A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

Schwoebel, James W.; Schwartz, Jeffrey; Warrenburg, Lindsay; Brown, Roland; Awasthi, Ashi; New, Austin; Butler, Monroe; Moss, Mark B.; Pissadaki, Eleftheria Kyriaki

doi:10.1101/2021.08.16.21262125

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…After literature screening for this review, a publication was released, which showcases the highlighted points for everyday-life data collection ( 81 ). The authors managed to gather voice samples via a web app of over 6,650 participants, of which roughly 10 % reported to be depressed.…”

Section: Discussionmentioning

confidence: 99%

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker

Steckhan

Eyben

et al. 2022

Front. Digit. Health

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Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

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Section: Discussionmentioning

confidence: 99%

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker

Steckhan

Eyben

et al. 2022

Front. Digit. Health

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“…This step consists of voice data collection coupled with well-documented clinical data in screening platforms such as Colive Voice [ 30 ] or large prospective cohort studies [ 2 , 31 ]. The collected data have to be diverse enough and should represent the target population in terms of languages, accents, and socioeconomic backgrounds to decrease the risk of systemic biases and the risk of increasing a potential preexisting digital and socioeconomic divide in the population.…”

Section: Development Of a Digital Health Solution Based On Vocal Biom...mentioning

confidence: 99%

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Fischer¹,

Elbéji²,

Aguayo³

et al. 2022

Interact J Med Res

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The COVID-19 pandemic accelerated the use of remote patient monitoring in clinical practice or research for safety and emergency reasons, justifying the need for innovative digital health solutions to monitor key parameters or symptoms related to COVID-19 or Long COVID. The use of voice-based technologies, and in particular vocal biomarkers, is a promising approach, voice being a rich, easy-to-collect medium with numerous potential applications for health care, from diagnosis to monitoring. In this viewpoint, we provide an overview of the potential benefits and limitations of using voice to monitor COVID-19, Long COVID, and related symptoms. We then describe an optimal pipeline to bring a vocal biomarker candidate from research to clinical practice and discuss recommendations to achieve such a clinical implementation successfully.

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“…The idea of using vocal acoustic features as potential biomarkers for identifying or diagnosing depression is an emerging area of research within the field of computational psychiatry and affective computing. The premise behind this approach is that changes in vocal characteristics, such as pitch, tone, rhythm, and intensity, may reflect underlying emotional states, including depression (Schwoebel et al, 2021 ; Sverdlov et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

Monaco,

Vignapiano,

Piacente

et al. 2024

Front. Artif. Intell.

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BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.MethodsThis prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source “Pothos” air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.ResultsFollowing initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.ConclusionThis study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

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A Longitudinal Normative Dataset and Protocol for Speech and Language Biomarker Research

Cited by 6 publications

References 53 publications

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

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