Automated Screening for Bipolar Disorder from Audio/Visual Modalities

Syed, Zafi Sherhan; Sidorov, Kirill; Marshall, David

doi:10.1145/3266302.3266315

Cited by 26 publications

(22 citation statements)

References 35 publications

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“…Wearables, including watches, rings, and clothes that measure biological and behavioral indices such as temperature, skin conductance, movement, and heart rate, can be potential indicators of anxiety and depression, and used to provide biofeedback . Features obtained from video recordings have been used to detect depression and bipolar disorder . Technologies such as MultiSense can be used to measure facial expressions, body gestures, smile‐frown dynamics, and eye contact.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

289

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: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

289

268

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“…This suggests that machine learning models trained on these embeddings carry complimentary information which can be fused together in order to achieve improved classification accuracy. This is a well known premise of decision-level fusion [31] and we have had success at improving the quality of machine learning models using fusion in our previous works [32], [16].…”

Section: Decision-level Fusionmentioning

confidence: 91%

Deep Acoustic Embeddings for Identifying Parkinsonian Speech

Syed¹,

Memon²,

Memon³

2020

IJACSA

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Parkinson's disease is a serious neurological impairment which adversely affects the quality of life in individuals. While there currently does not exist any cure for this disease, it is well known that early diagnosis can be used to improve the quality of life of affected individuals through various types of therapy. Speech based screening of Parkinson's disease is an active area of research intending to offer a non-invasive and passive tool for clinicians to monitor changes in voice that arise due to Parkinson's disease. Whereas traditional methods for speech based identification rely on domain-knowledge based hand-crafted features, in this paper, we investigate the efficacy of and propose the deep acoustic embeddings for identification of Parkinsonian speech. To this end, we conduct several experiments to benchmark deep acoustic embeddings against handcrafted features for differentiating between speech from individuals with Parkinson's disease and those who are healthy. We report that deep acoustic embeddings consistently perform better than domain-knowledge features. We also report on the usefulness of decision-level fusion for improving the classification performance of a model trained on these embeddings.

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“…Feature aggregation has been a key component of successful systems for speech paralinguistic tasks [64]- [66], and in recent years we have also had success using these methods [67]- [69]. In this work, we experimented with three types of feature aggregation methods that are based on functionals, Bag of Words [70], and Fisher Vector Encoding [71].…”

Section: Feature Aggregationmentioning

confidence: 99%

“…Fusion is particularly useful when machine learning models are trained and optimized for relatively small datasets. We have had great success previously with label fusion for yielding an improvement in the performance of machine learning models [26], [53], [69].…”

Section: Machine Learningmentioning

confidence: 99%

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Syed

Lech

et al. 2021

IEEE Access

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Alzheimer's dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neuralembeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer's Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.

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Automated Screening for Bipolar Disorder from Audio/Visual Modalities

Cited by 26 publications

References 35 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

Deep Acoustic Embeddings for Identifying Parkinsonian Speech

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

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