Scientifically substantiated

Overview

In the following we give an overview of the most important publications that form the scientific basis of the ∆elta platform. Below is a list of all our publications. An overview of all (clinical) studies in which ∆elta was or is used can be found on the studies page.

Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

Alexandra König, Aharon Satt, Alexander Sorin, Ron Hoory, Orith Toledo‐Ronen, Alexandre Derreumaux, Valeria Manera, Frans Verhey, Pauline Aalten, Phillipe H. Robert, Renaud David  (2015). Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring1(1), 112-124.

Background: To evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer’s disease (AD).

Methods: Healthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy.

Results: The classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% 6 5%; between HCs and those with AD, 87% 6 3%; and between those with MCI and those with AD, 80% 6 5%, demonstrating its assessment utility.

Conclusion: Automatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.

Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people

Alexandra König, Aharon Satt, Alex Sorin, Ran Hoory, Alexandre Derreumaux, Renaud David, Phillippe Robert (2018). Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Current Alzheimer Research15(2), 120-129.

Background: Various types of dementia and Mild Cognitive Impairment (MCI) are mani- fested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile applica- tion may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI.

Method: 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alz- heimer’s disease (AD) and mixed dementia (MD) patients) were recorded with a mobile application while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description, counting down and a free speech task. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their ‘power’ to distinguish between SCI, MCI, AD and MD. The second step included training automatic classifiers for detecting MCI and AD, based on machine learning methods, and testing the detection accuracy.

Results: The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs. MD vs. MCI vs. SCI. Using the data, we demonstrated classification accuracy as follows: SCI vs. AD = 92% accuracy; SCI vs. MD = 92% accuracy; SCI vs. MCI = 86% accuracy and MCI vs. AD = 86%.

Conclusions: Our results indicate the potential value of vocal analytics and the use of a mobile applica- tion for accurate automatic differentiation between SCI, MCI and AD. This tool can provide the clini- cian with meaningful information for assessment and monitoring of people with MCI and AD based on a non-invasive, simple and low-cost method.

Fully automatic speech-based analysis of the semantic verbal fluency task

Alexandra König, Nicklas Linz, Johannes Tröger, Maria Wolters, Jan Alexandersson, Phillipe Robert (2018). Fully automatic speech-based analysis of the semantic verbal fluency task. Dementia and geriatric cognitive disorders45(3-4), 198-209.

Background: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment.

Methods: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer’s or related de- mentias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manu- ally and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD.

Results: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758).

Conclusion: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

Detecting apathy in older adults with cognitive disorders using automatic speech analysis

Alexandra König, Nicklas Linz, Radia Zeghari, Xenia Klinge, Johannes Tröger, Jan Alexandersson, Philippe Robert (2019). Detecting apathy in older adults with cognitive disorders using automatic speech analysis. Journal of Alzheimer’s Disease69(4), 1183-1193

Background: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. Objective: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy.


Methods: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers.

Results: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females.

Conclusions: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy.

All publications

Hali Lindsay, Johannes Tröger, Nicklas Linz, Jan Alexandersson, Johannes Prudlo (2019)
ExLing 2019

Johannes Tröger, Nicklas Linz, Alexandra König, Philippe Robert, Jan Alexandersson, Jessica Peter, Jutta Kray (2019)
Neuropsychologia, 131, 53-61.

Nicklas Linz, Kristina Lundholm Fors, Hali Lindsay, Marie Eckerström, Jan Alexandersson, Dimitrios Kokkinakis (2019)
Sixth Workshop on Computational Linguistics and Clinical Psychology (CLPsych)

Kathleen C Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis (2019)
NAACL 2019

Kathleen C Fraser, Nicklas Linz, Hali Lindsay, Alexandra König (2019)
Sixth Workshop on Computational Linguistics and Clinical Psychology (CLPsych)

Hali Lindsay, Nicklas Linz, Johannes Troeger, Jan Alexandersson (2019)
3rd International Conference on Natural Language and Speech Processing

Alexandra König, Nicklas Linz, Radia Zeghari, Xenia Klinge, Johannes Tröger, Jan Alexandersson, Philippe Robert (2019)
Journal of Alzheimer’s Disease69(4), 1183-1193.

Nicklas Linz, Johannes Tröger, Hali Lindsay, Alexandra Konig, Philippe Robert, Jessica Peter, Jan Alexandersson (2018)
Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-18)

Johannes Tröger, Nicklas Linz, Alexandra König, Philippe Robert, Jan Alexandersson (2018)
12th EAI International Conference on Pervasive Computing Technologies for Healthcare

Alexandra König, Nicklas Linz, Johannes Tröger, Maria Wolters, Jan Alexandersson, Phillipe Robert (2018)
Dementia and geriatric cognitive disorders45(3-4), 198-209.

Alexandra König, Aharon Satt, Alex Sorin, Ran Hoory, Alexandre Derreumaux, Renaud David, Phillippe Robert (2018)
Current Alzheimer Research15(2), 120-129.

Nicklas Linz, Johannes Tröger, Jan Alexandersson, Maria Wolters, Alexandra König, Philippe Robert (2017)
IEEE International Conference on Data Mining Workshops (ICDMW)

Johannes Tröger, Nicklas Linz, Jan Alexandersson, Alexandra König, Philippe Robert (2017)
11th EAI International Conference on Pervasive Computing Technologies for Healthcare

Nicklas Linz, Johannes Tröger, Jan Alexandersson, Alexandra König (2017)
IWCS 2017

Alexandra König, Aharon Satt, Alexander Sorin, Ron Hoory, Orith Toledo‐Ronen, Alexandre Derreumaux, Valeria Manera, Frans Verhey, Pauline Aalten, Phillipe H. Robert, Renaud David (2015)
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring1(1), 112-124.

Aharon Satt, Ron Hoory, Alexandra König, Pauline Aalten, Philippe Robert (2014)
INTERSPEECH 2014