Speech rhythm alterations in Spanish-speaking individuals with Alzheimer's disease

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Objective

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.

Method

Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.

Results

Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.

Conclusion

Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Natural language processing techniques for studying language in pathological ageing: A scoping review

BACKGROUND

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria).

AIMS

To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach.

METHODS & PROCEDURES

We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews.

MAIN CONTRIBUTION

The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders).

CONCLUSIONS & IMPLICATIONS

Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.

WHAT THIS PAPER ADDS

What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

A tutorial on discourse analysis in healthy and pathological ageing

BACKGROUND

Age is a key factor when dealing with language and speech disorders, as it entails a progressive loss of neuroplasticity even in healthy individuals. Apart from this, ageing also affects our word-retrieval abilities, and thus, our discursive skills, particularly in people suffering from neurodegenerative diseases. Therefore, descriptions and/or measures of communicative performance always need to be interpreted through the lens of variation across the lifespan.

AIM

This paper's main objective is to create a general tutorial for researchers willing to start delving into discourse analysis, both in healthy and pathological ageing.

METHODS

An eight-step tutorial on discourse analysis in the elderly is presented. Each of these steps starts with general recommendations and progresses to more specific topics that may be relevant when conducting this type of research. All of the steps have been extrapolated from an extensive literature review on discourse analysis.

MAIN CONTRIBUTIONS

This work presents an easy-to-follow, step-by-step tutorial on discourse analysis in the elderly. It is aimed at clinical researchers who are taking their first steps in discourse analysis.It may also be useful for those who are already familiar with the methodology but may be interested in reading a general overview on the topic. Moreover, it offers new insights into the following topics: types of research questions, advantages and disadvantages of the different research methodologies and ethical considerations for data production in clinical linguistics.

CONCLUSIONS

Discourse analysis in the elderly is a highly complex issue that may require researching from different approaches and disciplines. This implies following a well-planned and thorough process, which we have detailed through the following eight steps: (i) reviewing literature; (ii) formulating the research question; (iii) designing the study; (iv) producing data; (v) selecting technological tools for data treatment; (vi) transcribing the corpus; (vii) annotating the corpus and (viii) analysing and interpreting the results.

WHAT THIS PAPER ADDS

What is already known on the subject Approaches in discourse analysis in elderly adults, and particularly, in people suffering from dementia have already been analysed by previous researchers and categorised into three main trends: the quantitative-experimental approach, the qualitative-naturalistic approach and an in-between path, the quantitative-naturalistic approach. Also, several handbooks on general discourse analysis have presented comprehensive revisions on potential resources and methodologies that can be applied to researching discourse in elderly populations. What this paper adds to existing knowledge This paper takes these three main approaches and analyses how the most recent research on language in ageing and dementia fits into them. Furthermore, it reviews the advantages and disadvantages each of them may bring for beginners in the field of discourse analysis. Moreover, it adds some studies that may fit into a fourth approach: the qualitative-experimental. This article also presents information about several of the main steps when analysing data from the pragmatic perspective: the formulation of the research question, data production and the transcription/annotation process. What are the potential or actual clinical implications of this work? This work has been devised for linguists who may want to read a systematization of the steps for analysing discourse in elderly populations. It may also be of interest to specialists from different fields such as speech therapy, psychology, gerontology or neurology who desire to start applying methods from discourse analysis in their work and aim to have a comprehensive scope of the main research trends within the field of clinical pragmatics.

Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities

OBJECTIVES

Automatic speech and language assessment methods (SLAMs) can help clinicians assess speech and language impairments associated with dementia in older adults. The basis of any automatic SLAMs is a machine learning (ML) classifier that is trained on participants' speech and language. However, language tasks, recording media, and modalities impact the performance of ML classifiers. Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment.

METHODOLOGY

Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment.

RESULTS

Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features.

CONCLUSION

This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants' speech; (2) Collect participants' voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. Our proposed methodology will help future researchers to investigate the impacts of different factors on the performance of ML classifiers for assessing dementia.

Speech biomarkers of risk factors for vascular dementia in people with mild cognitive impairment

Introduction

In this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.

Methods

In this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.

Results

We found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.

Discussion

The predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis

Background

Speech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening.

Objective

To better understand the use of automatic transcription for classification of neurodegenerative disease, namely, Alzheimer disease (AD), mild cognitive impairment (MCI), or subjective memory complaints (SMC) versus healthy controls, we compared automatically generated transcripts against transcripts that went through manual correction.

Methods

We recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-to-moderate AD, (n=44, 30%), MCI (n=20, 13%), SMC (n=8, 5%), as well as healthy controls (n=77, 52%) living in the community. Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used.

Results

The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models.

Conclusions

We found that automatically transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI, or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. However, manual addition of pauses is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data.

Changes in Speech Range Profile Are Associated with Cognitive Impairment

Background and Purpose

The aim of this study was to describe the variations in the speech range profile (SRP) of patients affected by cognitive decline.

Methods

We collected the data of patients managed for suspected voice and speech disorders, and suspected cognitive impairment. Patients underwent an Ear Nose and Throat evaluation and Mini-Mental State Examination (MMSE). To obtain SRP, we asked the patients to read 18 sentences twice, at their most comfortable pitch and loudness as they would do in daily conversation, and recorded their voice on to computer software.

Results

The study included 61 patients. The relationship between the MMSE score and SRP parameters was established. Increased severity of the MMSE score resulted in a statistically significant reduction in the average values of the semitones to the phonetogram, and the medium and maximum sound pressure levels (p<0.001). The maximum predictivity of MMSE was based on the highly significant values of semitones (p<0.001) and the maximum sound pressure levels (p=0.010).

Conclusions

The differences in SRP between the various groups were analyzed. Specifically, the SRP value decreased with increasing severity of cognitive decline. SRP was useful in highlighting the relationship between all cognitive declines tested and speech.

Reading Fluency in Spanish Patients with Alzheimer's Disease

BACKGROUND

Reading fluency is essential for our functioning in the literate society in which we live. Reading expressiveness or prosody, along with speed and accuracy, are considered key aspects of fluent reading. Prosodic patterns may vary, not being the same in children learning to read as in adulthood. But little is known about the prosodic characteristics and reading fluency of people with neurodegenerative diseases that causes language impairment and reading difficulties, such as Alzheimer's disease (AD).

OBJECTIVE

The aim of this work was to study reading fluency in AD, considering reading speed, accuracy and reading prosody.

METHODS

The participants were 20 healthy elderly Spanish adults, and 20 AD patients, aged 64-88 years. An experimental text was designed, that included declarative, exclamatory, and interrogative sentences, words with different stresses and low-frequency words. The reading of the participants was recorded and analyzed using Praat software.

RESULTS

The AD group showed significantly longer reading duration, both at the syllable level and at the word and sentence level. These patients also committed more pauses between words, which were also longer, and more reading errors. The control group showed a variation of the syllabic F0 in the three types of sentences, while these variations only appeared in declarative ones in the AD group.

CONCLUSION

The pauses, along with the slight pitch variations and the longer reading times and errors committed, compromise the reading fluency of people with AD. Assessment of this reading feature could be interesting as a possible diagnostic marker for the disease.

Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article

Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy.

Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists.

Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment.

Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.

[Use of new technologies in the diagnosis of neurodegenerative pathologies]

These last ten years, new technologies are more and more used in therapeutic and rehabilitation programms for patients with dementia, and used for the diagnosis of theses diseases, from the signal treatment. A review of litterature shows this growing interest among the scientific communauty for these new technologies.

A systematic literature review of automatic Alzheimer's disease detection from speech and language

OBJECTIVE

In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research.

MATERIALS AND METHODS

We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved?

RESULTS AND DISCUSSION

We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups.

CONCLUSION

The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

Convolutional neural network to explore the effect of the drug on postoperative POCD in elderly patients with hip fracture

The incidence of POCD may be further increased in elderly patients due to degenerative changes in the central nervous system. Once POCD occurs in these patients, it will not only prolong the length of hospital stay, increase medical expenses, but also seriously affect the quality of life of patients, delay the postoperative rehabilitation process, and bring a heavy burden to the family and society. Based on this, this study combines with image recognition technology to study the effect of trypsin inhibitor on postoperative POCD in elderly patients with hip fracture. The hip CT image segmentation algorithm based on concatenated convolutional neural network is used to realize the automatic phased segmentation of hip CT images. In addition, this study combines with image analysis to study the effect of trypsin inhibitor on postoperative POCD in elderly patients with hip fracture, and the image analysis method was based on the previous research methods. The research results show that the proposed method has certain effects.

Automatic speech analysis to early detect functional cognitive decline in elderly population

This study aimed at evaluating whether people with a normal cognitive function can be discriminated from subjects with a mild impairment of cognitive function based on a set of acoustic features derived from spontaneous speech. Voice recordings from 90 Italian subjects (age >65 years; group 1: 47 subjects with MMSE>26; group 2: 43 subjects with 20≤ MMSE ≤26) were collected. Voice samples were processed using a MATLAB-based custom software to derive a broad set of known acoustic features. Linear mixed model analyses were performed to select the features able to significantly distinguish between groups. The selected features (% of unvoiced segments, duration of unvoiced segments, % of voice breaks, speech rate, and duration of syllables), alone or in addition to age and years of education, were used to build a learning-based classifier. The leave-one-out cross validation was used for testing and the classifier accuracy was computed. When the voice features were used alone, an overall classification accuracy of 0.73 was achieved. When age and years of education were additionally used, the overall accuracy increased up to 0.80. These performances were lower than the accuracy of 0.86 found in a recent study. However, in that study the classification was based on several tasks, including more cognitive demanding tasks. Our results are encouraging because acoustic features, derived for the first time only from an ecologic continuous speech task, were able to discriminate people with a normal cognitive function from people with a mild cognitive decline. This study poses the basis for the development of a mobile application performing automatic voice analysis on-the-fly during phone calls, which might potentially support the detection of early signs of functional cognitive decline.

A Prototype for the Voice Analysis Diagnosis of Alzheimer's Disease

BACKGROUND

Speech variations enable us to map the performance of cognitive processes of syntactic, semantic, phonological, and articulatory planning and execution. Speaking is one of the first functions to be affected by neurodegenerative complaints such as Alzheimer's disease (AD), which makes the speech a highly promising biomarker for detecting the illness before the first preclinical symptoms appear.

OBJECTIVE

This paper has sought to develop and validate a technological prototype that adopts an automated approach to speech analysis among older people.

METHODS

It uses a mathematical algorithm based on certain discriminatory variables to estimate the probability of developing AD.

RESULTS AND CONCLUSION

This device may be used at a preclinical stage by non-expert health professionals to determine the likelihood of the onset of AD.

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Background: The discovery of early, non-invasive biomarkers for the identification of “preclinical” or “pre-symptomatic” Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals.

Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features.

Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI.

Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption.