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Kaser AN, Lacritz LH, Winiarski HR, Gabirondo P, Schaffert J, Coca AJ, Jiménez-Raboso J, Rojo T, Zaldua C, Honorato I, Gallego D, Nieves ER, Rosenstein LD, Cullum CM. A novel speech analysis algorithm to detect cognitive impairment in a Spanish population. Front Neurol 2024; 15:1342907. [PMID: 38638311 PMCID: PMC11024431 DOI: 10.3389/fneur.2024.1342907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Objective Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population. Method Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores. Results Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively. Conclusion Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.
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Affiliation(s)
- Alyssa N. Kaser
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Laura H. Lacritz
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Holly R. Winiarski
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | | | - Jeff Schaffert
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Alberto J. Coca
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
- Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, United Kingdom
| | | | - Tomas Rojo
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | - Carla Zaldua
- AcceXible Impacto, Sociedad Limitada, Bilbao, Spain
| | | | | | - Emmanuel Rosario Nieves
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - Leslie D. Rosenstein
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Parkland Health and Hospital System Behavioral Health Clinic, Dallas, TX, United States
| | - C. Munro Cullum
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
- Department of Neurological Surgery, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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Gagliardi G. Natural language processing techniques for studying language in pathological ageing: A scoping review. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:110-122. [PMID: 36960885 DOI: 10.1111/1460-6984.12870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria). AIMS To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach. METHODS & PROCEDURES We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews. MAIN CONTRIBUTION The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders). CONCLUSIONS & IMPLICATIONS Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice. WHAT THIS PAPER ADDS What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.
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Affiliation(s)
- Gloria Gagliardi
- Department of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy
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Benítez-Burraco A, Ivanova O. Language in healthy and pathological ageing: Methodological milestones and challenges. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:4-12. [PMID: 38149881 DOI: 10.1111/1460-6984.13003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Affiliation(s)
- Antonio Benítez-Burraco
- Department of Spanish, Linguistics and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, Sevilla, Spain
| | - Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca/Institute of Neuroscience of Castilla y León (INCYL), Salamanca, Spain
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Cabrera-León Y, Báez PG, Fernández-López P, Suárez-Araujo CP. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review. J Alzheimers Dis 2024; 98:793-823. [PMID: 38489188 PMCID: PMC11091566 DOI: 10.3233/jad-231271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2024] [Indexed: 03/17/2024]
Abstract
Background The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too. Background Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
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Affiliation(s)
- Ylermi Cabrera-León
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Patricio García Báez
- Departamento de Ingeniería Informática y de Sistemas, Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, San Cristóbal de La Laguna, Canary Islands, Spain
| | - Pablo Fernández-López
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Carmen Paz Suárez-Araujo
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
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Zolnoori M, Zolnour A, Topaz M. ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia. Artif Intell Med 2023; 143:102624. [PMID: 37673583 PMCID: PMC10483114 DOI: 10.1016/j.artmed.2023.102624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 06/22/2023] [Accepted: 07/08/2023] [Indexed: 09/08/2023]
Abstract
Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.
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Affiliation(s)
- Maryam Zolnoori
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America.
| | - Ali Zolnour
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Maxim Topaz
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America
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Qi X, Zhou Q, Dong J, Bao W. Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review. Front Aging Neurosci 2023; 15:1224723. [PMID: 37693647 PMCID: PMC10484224 DOI: 10.3389/fnagi.2023.1224723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/04/2023] [Indexed: 09/12/2023] Open
Abstract
Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.
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Affiliation(s)
- Xiaoke Qi
- School of Information Management for Law, China University of Political Science and Law, Beijing, China
| | | | - Jian Dong
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
| | - Wei Bao
- Information Technology Research Center, China Electronics Standardization Institute, Beijing, China
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Martínez-Nicolás I, Martínez-Sánchez F, Ivanova O, Meilán JJG. Reading and lexical-semantic retrieval tasks outperforms single task speech analysis in the screening of mild cognitive impairment and Alzheimer's disease. Sci Rep 2023; 13:9728. [PMID: 37322073 PMCID: PMC10272227 DOI: 10.1038/s41598-023-36804-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Age-related cognitive impairment have increased dramatically in recent years, which has risen the interes in developing screening tools for mild cognitive impairment and Alzheimer's disease. Speech analysis allows to exploit the behavioral consequences of cognitive deficits on the patient's vocal performance so that it is possible to identify pathologies affecting speech production such as dementia. Previous studies have further shown that the speech task used determines how the speech parameters are altered. We aim to combine the impairments in several speech production tasks in order to improve the accuracy of screening through speech analysis. The sample consists of 72 participants divided into three equal groups of healthy older adults, people with mild cognitive impairment, or Alzheimer's disease, matched by age and education. A complete neuropsychological assessment and two voice recordings were performed. The tasks required the participants to read a text, and complete a sentence with semantic information. A stepwise linear discriminant analysis was performed to select speech parameters with discriminative power. The discriminative functions obtained an accuracy of 83.3% in simultaneous classifications of several levels of cognitive impairment. It would therefore be a promising screening tool for dementia.
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Affiliation(s)
| | | | - Olga Ivanova
- Faculty of Philology, University of Salamanca, 37008, Salamanca, Spain
| | - Juan J G Meilán
- Faculty of Psychology, University of Salamanca, 37008, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, 37007, Salamanca, Spain
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