Background: Current conventional cognitive assessments are limited in their efficiency and
sensitivity, often relying on a single score such as the total correct items. Typically, multiple features of
response go uncaptured.
Objectives: We aim to explore a new set of automatically derived features from the Digit Span (DS)
task that address some of the drawbacks in the conventional scoring and are also useful for distinguishing
subjects with Mild Cognitive Impairment (MCI) from those with intact cognition.
Methods: Audio-recordings of the DS tests administered to 85 subjects (22 MCI and 63 healthy controls,
mean age 90.2 years) were transcribed using an Automatic Speech Recognition (ASR) system.
Next, five correctness measures were generated from Levenshtein distance analysis of responses: number
correct, incorrect, deleted, inserted, and substituted words compared to the test item. These per-item
features were aggregated across all test items for both Forward Digit Span (FDS) and Backward Digit
Span (BDS) tasks using summary statistical functions, constructing a global feature vector representing
the detailed assessment of each subject’s response. A support vector machine classifier distinguished
MCI from cognitively intact participants.
Results: Conventional DS scores did not differentiate MCI participants from controls. The automated
multi-feature DS-derived metric achieved 73% on AUC-ROC of the SVM classifier, independent of
additional clinical features (77% when combined with demographic features of subjects); well above
Conclusion: Our analysis verifies the effectiveness of introduced measures, solely derived from the DS
task, in the context of differentiating subjects with MCI from those with intact cognition.