Computerized analysis of speech and voice for Parkinson's disease: A systematic review

Ngo, Quoc Cuong; Motin, Mohammod Abdul; Pah, Nemuel Daniel; Drotár, Peter; Kempster, Peter; Kumar, Deepak

doi:10.1016/j.cmpb.2022.107133

Cited by 18 publications

(11 citation statements)

References 188 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Acoustic parameters were analyzed using Computerized Speech Lab (CS) model 4500 (PENTAX Medical, New Jersey, USA). CSL is considered the gold standard system for acoustic analysis and has been validated to assess voice pathology in many controlled trials 9 , 16 . The Multi-Dimensional Voice Program (MDVP), a computerized voice analysis system, in conjunction with CSL, is a versatile voice-processing and spectrographic analysis software package ideally suited for use in determining acoustic parameters including fundamental frequency (F0), fundamental frequency variation (vF0), jitter, shimmer, relative average perturbation (RAP), and noise-to-harmonic ratio (NHR).…”

Section: Methodsmentioning

confidence: 99%

Predicting glycemic control status and high blood glucose levels through voice characteristic analysis in patients with cystic fibrosis-related diabetes (CFRD)

Suppakitjanusant,

Kasemkosin,

Sivapiromrat

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Cystic fibrosis-related diabetes (CFRD) is associated with reduced life expectancy in adults with cystic fibrosis (CF). Voice analysis may be a convenient method for diagnosing and monitoring CFRD. This study aims to determine the relationship between voice characteristics and markers of glucose and glycemic control and to identify if voice analysis can predict high blood glucose levels and glycemic control in adults with CFRD. We conducted a prospective cross-sectional study in adults with CF from March to December 2021. We recorded 3-second voice samples of a sustained /a/ vowel and analyzed voice characteristic using the Computerized Speech Lab with the Multi-Dimensional Voice Program. In female participants with CFRD, the noise-to-harmonic ratio was significantly lower in those with HbA1c ≥ 7. Furthermore, fundamental frequency variation was significantly lower in both male and female participants with CFRD who had a glucose level of 200 mg/dL or higher at the time of collection. This finding was also associated with a high level of point-of-care glucose. The human voice has potential as a non-invasive tool for measuring glucose levels and glycemic control status in CFRD patients in the future.

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting glycemic control status and high blood glucose levels through voice characteristic analysis in patients with cystic fibrosis-related diabetes (CFRD)

Suppakitjanusant,

Kasemkosin,

Sivapiromrat

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Various studies employed different modalities to detect PD (Mei et al 2021, Ngo et al 2022. However, cognitive dysfunction is another commonly observed biomarker in individuals with PD, including those who have recently been diagnosed clinically (Fang et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson’s disease even being under medication

Tülay

2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Objective: Brain-Computer Interface technologies, as a type of Human-Computer Interaction, provide a control ability on machines and intelligent systems via human brain functions without needing physical contact. Moreover, it has a considerable contribution to the detection of cognitive state changes, which gives a clue for neuropsychiatric diseases, including Parkinson’s disease (PD), in recent years. Although various studies implemented different machine learning models with several EEG features to detect PD and receive remarkable performances, there is a lack of knowledge on how brain connectivity during a cognitive task contributes to the differentiation of PD, even being under medication. Approach: To fill this gap, this study used three ensemble classifiers, which were fed by functional connectivity through cognitive response coherence (CRC) with varying selected features in different frequency bands upon application of the 3-Stimulation auditory oddball paradigm to differentiate PD medication ON and OFF and healthy controls (HC). Main results: The results revealed that the most remarkable performances were exhibited in slow frequency bands (delta and theta) in comparison to high frequency and wide range bands, especially in terms of target sounds. Moreover, in the delta band, target CRC distinguishes all groups from each other with accuracy rates of 80% for HC vs PD-OFF, 80% for HC vs PD-ON, and 81% for PD-ON vs PD-OFF. In the theta band, again target sounds were the most distinctive stimuli to classify HCxPD-OFF (80% accuracy), HCxPD-ON (80.5% accuracy) with quite good performances, and PD-ONxPD-OFF (76% accuracy) with acceptable performance. Besides, this study achieved a state-of-the-art performance with an accuracy of 87.5% in classifying PD-ON x PD-OFF via CRC of standard sounds in the delta band. Significance: Overall, the findings revealed that brain connectivity contributes to identifying PD and HC as well as the medication state of PD, especially in the slow frequency bands.

show abstract

“…To reduce the likelihood of misdiagnosis, the International Parkinson’s and Movement Disorder Society (MDS) has formulated the (UPDRS) guidelines [4] . However, many of the early-stage symptoms such as subtle changes in handwriting and voice [5] , can be missed because of the natural variation in the population and changes in an individual with age [6] , [7] , [8] .…”

Section: Introductionmentioning

confidence: 99%

NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting

Ngo,

McConnell,

Motin

et al. 2024

IEEE J. Transl. Eng. Health Med.

Self Cite

View full text Add to dashboard Cite

Objective: A change in handwriting is an early sign of Parkinson’s disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. Methods: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. Results: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. Conclusion: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement — This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson’s disease using automated handwriting analysis software, NeuroDiag.

show abstract

Computerized analysis of speech and voice for Parkinson's disease: A systematic review

Cited by 18 publications

References 188 publications

Predicting glycemic control status and high blood glucose levels through voice characteristic analysis in patients with cystic fibrosis-related diabetes (CFRD)

Predicting glycemic control status and high blood glucose levels through voice characteristic analysis in patients with cystic fibrosis-related diabetes (CFRD)

Ensemble classifiers fed by functional connectivity during cognitive processing differentiate Parkinson’s disease even being under medication

NeuroDiag: Software for Automated Diagnosis of Parkinson’s Disease Using Handwriting

Contact Info

Product

Resources

About