A machine learning approach for predicting post-stroke aphasia recovery

Gu, Yiwen; Bahrani, Murtadha; Billot, Anne; Lai, Sha; Braun, Emily J.; Varkanitsa, Maria; Bighetto, Julia; Rapp, Brenda; Parrish, Todd B.; Caplan, David; Thompson, Cynthia K.; Kıran, Swathi; Betke, Margrit

doi:10.1145/3389189.3389204

Cited by 9 publications

(10 citation statements)

References 46 publications

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“…3310 sentences English [ 111 ] CMUArctic Festvox 122 Dysarthria 5 M, and 2 F. 1150 utterances English [ 123 ] EMA 97 - Dysarthria 3 (1 M, 2 F), 680 utterances English [ 124 ] IEMOCAP 125 - Dysarthria 1 M and 1 F. 3900 utterances English: USA [ 124 ] Parkinsons 28 Parkinsons 126 Parkinson’s disease 23 with, and 8 without English [ 127 ] [ 70 ] - Parkinson’s disease 20 with and 20 without. 1040 speech signals Turkish [ 70 ] Spanish datasets 13 - Dysphagia 46 with (23 M, 23 F), and 46 without (23 M, 23 F) Spanish [ 13 ] [ 11 ] - Aphasia 65 with (- M, - F), and 15 without (- M, - F) English [ 11 ] Aphasic Speech Corpus 128 <...…”

Section: Discussionmentioning

confidence: 99%

“…One significant gap lies in the limited availability of large, balanced, and high-quality annotated datasets for training and evaluating machine learning models for speech disorders. 7 , 11 , 12 , 102 , 105 , 117 , 120 , 123 , 138 The scarcity of such datasets limits the generalizability and validity of the results, leading to increased ambiguity and reduced statistical control. Moreover, it hinders the ability to draw strong inferences and develop and evaluate robust algorithms.…”

Section: Gaps and Future Directionsmentioning

confidence: 99%

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Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

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Purpose Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014–2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

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Section: Discussionmentioning

confidence: 99%

Section: Gaps and Future Directionsmentioning

confidence: 99%

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

View full text Add to dashboard Cite

show abstract

“…The choice of the CML’s classifiers in this paper was influenced by the comparative performance investigation reported in [ 19 , 25 ], which found that the random forest (RF) and a radial basis function (RBF) kernel SVM outperformed 14 other classification algorithms. However, other classification algorithms such as fuzzy and neuro-fuzzy-based techniques can be used when the data is affected by uncertainty and/or inaccuracies [ 26 , 27 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

“…The majority of aphasia research uses speech utterance for aphasia diagnosis and assessment. However, some researchers have used a neuroimaging dataset instead of a speech dataset to diagnose aphasia [ 19 , 20 , 21 ]. In [ 20 ], Kristinsson et al used ML techniques to predict aphasia severity and specific language measures based on a multimodal neuroimaging dataset.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Mahmoud

Kumar

et al. 2021

Sensors

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Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

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“…Future directions towards predicting post-stroke recovery and treatment outcomes could be machine-learning algorithms, which classify and predict the participants' responsiveness to therapy [75]. In this pilot study, 64 post-stroke aphasia patients were analyzed, the predictive framework was created based on collected data-demographic, brain structure (MRI) and behavioral (clinical scales of aphasia severity)-and then Random Forest models were used to evaluate the importance of these features.…”

Section: Future Directions-multimodal Panels Of Neuroimaging Biomarke...mentioning

confidence: 99%

Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis

Włodarczyk

Cichoń

Saluk‐Bijak

et al. 2022

JCM

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Stroke as the most frequent cause of disability is a challenge for the healthcare system as well as an important socio-economic issue. Therefore, there are currently a lot of studies dedicated to stroke recovery. Stroke recovery processes include angiogenesis and neuroplasticity and advances in neuroimaging techniques may provide indirect description of this action and become quantifiable indicators of these processes as well as responses to the therapeutical interventions. This means that neuroimaging and neurophysiological methods can be used as biomarkers—to make a prognosis of the course of stroke recovery and define patients with great potential of improvement after treatment. This approach is most likely to lead to novel rehabilitation strategies based on categorizing individuals for personalized treatment. In this review article, we introduce neuroimaging techniques dedicated to stroke recovery analysis with reference to angiogenesis and neuroplasticity processes. The most beneficial for personalized rehabilitation are multimodal panels of stroke recovery biomarkers, including neuroimaging and neurophysiological, genetic-molecular and clinical scales.

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A machine learning approach for predicting post-stroke aphasia recovery

Cited by 9 publications

References 46 publications

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment

Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis

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