Correction: Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia

Orimaye, Sylvester Olubolu; Wong, Jojo Sze-Meng; Wong, Chin Chin

doi:10.1371/journal.pone.0214103

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…As far as we know, SOTA on Pitt corpus is the study of Roshanzamir et al (48) in 2021, and our method in this paper performs better than SOTA. Also, transformer+FP 25 is the feature project method for text classification, and we did the experiment with this method on our Pitt datasets; the performance of our method is better than the project method with the same datasets. To further compare with some proposed popular pretrained models in recent years, including Bert (37), ERNIE (38), RCNN (31), and DPCNN (32), we do the comparative experiments with the combination models, including BertRCNN, BertDPCNN, BertLogistic, and ERNIEDPCNN models, which are the combination of Bert + CNN, Bert + RCNN, Bert + DPCNN, Bert + Logistic Regression, and ERNIE + DPCNN, respectively.…”

Section: Experiments Resultsmentioning

confidence: 99%

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Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Liu

Yuan

Tang

2022

Front. Public Health

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Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.

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Section: Experiments Resultsmentioning

confidence: 99%

“…Existing studies on AD diagnosis across spontaneous speech mainly focus on two aspects. One is feature extraction manually including acoustic features (19)(20)(21), linguistic features (22)(23)(24)(25), or their combinations (21). This method is subjective and needs more professional knowledge.…”

Section: Related Workmentioning

confidence: 99%

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Liu

Yuan

Tang

2022

Front. Public Health

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“…According to the DSM-5 (American Psychiatric Association, 2014), different variables such as attention, executive function, learning and memory, perceptual motor skills, social recognition, and language, are variables of interest in the analysis of cognitive functioning in older adults. While the study of memory is one of the main targets in the analysis of cognitive decline in cases such as Alzheimer’s disease (AD), the literature has also documented a progressive decline in language skills [ 1 , 2 , 3 ]. Obviously, this decline seems to be more pronounced in AD than in normal ageing.…”

Section: Introductionmentioning

confidence: 99%

Predicting Scores on the Mini-Mental State Examination (MMSE) from Spontaneous Speech

Bueno-Cayo

Carmona

Castell-Enguix

et al. 2022

Behavioral Sciences

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The aim of this study was to examine the relationship between language components, such as lexical density, length, and content in terms of “Time, Space and Action”, with MMSE scores. For this reason, a group of 33 older participants, without a diagnosis of dementia, was examined, providing information regarding recent and future events. Participants with higher MMSE scores showed higher lexical density, speech length, as well as number of tokens related to Time, Place and Action in their speech. However, these differences only reach the statistical level for lexical density when participants were divided into two groups (MCI and healthy controls). Word frequency was lower for participants with MCI but this difference was not statistically significant. Lastly, lexical density was positively correlated with MMSE scores and predicted MMSE scores. These results could be of interest at the applied level in the screening of MCI.

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Automated text‐level semantic markers of Alzheimer's disease

Sanz

Carrillo

Slachevsky

et al. 2022

Alz & Dem Diag Ass & Dis Mo

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Introduction Automated speech analysis has emerged as a scalable, cost‐effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity. Methods Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients. Results Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near‐chance classification between PD patients and HCs. Discussion Automated discourse‐level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well‐established neuropsychological targets with digital assessment tools.

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Correction: Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia

Cited by 15 publications

References 2 publications

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Predicting Scores on the Mini-Mental State Examination (MMSE) from Spontaneous Speech

Automated text‐level semantic markers of Alzheimer's disease

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