Speech deterioration in amyotrophic lateral sclerosis (ALS) after manifestation of bulbar symptoms

Makkonen, Tanja; Ruottinen, Hanna; Puhto, Riitta; Helminen, Mika; Palmio, Johanna

doi:10.1111/1460-6984.12357

Cited by 73 publications

(66 citation statements)

References 24 publications

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“…Patients with limb onset ALS maintain speech much longer than those with bulbar onset; however, 80% of all ALS patients experience dysarthria, or unclear, difficult speech articulation [1]. On average, speech remains adequate for about 18 months after the first bulbar symptom appears [2]. Experts measure disease progression with the ALSFRS-R score, which measures various abilities known to decline as the disease progresses.…”

Section: Introductionmentioning

confidence: 99%

“…Several clinical assessment scales exist for dysarthria, but comparative assessment tools for the scales themselves do not exist. The dataset used in this study includes ALSFRS -R [2] scores for each recording. These ALSFRS-R speech scores, however, do not reflect the specific attributes of dysarthria, are subject to bias via self-report, and have poor granularity to characterize the many variations of ALS speech [1].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Norel¹,

Pietrowicz²,

Agurto³

et al. 2018

Preprint

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ALS is a fatal neurodegenerative disease with no cure. Experts typically measure disease progression via the ALSFRS-R score, which includes measurements of various abilities known to decline. We propose instead the use of speech analysis as a proxy for ALS progression. This technique enables 1) frequent non-invasive, inexpensive, longitudinal analysis, 2) analysis of data recorded in the wild, and 3) creation of an extensive ALS databank for future analysis. Patients and trained medical professionals need not be co-located, enabling more frequent monitoring of more patients from the convenience of their own homes. The goals of this study are the identification of acoustic speech features in naturalistic contexts which characterize disease progression and development of machine models which can recognize the presence and severity of the disease. We evaluated subjects from the Prize4Life Israel dataset, using a variety of frequency, spectral, and voice quality features. The dataset was generated using the ALS Mobile Analyzer, a cellphone app that collects data regarding disease progress using a self-reported ALSFRS-R questionnaire and several active tasks that measure speech and motor skills. Classification via leavefive-subjects-out cross-validation resulted in an accuracy rate of 79% (61% chance) for males and 83% (52% chance) for females.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Norel¹,

Pietrowicz²,

Agurto³

et al. 2018

Preprint

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show abstract

“…People with ALS demonstrate various speech symptoms at the time of diagnosis, ranging from normal speech to the use of augmentative communication. Particularly those with bulbar-onset ALS may have obvious speech deterioration before a definitive diagnosis [7].…”

Section: Diagnosis and Impact On Phonation Processmentioning

confidence: 99%

“…Classification ALS can be classified in several different ways, but the most consensual division encompasses (a) upper motor neurons and (b) lower motor neurons. Upper motor neuron involvement leads to spasticity, weakness, and brisk deep tendon reflexes, while lower motor neuron manifestations include fasciculations, wasting, and weakness [7]. Individuals may develop problems with movement, swallowing (dysphagia), speaking or forming words (dysarthria), and breathing (dyspnea) [8].…”

Section: Introductionmentioning

confidence: 99%

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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“…The disease progression involves all voluntary muscles including not only verbal communication but also hand writing or pointing. This situation makes it a challenge for patients to express themselves [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Student participants’ free comments after a short-term session of augmentative and alternative communication for patients with amyotrophic lateral sclerosis

Ishikawa

Narita

Fukuroku

et al. 2019

ISASE

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Augmentative and alternative communication (AAC) has been used for amyotrophic lateral sclerosis (ALS) patients with severe communication difficulties. We planned a brief educational course for students in multiple healthcare disciplines. In 2018 we held 2 sessions more than 5 months apart. We set out to test a preliminary hypothesis that there would be a difference between novice students and an experienced group of students who had already attended the session once, especially in terms of their ease in managing the AAC. We compared data from the second training session between 6 students (experienced group) who had attended the same session twice in March and August, and 6 new students (novice group) who had never experienced AAC before August. We measured the number of letters obtained by each AAC method and the participants' perceived burden before/after using AAC evaluated by a visual analog scale (VAS). We also asked the participants to write comments after using AAC. We compared the number of letters and the subjective burden between the two groups. Free-text comments were processed by text-mining software (KH Coder®). The burden of the Flick type of communication board was lower for the experienced group (p=0.034). Comparison of Difficulty/Ease of the two groups with KH Coder ® suggested less difficulty in the experienced group (p=0.002). Text-mining of the free comments suggested some difference on Difficulty/Ease between the 2 groups with and without the experience of the educational course.

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Speech deterioration in amyotrophic lateral sclerosis (ALS) after manifestation of bulbar symptoms

Cited by 73 publications

References 24 publications

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

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

Student participants’ free comments after a short-term session of augmentative and alternative communication for patients with amyotrophic lateral sclerosis

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