npj Digital Medicine · 2020 · 80 citations · 17 references
ComputingNeurologySpeech-language PathologyBiomedical Signal ProcessingComputer VisionSurvival AnalysisAmyotrophic Lateral SclerosisClinical PhoneticsSpeech RecognitionAmyotrophic Lateral Sclerosis PathologyBulbar Dysfunction AssessmentBulbar SymptomsMotor Speech AssessmentSpeech Activity DetectionSpeech Dysfunction
Abstract Bulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients’ ability to communicate, and is linked to shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity to early changes. In this paper, using a cohort of N = 65 ALS patients who provided regular speech samples for 3–9 months, we demonstrated that it is possible to remotely detect early speech changes and track speech progression in ALS via automated algorithmic assessment of speech collected digitally.
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Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
nlme : Linear and nonlinear mixed effects models
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Michael A. del Aguila, W.T. Longstreth, Valerie McGuire et al. · Neurology · 2003 · 450 citations