Automatic Early Detection of Amyotrophic Lateral Sclerosis from Intelligible Speech Using Convolutional Neural Networks

Abstract

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease of the motor system that leads to the impairment of speech and swallowing functions. The lack of a biomarker typically causes a diagnostic delay. To advance the current diagnostic process, we explored the feasibility of automatic detection of patients with ALS at an early stage from highly intelligible speech. A speech dataset was collected from thirteen newly diagnosed patients with ALS and thirteen ageand gender-matched healthy controls. Convolutional Neural Networks (CNNs), including time-domain CNN and frequencydomain CNN, were used to classify the intelligible speech produced by patients with ALS and those by healthy individuals. Experimental results indicated both time-and frequency-CNN outperformed standard neural network. The best sample-level sensitivity and specificity were obtained by time-CNN (71.6% and 80.9%, respectively). When multiple samples were used to vote to estimate a person-level performance, the best result was obtained by frequency-CNN (76.9% sensitivity and 92.3% specificity). Results demonstrated the possibility of early detection of ALS from intelligible speech signals.

Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato

“…In several studies detection of ALS is performed using kinematic sensors [4,12] to model articulation and measure prosodic elements such as vowel duration or speaking rate [2]. Running speech test was used in [7,10,11] as a basis for ALS detection. In [10] representational learning approach based on convolutional neural networks (CNN) was applied for ALS detection.…”

Early Identification of Speech Changes Due to Amyotrophic Lateral Sclerosis Using Machine Classification

“…Although our diagnostic goal was to identify presymptomatic patients, we found that models trained on Presymptomatic-Control data performed around chance, probably due to our small sample size. [21] obtained an abovechance classification performance for participants with early ALS (71.6% sensitivity and 80.9% specificity), possibly due to their larger gender-mixed dataset and more complicated ML algorithm (convolutional neural network).…”

Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

“…For this reason, in recent years, speech technologies are being proposed for the assessment, diagnosis and tracking of different health conditions that affect the subject’s voice [ 20 ]. In this area, commonly referred to as Computational Paralinguistic Analysis , current research encompasses the detection of pathological voices due, for example, to laryngeal disorders [ 21 ]; the diagnosis and monitoring of neurodegenerative conditions, such as Parkinson’s disease [ 22 , 23 ], Mild Cognitive Impairment [ 24 ], Alzheimer’s disease [ 24 , 25 ] or Amyotrophic Lateral Sclerosis [ 26 ]; the prediction of stress and cognitive load level [ 27 , 28 ]; and the detection of psychological pathologies, such as autism [ 29 ] or depression [ 30 ], which is the topic of this paper.…”

“…Conventional systems for speech-based health tasks consists of data-driven approaches based on hand-crafted acoustic features, such as pitch, prosody, loudness, rate of speech, and energies, among others, and a machine-learning algorithm such as Logistic Regression, Support Vector Machines (SVM) or Gaussian Mixture models [ 22 , 23 , 24 , 29 ]. Nevertheless, very recent works, such as, for example, [ 20 , 21 , 25 , 26 , 27 , 28 ], deal with the use of deep-learning techniques for these tasks, since, presently, these kinds of methods have achieved unprecedented successes in the field of automatic learning applied to signal processing, and particularly in image, video, and audio problems.…”