Parkinson's telemonitoring

We have demonstrated the potential of using speech towards:

• diagnosis of Parkinson’s
• monitoring of symptom progression
• rehabilitation
• subtyping of Parkinson’s

Background

Parkinson’s is a chronic, progressive neurodegenerative disorder straining health systems worldwide due to increasing prevalence rates.

Characteristic symptoms include tremor, rigidity, bradykinesia, and postural instability, within the broader remit of motor, cognitive, and neuropsychiatric symptoms.

Speech problems are reported in the vast majority of people living with Parkinson’s and about 30% consider it one of the major symptoms associated with Parkinson’s that affects their daily lives.

Research question

Can we use speech signals as a remote, accurate, inexpensive biomarker in Parkinson’s? (Aiming to facilitate early diagnosis, telemonitoring of symptoms and rehabilitation.)

Results

We have developed sophisticated signal processing algorithms to mine sustained vowels (‘aah…’ sounds) towards:

• facilitating the differentiation of people with Parkinson’s from controls (Tsanas et al, TBME 2012),
• telemonitoring Parkinson’s symptom severity with accuracy that is greater than the inter-rater variability (i.e. how expert clinicians might differ in symptom assessment) (Tsanas et al, JRSI 2011),
• assisting rehabilitation whilst staying at home (Tsanas et al, TNSRE 2014).

Moreover, we have demonstrated how speech can be useful when used as an early biomarker of Parkinson’s.

Similarly, we have also demonstrated that these assessments can be used with signals collected over the standard telephone network, thus providing new tools that could help early diagnosis (Arora et al, 2018).

We have also developed novel mechanistic models to understand dysarthria aspects and provide tentative physiological insights into speech pathologies associated with Parkinson’s (Gomez et al, BSPC 2021).

Recently, we set up the Parkinson’s Voice Initiative, the largest speech-Parkinson’s study to date, in order to assess the feasibility of developing a large scale population screening tool for the assessment of Parkinson’s disease using telephone-quality speech (Arora et al, JPD 2019), and expanded our findings on using telephone-quality speech to remotely monitor symptom progression at no additional cost (Tsanas et al, IEEE Access 2021).

We have further capitalized on the use of smartphones and speech towards facilitating early assessment (Arora et al, IEEE Access 2021).

 

Funders	EPSRC Intel Corporation
Chief Investigator	Dr Thanasis Tsanas
Key Collaborator	Dr Siddharth Arora, University of Oxford

Papers

Arora S, Lo C, Hu M, Tsanas A. Smartphone speech testing for symptom assessment in rapid eye movement sleep behavior disorder and Parkinson’s disease. IEEE Access. 2021; 9:44813-44824. doi: 10.1109/ACCESS.2021.3057715.

Gomez A, Tsanas A, Gomez P, Palacios D, Rodellar V, Alvarez A. Acoustic to kinematic projection in Parkinson’s disease dysarthria. Biomedical Signal Processing and Control. 2021; 66(4), e102422.

Tsanas A, Little MA, Ramig LO. Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network. IEEE Access. 2021 Jan 11;9:11024-11036. doi: 10.1109/ACCESS.2021.3050524. PMID: 33495722; PMCID: PMC7821632.

Arora S, Baghai-Ravary L, Tsanas A. Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice. J Acoust Soc Am. 2019 May;145(5):2871. doi: 10.1121/1.5100272. PMID: 31153319; PMCID: PMC6509044.

Arora S, Visanji NP, Mestre TA, Tsanas A, AlDakheel A, Connolly BS, Gasca-Salas C, Kern DS, Jain J, Slow EJ, Faust-Socher A, Lang AE, Little MA, Marras C. Investigating Voice as a Biomarker for Leucine-Rich Repeat Kinase 2-Associated Parkinson's Disease. J Parkinsons Dis. 2018;8(4):503-510. doi: 10.3233/JPD-181389. PMID: 30248062.

Tsanas A, Little MA, Fox C, Ramig LO. Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng. 2014 Jan;22(1):181-90. doi: 10.1109/TNSRE.2013.2293575. PMID: 26271131.

Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Trans Biomed Eng. 2012 May;59(5):1264-71. doi: 10.1109/TBME.2012.2183367. Epub 2012 Jan 9. PMID: 22249592.

Tsanas A, Little MA, McSharry PE, Ramig LO. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity. J R Soc Interface. 2011 Jun 6;8(59):842-55. doi: 10.1098/rsif.2010.0456. Epub 2010 Nov 17. PMID: 21084338; PMCID: PMC3104343.

Tsanas A, Little MA, McSharry PE, Ramig LO. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE Trans Biomed Eng. 2010 Apr;57(4):884-93. doi: 10.1109/TBME.2009.2036000. Epub 2009 Nov 20. PMID: 19932995.