Multi-disciplinary clinical protocol for the diagnosis of bulbar amyotrophic lateral sclerosis

Chiaramonte, Rita; Luciano, Carmela Di; Chiaramonte, I.; Bonfiglio, Marco

doi:10.1016/j.otoeng.2017.12.010

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Voice hoarseness, voice roughness, and increased jitter were noticed in ALS patients. Chiaramonte et al [18] and Tomik et al [19] both evaluated the abnormal voice qualities in patients with ALS via a multidisciplinary approach, and despite the distinct methods that were used in each, both demonstrated similar results. One of the most interesting aspects identified by Tomik et al [19] was the fact that differences were detected between male and female results.…”

Section: Discussionmentioning

confidence: 99%

“…From these we have made a manual analysis of the titles/abstracts; the excluded articles were merely about voice symptoms in ALS and not about using voice analysis in ALS assessment. The remaining 7 articles and related references constitute the main object of this review [16][17][18][19][20][21]25].…”

Section: Methodsmentioning

confidence: 99%

“…Early diagnosis is crucial for a better prognosis in the treatment of the disease and, to date, no other method offers such low cost and simplicity as voice-assisted diagnosis. Hence, evaluation of speech and speaking performance may be well suited for ALS early detection and monitoring [15][16][17][18][19][20][21]. In fact, in a large cohort, more than 33% of patients reported to have dysarthria problems [22].…”

Section: Diagnosis and Impact On Phonation Processmentioning

confidence: 99%

“…In the work of Chiaramonte et al [18], the main objective was to study the role of different specialists in the diagnosis of ALS to understand changes in verbal expression and phonation, respiratory dynamics, and swallowing that occurred rapidly over a short period of time. This study consisted in analyzing 22 patients with bulbar ALS using several methods: voice assessment, ENT evaluation, multi-dimensional voice program, spectrogram, electroglottography, and fibertropic endoscopic evaluation of swallowing.…”

Section: Stage 2 Definition Of Article Search and Selection Strategiesmentioning

confidence: 99%

“…In the acoustic analysis of Chiaramonte et al [18], jitter, shimmer, v f0 (coefficient of fundamental frequency variation, the relative standard deviation of f 0 ), ATRI (amplitude tremor intensity index, the average ratio of the amplitude of the most intense low-frequency amplitude-modulating component to the total amplitude of the analyzed voice signal), FTRI (frequency tremor intensity index, the average ratio of the frequency magnitude of the most intensive low-frequency-modulating component to the total frequency magnitude of the analyzed voice signal), DVB (degree of voice breaks, the ratio of the total length of areas representing voice breaks to the time of the complete voice sample), DSH (degree of subharmonic components, the estimated relative evaluation of subharmonic to f 0 components in the voice sample), and v Am (coefficient of amplitude variation, the relative standard deviation of the peak-to-peak amplitude) are increased, and NHR (noise-to-harmonics ratio, the average ratio of the inharmonic spectral energy in the frequency range 1,500-4,500 Hz to the harmonic spectral energy in the frequency range 70-4,500 Hz) and maximum phonation time (persistently pronounce the vowel "a" with a conscious and comfortable pitch and sound intensity for as long as possible) are reduced. In the spectrogram, there were new formants and more noise components.…”

Section: Stage 2 Definition Of Article Search and Selection Strategiesmentioning

confidence: 99%

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Voice-Based Classification of Amyotrophic Lateral Sclerosis: Where Are We and Where Are We Going? A Systematic Review

et al. 2019

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Background: Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems. Summary: We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context. Key Messages: The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most co-horts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

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Section: Discussionmentioning

confidence: 99%