Acoustic Voice Modifications in Individuals with Parkinson Disease Submitted to Deep Brain Stimulation

Romann, Aline Juliane; Beber, Bárbara Costa; Cielo, Carla Aparecida; Rieder, Carlos Roberto de Mello

doi:10.1055/s-0038-1675392

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Before applying feature extraction, all the recordings from both databases were trimmed down to one second discarding any leading or trailing silence. This length has been considered sufficient to extract speech features from sustained vowel phonations by other authors [ 40 ]. Voice recordings were edited using Audacity software (release 2.0.5).…”

Section: Methodsmentioning

confidence: 99%

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

et al. 2021

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Background and objective Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. Methods A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. Results In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. Conclusion The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.

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Section: Methodsmentioning

confidence: 99%

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

et al. 2021

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“…Several studies have explored the impact of ON/OFF stimulation on phonation and articulation in PD patients undergoing DBS [97, 169,170,[183][184][185][186][187][188][189][190][191][192][193]. Some studies yielded positive effects of DBS on speech parameters, such as a reduced jitter and noise-to-harmonics ratio, improved fundamental frequency (F0), and vocal response to pitch shift, as well as increased phonation time and syllable length [184,185,189,193].…”

Section: Phonation and Articulationmentioning

confidence: 99%

“…Some studies yielded positive effects of DBS on speech parameters, such as a reduced jitter and noise-to-harmonics ratio, improved fundamental frequency (F0), and vocal response to pitch shift, as well as increased phonation time and syllable length [184,185,189,193]. Conversely, other studies found negative effects of DBS, such as diminished speech intelligibility, deteriorated articulation, and reduced vocal response magnitudes [97, 106,169,170,188,[190][191][192]. Additionally, some others have showed conflicting results for various speech measures [170,183].…”

Section: Phonation and Articulationmentioning

confidence: 99%

Speech, voice, and language outcomes following deep brain stimulation: A systematic review

Tabari,

Berger,

Flouty

et al. 2024

PLoS ONE

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Background Deep brain stimulation (DBS) reliably ameliorates cardinal motor symptoms in Parkinson’s disease (PD) and essential tremor (ET). However, the effects of DBS on speech, voice and language have been inconsistent and have not been examined comprehensively in a single study. Objective We conducted a systematic analysis of literature by reviewing studies that examined the effects of DBS on speech, voice and language in PD and ET. Methods A total of 675 publications were retrieved from PubMed, Embase, CINHAL, Web of Science, Cochrane Library and Scopus databases. Based on our selection criteria, 90 papers were included in our analysis. The selected publications were categorized into four subcategories: Fluency, Word production, Articulation and phonology and Voice quality. Results The results suggested a long-term decline in verbal fluency, with more studies reporting deficits in phonemic fluency than semantic fluency following DBS. Additionally, high frequency stimulation, left-sided and bilateral DBS were associated with worse verbal fluency outcomes. Naming improved in the short-term following DBS-ON compared to DBS-OFF, with no long-term differences between the two conditions. Bilateral and low-frequency DBS demonstrated a relative improvement for phonation and articulation. Nonetheless, long-term DBS exacerbated phonation and articulation deficits. The effect of DBS on voice was highly variable, with both improvements and deterioration in different measures of voice. Conclusion This was the first study that aimed to combine the outcome of speech, voice, and language following DBS in a single systematic review. The findings revealed a heterogeneous pattern of results for speech, voice, and language across DBS studies, and provided directions for future studies.

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“…These side-effects include muscle contractions, voice changes, deleterious mood and cognitive changes, memory impairments, seizures, and more. [18][19][20][21][22][23] While side effects or returning symptoms caused by a change to the stimulation setting may be briefly tolerable under the supervision of a clinician, they could be unsafe or detrimental during daily living activities. As a consequence, data-driven methods must be designed to actively learn and avoid unsafe parameter space regions for any automated or unsupervised optimization approaches.…”

Section: Introductionmentioning

confidence: 99%

SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

Cole,

Connolly,

Ghetiya

et al. 2024

Preprint

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To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject-specific safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.

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Acoustic Voice Modifications in Individuals with Parkinson Disease Submitted to Deep Brain Stimulation

Cited by 8 publications

References 35 publications

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

Speech, voice, and language outcomes following deep brain stimulation: A systematic review

SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

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