Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

Maxhuni, Alban; Muñoz-Meléndez, Angélica; Osmani, Venet; Perez, Humberto; Mayora, Oscar; Morales, Eduardo F.

doi:10.1016/j.pmcj.2016.01.008

Cited by 75 publications

(99 citation statements)

References 24 publications

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“…83 Furthermore, a significantly higher number of longer pauses were observed in depressive states than in euthymic or hypomanic states, 81 and speech pauses became longer as patients transitioned into depressive states. 13 Changes in speech in depression and mania are well known clinically as they are captured by the psychomotor retardation item in the Hamilton Depression Rating 84 and the speech rate item on the Young Mania Rating Scale 85 ; however, speech rate was not identified in the reviewed studies, and therefore, remains promising for future studies.…”

Section: Bipolar Disordermentioning

confidence: 99%

“…• Already done in many clinics • By not recording other caller, no need for diarization. Smartphones provide accelerometer data 13 test hypotheses, 74,88 we have not encountered preregistrations, which is becoming a common practice in other scientific fields.…”

Section: Preregister the Model Building Protocolmentioning

confidence: 99%

“…Technologies such as MultiSense can be used to measure facial expressions, body gestures, smile‐frown dynamics, and eye contact. Many smartphones can measure ambient light, moisture, pressure, gait, location, and acceleration, steps taken, some of which are used to detect psychiatric disorders . Features extracted from handwriting have shown to indicate anxiety and stress .…”

Section: Introductionmentioning

confidence: 99%

“…Out of the 127 studies included in this review, 32% used AVEC data sets. The creation of apps to collect data such as MONARCA13,33 or PRIORI 66 apps for hypomanic and depressive state detection in bipolar disorder has also helped promote studies. Schizophrenia, on the other hand, has been studied less from the machine learning field since 87% of studies performed null-hypothesis testing only.Regarding performance in predictive models, few studies used held-out test sets.…”

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confidence: 99%

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Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

316

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Section: Bipolar Disordermentioning

confidence: 99%

Section: Preregister the Model Building Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

316

268

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“…Une autre équipe a utilisé le système MONARCA [26] sur un faible nombre de patients (n = 5), mais en associant à l'application un système autonome qui élargit la collecte des données passives avec notamment des données audio (via le microphone du smartphone) et des données motrices (via l'accéléromètre et le GPS). Concernant les données motrices, les résultats sont concordants avec ceux des autres études avec notamment une augmentation de l'activité associée aux symptômes maniaques et une diminution associée aux symptômes dépressifs.…”

Section: éValuation Par Smartphoneunclassified

Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre

et al. 2018

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All these systems can be used to computerize the clinical characteristics of the various mental states studied, sometimes with greater precision than a clinician could do. Most authors recommend the use of passive data rather than active data in the context of bipolar disorders because automatically generated data reduce biases and limit the feeling of intrusion that self-questionnaires may cause. The impact of these technologies questions the psychiatrist's professional culture, defined as a specific language and a set of common values. We address issues related to these changes. Impact on psychiatrists could be important because their unity seems to be questioned due to technologies that profoundly modify the collect and process of clinical data.

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A survey of autonomous monitoring systems in mental health

Gopalakrishnan,

Gururajan,

Zhou

et al. 2024

WIREs Data Min & Knowl

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Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted.This article is categorized under: Technologies > Machine Learning Technologies > Prediction Application Areas > Health Care

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Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

Cited by 75 publications

References 24 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre

A survey of autonomous monitoring systems in mental health

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