoustic Features Data Set” from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results: The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson’s Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features.Conclusions: Machine learning can accurately detect Parkinson’s disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson’s disease.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Karabayir

Goldman

Pappu

et al. 2020

Preprint

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“…The total UPDRS score was estimated with MAE = 7.52. A parametric version of this dataset has been made available for research purposes and other research teams further decreased the estimation error [25][26][27]. Another work that deals with the automatic clinical scores estimation was published by Mekyska et al [21].…”

Section: Introductionmentioning

confidence: 99%

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

et al. 2018

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Hypokinetic dysarthria, which is associated with Parkinson's disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11-26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/.

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A novel sample and feature dependent ensemble approach for Parkinson’s disease detection

Ali

Chakraborty

et al. 2022

Neural Comput & Applic

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Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

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Addressing voice recording replications for tracking Parkinson’s disease progression

Cited by 27 publications

References 26 publications

Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

A novel sample and feature dependent ensemble approach for Parkinson’s disease detection

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