Abstract
Aim:Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905-22) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.Methods:Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores.Results:Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].Conclusions:Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.
Highlights
Dementia is a syndrome that involves loss of cognitive function including memory, comprehension, and orientation, to a degree that impacts independent functioning
We evaluate the performance of the proposed expanded linguistic model in terms of true positive rate (TPR) for sub-classifications of cognitive impairment, including mild cognitive impairment Mini-Mental State Examination (MMSE) (MCI) and dementia
By tapping into the existing clinical diagnostic framework (e.g., neuropsychological tests (NPTs), MMSE), we can provide context as to which linguistics features are most well suited to predict cognitive status–through their comparison to what is currently used in clinical practice– and understand the value of speech markers
Summary
Dementia is a syndrome that involves loss of cognitive function including memory, comprehension, and orientation, to a degree that impacts independent functioning. Dementia poses a significant burden both socially and economically. According to the World Health Organization, dementia affects around 50 million people worldwide, with nearly 10 million new cases per year [1]. An estimated 16 million caregivers, often family members and friends, provide informal unpaid care for people with dementia, providing an estimated 18.6 billion hours of care valued at nearly $244 billion [3]. And accurate detection of cognitive decline can help promote optimal management of the disease, reducing the risk of accidents and injuries, as well as improving the experience of families and caregivers [4]. There is a need for simple, accessible, and noninvasive methods to determine cognitive status and the risk of developing dementia [5]
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