Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data (2022)

Abstract: A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating depression from dementia are rare. Co-morbid depression is frequent in dementia and these clinical conditions share many overlapping symptoms, but the ability to distinguish between depression and dementia is essential as depression is often curable. In this work, we investigate the ability of clustering approaches in distinguishing between depression and dementia from human speech. We introduce a novel aggregated dataset, which combines narrative speech data from multiple conditions, i.e., Alzheimer’s disease, mild cognitive impairment, healthy control, and depression. We compare linear and non-linear clustering approaches and show that non-linear clustering techniques distinguish better between distinct disease clusters. Our interpretability analysis shows that the main differentiating symptoms between dementia and depression are acoustic abnormality, repetitiveness (or circularity) of speech, word finding difficulty, coherence impairment, and differences in lexical complexity and richness.

## introduction depressive disorder and dementia are clinical conditions that both impose a substantial cost globally in terms of mortality and morbidity and have a significant negative impact on social and economic productivity @xcite . distinguishing between these conditions has proven to be a challenging task @xcite as they frequently co-occur and have many overlapping symptoms such as apathy @xcite , changes in sleep patterns @xcite , and concentration issues @xcite . however, de-pression is generally curable by either psychotherapy or medication, while dementia is a neurodegenerative disease, which is caused by irreversible deterioration of the nervous system. it is hence crucial to differentiate between these two conditions @xcite . previous studies demonstrated that machine learning methods and speech analysis are useful in detecting dementia from depression @xcite . however, the machine learning methods used in prior studies suffer from three main limitations: firstly, the datasets applied in prior literature only comprise alzheimer's disease (ad), healthy control (hc), and depression (depr) samples of senior participants with similar demographic distributions and recording environments @xcite . in real world settings, the datasets are very noisy due to variations in the data collection procedures. additionally, dementia is not necessarily of the ad type in all cases, and other types of dementia like mild cognitive impairment (mci) can be included. secondly, to the best of our knowledge, previous studies have only used classification approaches to detect ad from hc @xcite @xcite , depr from hc @xcite , or ad from depr @xcite using speech. this might not be an ideal simulation of the real world diagnosis procedure. in clinical diagnosis, the first step is to detect the symptoms and explore the pattern changes in patient records before diagnosing the disease @xcite , while in classification, we first map the samples to the disease labels and then, apply interpretability methods to explore the differentiating features between the classes @xcite . lastly, prior studies demonstrated that acoustic and linguistic features extracted from spontaneous speech provide valuable indicators of both mental disorders such as depression @xcite and cognitive impairment like ad or mci @xcite . however, they did not derive a strong conclusion about the main distinguishing speech-based symptoms in classifying dementia from depression @xcite . to address the first limitation, we generate a novel aggregated dataset, which combines several speech datasets comprising ad, mci, hc, and depr labels with a variety of data collection procedures. to address the second and third limitations, we introduce a novel approach, which applies clustering techniques to inspect what data-driven feature categories (symptoms) are the main differentiators between ad, mci, depr, and hc samples. we then use the distinguishing symptoms as a feature selection technique to classify ad, mci, and depr. our key findings indicate that 1) the non-linear clustering approaches outperform the linear techniques in terms of separability level of distinct disease clusters; 2) acoustic abnormalities, variations in lexical complexity and richness, repetitiveness (or circularity) of speech, word finding difficulty, and coherence impairment are the main differentiating symptoms to distinguish between different types of dementia (e.g., ad and mci), and depr; 3) data-driven differentiators are able to substantially improve performance of classification across diseases. ## related work there has been a substantial number of studies on detecting either dementia (e.g., mci or ad) or depression from spontaneous speech. however, little has been done to distinguish dementia from depression using discourse patterns. to discriminate dementia from depression, @xcite applied speech data from the pitt corpus in the dementiabank database @xcite , elicited from elderly participants through picture description task, with 'cookie theft' @xcite used as a picture. the samples were labeled as either ad or hc based on a personal history and a neuropsychological assessment battery @xcite . a subset of the samples were labeled as depressed or nondepressed based on the established threshold on hamilton depression rating scale (ham-d) test scores @xcite . to explore the distin-guishing discourse patterns between ad and depr, @xcite collected a speech dataset of elderly participants (with depr, ad, or hc labels) who completed a picture description task, with norman rockwell's painting 'the soldier' used as a picture. samples with depr were diagnosed based on dsm-iv criteria @xcite and samples with ad met nincds-adrda criteria @xcite for probable ad. the datasets used in these studies didn't include other types of dementia such as mci, and all of their samples followed the same data collection procedure, while we create an aggregated dataset, which consists of ad, mci, hc, and depr samples from different speech datasets with various data collection procedures. @xcite examined whether elderly individuals with depression can be distinguished from those at early stages of ad through distinct patterns in narrative speech. based on their findings, individuals with ad generated less informative speech compared to the depressed patients in their picture descriptions, while there were no significant differences in the informativeness of the narratives between hc and depr samples. furthermore, quantitative and syntactic measures of discourse did not differ across the three groups. however, @xcite did not attempt to make predictions using the data. @xcite investigated if the automated ad screening tools misclassify cognitively healthy participants with depr as ad when using narrative speech. they also used linguistic and acoustic features to classify non-depressed ad subjects from those with comorbid depression from speech elicited through picture description task. in their study, they compared logistic regression (lr) with support vector machines (svm) classification models. their performance in distinguishing between depressed and non-depressed ad samples was moderate (@xmath0.658) due to a wide range of overlapping symptoms. in addition, they only applied classification approaches and they didn't derive the most informative features discriminating between ad patients with and without depression. in the present work, we apply clustering approaches to cluster the diseases based on the similarities in the discourse patterns, and apply interpretability techniques to explore the distinguishing feature categories (symptoms) between distinct diagnosis labels (i.e., hc, ad, mci, and depr). we use the differentiating symptoms as a feature selection technique to classify the diseases. ## conclusion in this work, we generate a novel aggregated dataset composed of a number of speech corpora including a combination of different clinical conditions (e.g., ad, mci, hc, and depr). we extract a hand-crafted set of acoustic and linguistic features derived from speech data, which are used as model predictors for discriminating between the diagnosis labels and we categorize these features under data-driven feature categories in line with the clinical symptoms of these diseases. we cluster the samples into distinguishable disease clusters and examine what speech symptoms are the main differentiating factors between the diseases. based on our findings, non-linear clustering approaches outperform the linear ones in terms of distinguishing between distinct disease clusters. our results signify that acoustic abnormality, repetitiveness, or circularity of speech, word finding difficulty, coherence, and differences in lexical complexity and richness are the main differentiating symptoms between different types of dementia (e.g., mci and ad), and depression.