Abstract
Neurodegenerative diseases causing dementia are known to affect a person’s speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients’ verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehension. By capturing neurodegeneration-associated characteristics in a person’s voice, the incorporation of novel methods based on the automatic analysis of speech signals may give us more information about a person’s ability to interact which could contribute to the diagnostic process. In this proof-of-principle study, we demonstrate that purely acoustic features, extracted from recordings of patients’ answers to a neurologist’s questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The study involved 15 FMD and 15 ND patients where a total of 51 acoustic features were extracted from the recordings. Feature selection was used to identify the most discriminating features which were then used to train five different machine learning classifiers to differentiate between the FMD/ND classes, achieving a mean classification accuracy of 96.2%. The discriminative power of purely acoustic approaches could be integrated into diagnostic pathways for patients presenting with memory concerns and are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding.
Highlights
Memory complaints are common, increase with age and are a major reason for primary care consultations
A classification model was proposed in [16] to distinguish between three groups of subjects (AD, MCI and healthy elderly controls (HC)), and we achieved accuracies ranging from 89.2% to 92.4% when doing pairwise classification between the Alzheimer’s disease (AD), MCI and HC classes
The results of the study suggest that machine learning models based on the analyses of the acoustic data from patients with cognitive complaints are capable of detecting differences between the two classes, neurodegenerative disorders (ND) and FMD, in keeping with prior research [7,8,9, 14, 16]
Summary
Increase with age and are a major reason for primary care consultations. The drive to seek early diagnostic clarification has led to an over 600% increase in referrals to secondary care memory clinics in the UK over the last ten years and generated considerable pressure on diagnostic pathways [3]. These dramatic changes have increased the number of patients in whom neurodegenerative disorders have been identified, a large proportion of the patients referred to specialist memory clinics have functional (non-progressive) memory concerns without objective evidence of cognitive deficits. Biomarkers capable of identifying patients at high risk of developing the commonest cause of progressive cognitive decline, Alzheimer’s disease (AD), pre-symptomatically exist [5] but are either expensive and only available in very few centers (e.g. amyloid Positron Emission Tomography) or are invasive (e.g. amyloid and tau testing in the cerebrospinal fluid) and not suitable for for screening at the interface between primary and specialist care patients [6]
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