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

Effects of subthalamic nucleus deep brain stimulation on the speech of Spanish-speaking Parkinson's disease patients during the first year of treatment

PURPOSE

To describe the effects of subthalamic nucleus deep brain stimulation (STN-DBS) on the speech of Spanish-speaking Parkinson's disease (PD) patients during the first year of treatment.

METHODS

The speech measures (SMs): maximum phonation time, acoustic voice measures, speech rate, speech intelligibility measures, and oral diadochokinesis rates of nine Colombian idiopathic PD patients (four females and five males; age = 63 ± 7 years; years of PD = 10 ± 7 years; UPDRS-III = 57 ± 6; H&Y = 2 ± 0.3) were studied in OFF and ON medication states before and every three months during the first year after STN-DBS surgery. Praat software and healthy native listeners' ratings were used for speech analysis. Statistical analysis tried to find significant differences in the SMs during follow-up (Friedman test) and between medication states (Wilcoxon paired test). Also, a pre-surgery variation interval (PSVI) of reference for every participant and SM was calculated to make an individual analysis of post-surgery variation.

RESULTS

Non-significative post-surgery or medication state-related differences in the SMs were found. Nevertheless, individually, based on PSVIs, the SMs exhibited: no variation, inconsistent or consistent variation during post-surgery follow-up in different combinations, depending on the medication state.

CONCLUSION

As a group, participants did not have a shared post-surgery pattern of change in any SM. Instead, based on PSVIs, the SMs varied differently in every participant, which suggests that in Spanish-speaking PD patients, the effects of STN-DBS on speech during the first year of treatment could be highly variable.

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

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), 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.Approach.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 searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin 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's safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

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

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.

Acoustic and Physiological Voice Assessment And Maximum Phonation Time In Patients With Different Types Of Dysarthria

OBJECTIVE

To compare the maximum phonation time of /a/, acoustic glottal source parameters, and physiological measures in patients with dysarthria.

METHOD

Thirteen patients were classified according to dysarthria type and divided into functional profiles (hypofunctional, hyperfunctional, and mixed). Assessments of maximum phonation time of /a/, glottal source parameters, electroglottography, and nasometry were performed. Results were compared between groups using ANOVA and Tukey posthoc tests.

RESULTS

The highest fundamental frequency differed significantly between groups, with the hyperfunctional profile showing higher values than the other participant groups. Reductions in the maximum phonation time of /a/ and alterations in acoustic glottal source parameters and electroglottography measures were observed in all groups, with no significant differences between them. The remaining measures did not differ between groups.

CONCLUSION

The maximum phonation times for /a/ were reduced in all participant groups, suggesting air escape during phonation. The presence of alterations in several glottal source parameters in all participant groups is indicative of noise, tremor, and vocal instability. Lastly, the high fundamental frequency in patients with a hyperfunctional profile reinforces the presence of vocal instability. These findings suggest that, although the characteristics observed in the assessments were consistent with expectations of patients with dysarthria, it is difficult to perform a differential diagnosis of this condition based on acoustic and physiological parameters alone.

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

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.

Investigating the effects of subthalamic Nucleus-Deep brain stimulation on the voice quality

Introduction: Deep brain stimulation (DBS) is a standard surgical treatment method which is generally applied to subthalamic nucleus in Parkinson's patients in cases where medical treatment is insufficient in treating the motor symptoms. It is known that Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) treats many motor symptoms. However, the results of studies on speech and voice vary. The aim of the study is analysing the effect of STN-DBS on the characteristics of voice.Materials/methods: A total of 12 patients, (8 male-4 female) with an age average of 58.8 ± 9.6, who have been applied DBS surgery on STN included in the study. The voice recordings of the patients have been done prior to surgery and 6 months after the surgery. The evaluation of voice has been carried out through the instrumental method. The patients' voice recordings of the /a,e,i/ vowels have been done. The obtained recordings were evaluated by the Praat programme and the effects on jhitter, shimmer, fundamental frequency (F0) and noise harmonic rate (NHR) were analysed.Results: Numerical values of F0 of all female participants have been decreased for all of the vowels postoperatively. In the females; jhitter and fraction parameters were found to be significantly different (0.056 and 0.017, perspectively) for the vowel /e/. In addition, p values in the shimmer for vowels /e,i/ were thought to be clinically significant (.087, .079 and .076) respectively. All these changes in second measurements were found to indicate worsening vocal quality after the DBS in females. In males, there is not any significant difference observed between two measures in any of the parameters of any vowels.Conclusions: Acoustic voice quality deteriorated after STN-DBS predominantly for females however this deterioration was not prominent audio-perceptually. This finding commented as a result of the fact that that voice quality deviance of the participants was not severe.