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Huang L, Yang H, Che Y, Yang J. Automatic speech analysis for detecting cognitive decline of older adults. Front Public Health 2024; 12:1417966. [PMID: 39175901 PMCID: PMC11338907 DOI: 10.3389/fpubh.2024.1417966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/24/2024] [Indexed: 08/24/2024] Open
Abstract
Background Speech analysis has been expected to help as a screening tool for early detection of Alzheimer's disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China. Objective Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers. Methods A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared. Results The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features. Conclusion Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data.
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Affiliation(s)
- Lihe Huang
- School of Foreign Studies, Tongji University, Shanghai, China
- Research Center for Ageing, Language and Care, Tongji University, Shanghai, China
| | - Hao Yang
- Research Center for Ageing, Language and Care, Tongji University, Shanghai, China
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China
| | - Yiran Che
- School of Foreign Studies, Tongji University, Shanghai, China
- Research Center for Ageing, Language and Care, Tongji University, Shanghai, China
| | - Jingjing Yang
- School of Foreign Studies, Tongji University, Shanghai, China
- Research Center for Ageing, Language and Care, Tongji University, Shanghai, China
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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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Walker G, Pevy N, O'Malley R, Mirheidari B, Reuber M, Christensen H, Blackburn DJ. Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls. CLINICAL LINGUISTICS & PHONETICS 2023:1-22. [PMID: 37722818 DOI: 10.1080/02699206.2023.2254458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/28/2023] [Indexed: 09/20/2023]
Abstract
Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.
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Affiliation(s)
- Gareth Walker
- School of English, University of Sheffield, Sheffield, UK
| | - Nathan Pevy
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Ronan O'Malley
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Bahman Mirheidari
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Markus Reuber
- Academic Neurology Unit, Royal Hallamshire Hospital, University of Sheffield, Sheffield, UK
| | - Heidi Christensen
- Department of Computer Science, University of Sheffield, Sheffield, UK
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Gosztolya G, Svindt V, Bona J, Hoffmann I. Extracting Phonetic Posterior-Based Features for Detecting Multiple Sclerosis From Speech. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3234-3244. [PMID: 37549073 DOI: 10.1109/tnsre.2023.3300532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC). We exploit the fact that MS might cause a motor speech disorder similar to dysarthria, which, with our hypothesis, might affect the phonetic posterior estimates supplied by a Deep Neural Network acoustic model. From our experimental results, the proposed posterior posteriorgram-based feature extraction approach is useful for detecting MS: depending on the actual speech task, we obtained Equal Error Rate values as low as 13.3%, and AUC scores up to 0.891, indicating a competitive and more consistent classification performance compared to both the x-vector and the openSMILE 'ComParE functionals' attributes. Besides this discrimination performance, the interpretable nature of the phonetic posterior features might also make our method suitable for automatic MS screening or monitoring the progression of the disease. Furthermore, by examining which specific phonetic groups are the most useful for this feature extraction process, the potential utility of the proposed phonetic features could also be utilized in the speech therapy of MS patients.
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Oh C, Morris R, Wang X, Raskin MS. Analysis of emotional prosody as a tool for differential diagnosis of cognitive impairments: a pilot research. Front Psychol 2023; 14:1129406. [PMID: 37425151 PMCID: PMC10327638 DOI: 10.3389/fpsyg.2023.1129406] [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: 12/21/2022] [Accepted: 05/26/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction This pilot research was designed to investigate if prosodic features from running spontaneous speech could differentiate dementia of the Alzheimer's type (DAT), vascular dementia (VaD), mild cognitive impairment (MCI), and healthy cognition. The study included acoustic measurements of prosodic features (Study 1) and listeners' perception of emotional prosody differences (Study 2). Methods For Study 1, prerecorded speech samples describing the Cookie Theft picture from 10 individuals with DAT, 5 with VaD, 9 with MCI, and 10 neurologically healthy controls (NHC) were obtained from the DementiaBank. The descriptive narratives by each participant were separated into utterances. These utterances were measured on 22 acoustic features via the Praat software and analyzed statistically using the principal component analysis (PCA), regression, and Mahalanobis distance measures. Results The analyses on acoustic data revealed a set of five factors and four salient features (i.e., pitch, amplitude, rate, and syllable) that discriminate the four groups. For Study 2, a group of 28 listeners served as judges of emotions expressed by the speakers. After a set of training and practice sessions, they were instructed to indicate the emotions they heard. Regression measures were used to analyze the perceptual data. The perceptual data indicated that the factor underlying pitch measures had the greatest strength for the listeners to separate the groups. Discussion The present pilot work showed that using acoustic measures of prosodic features may be a functional method for differentiating among DAT, VaD, MCI, and NHC. Future studies with data collected under a controlled environment using better stimuli are warranted.
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Affiliation(s)
- Chorong Oh
- School of Rehabilitation and Communication Sciences, Ohio University, Athens, OH, United States
| | - Richard Morris
- School of Communication Science and Disorders, Florida State University, Tallahassee, FL, United States
| | - Xianhui Wang
- School of Medicine, University of California Irvine, Irvine, CA, United States
| | - Morgan S. Raskin
- School of Communication Science and Disorders, Florida State University, Tallahassee, FL, United States
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Giorelli M. Current and future perspectives of an early diagnosis of cognitive impairment. Front Neurol 2023; 14:1171681. [PMID: 37090988 PMCID: PMC10113481 DOI: 10.3389/fneur.2023.1171681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 03/15/2023] [Indexed: 04/08/2023] Open
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Javeed A, Dallora AL, Berglund JS, Ali A, Ali L, Anderberg P. Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. J Med Syst 2023; 47:17. [PMID: 36720727 PMCID: PMC9889464 DOI: 10.1007/s10916-023-01906-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/03/2023] [Indexed: 02/02/2023]
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Tomtebodavagen, Stockholm, 17165, Solna, Sweden
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
| | - Johan Sanmartin Berglund
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden.
| | - Arif Ali
- Department of Computer Science, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Liaqata Ali
- Department of Electrical Engineering, University of Science and Technology Bannu, Township, Bannu, 28100, Khyber-Pakhtunkhwa, Pakistan
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141, Blekinge, Sweden
- School of Health Sciences, University of Skovde, Högskolevägen 1, Skövde, SE-541 28, Skövde, Sweden
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Pigliautile M, Colombo M, Pizzuti T, Procopio N, Stillo M, Curia R, Mecocci P. DMapp: a developing promising approach to monitor symptoms progression and stimulate memory in Italian people with cognitive impairments. Aging Clin Exp Res 2022; 34:2721-2731. [PMID: 36036304 DOI: 10.1007/s40520-022-02219-4] [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: 05/02/2022] [Accepted: 07/31/2022] [Indexed: 01/04/2023]
Abstract
BACKGROUND Based on recent researches on the use of natural language processing techniques for very early detection of cognitive decline and the benefits of cognitive stimulation for people with cognitive impairments, the Dementia Monitoring application (DMapp) is developed inside the Memento project. AIMS The aims of this work are: (1) to present DMapp; (2) to report the results of two preliminary studies on DMapp; (3) to describe the clinical and experimental potentiality of DMapp. METHODS Italian people with the diagnosis of mild cognitive impairment due to Alzheimer's disease or dementia due to Alzheimer's Disease with a Mini-Mental-State-Examination between 24 and 28 (inclusive) were involved in the DMapp development prototype during the Lab Trial (4 subjects) and Filed Trial (5 subjects) of the Memento project. Qualitative and quantitative data were collected to evaluate participants' opinions, the DMapp ability to perform the automatic analysis of the speech and participants' visible emotional state effective. Ad hoc interviews, the Observed Emotion Rating Scale and performance metrics to solve different tasks were used. The relation between cognitive measures (global cognitive measures) and linguistic indexes values was considered using Euclidean distances between the participants. RESULTS Linguistic indexes were calculated and seemed to classify the participants' performance as expected from cognitive measures. The DMapp was appreciated by people with cognitive impairment. Positive emotions were present. CONCLUSION DMapp seems an interesting approach to monitor dementia symptoms progression and stimulate memory. Possible developments and open questions are discussed.
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Affiliation(s)
- Martina Pigliautile
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy.
| | - Matteo Colombo
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy
| | | | | | - Maria Stillo
- Innovation Lab, Integris S.P.A, Rende and Pisa, Italy
| | - Rosario Curia
- Innovation Lab, Integris S.P.A, Rende and Pisa, Italy
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Piazzale Gambuli, 1, 06132, Perugia, Italy.,Division of Clinical Geriatrics NVS Department Karolinska Institutet, Stockholm, Sweden
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Lin YC, Yan HT, Lin CH, Chang HH. Predicting frailty in older adults using vocal biomarkers: a cross-sectional study. BMC Geriatr 2022; 22:549. [PMID: 35778699 PMCID: PMC9248103 DOI: 10.1186/s12877-022-03237-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 06/17/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Frailty is a common issue in the aging population. Given that frailty syndrome is little discussed in the literature on the aging voice, the current study aims to examine the relationship between frailty and vocal biomarkers in older people. METHODS Participants aged ≥ 60 years visiting geriatric outpatient clinics were recruited. They underwent frailty assessment (Cardiovascular Health Study [CHS] index; Study of Osteoporotic Fractures [SOF] index; and Fatigue, Resistance, Ambulation, Illness, and Loss of weight [FRAIL] index) and were asked to pronounce a sustained vowel /a/ for approximately 1 s. Four voice parameters were assessed: average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4). RESULTS Among 277 older adults, increased A1 was associated with a lower likelihood of frailty as defined by SOF (odds ratio [OR] 0.84, 95% confidence interval [CI] 0.74-0.96). Participants with larger A2 values were more likely to be frail, as defined by FRAIL and CHS (FRAIL: OR 1.41, 95% CI 1.12-1.79; CHS: OR 1.38, 95% CI 1.10-1.75). Sex differences were observed across the three frailty indices. In male participants, an increase in A3 by 10 points increased the odds of frailty by almost 7% (SOF: OR 1.07, 95% CI 1.02-1.12), 6% (FRAIL: OR 1.06, 95% CI 1.02-1.11), or 6% (CHS: OR 1.06, 95% CI 1.01-1.11). In female participants, an increase in A4 by 0.1 conferred a significant 2.8-fold (SOF: OR 2.81, 95% CI 1.71-4.62), 2.3-fold (FRAIL: OR 2.31, 95% CI 1.45-3.68), or 2.8-fold (CHS: OR 2.82, 95% CI 1.76-4.51, CHS) increased odds of frailty. CONCLUSIONS Vocal biomarkers, especially spectral-domain voice parameters, might have potential for estimating frailty, as a non-invasive, instantaneous, objective, and cost-effective estimation tool, and demonstrating sex differences for individualised treatment of frailty.
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Affiliation(s)
- Yu-Chun Lin
- Department of Chinese Medicine, China Medical University Hospital, No. 2, Yude Road, North District, 40447, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, China Medical University, No.91, Hsueh-Shih Road, North District, Taichung, 40402, Taiwan
| | - Huang-Ting Yan
- Institute of Political Science, Academia Sinica, 128 Academia Rd., Sec.2, Nankang, Taipei, 115, Taiwan
| | - Chih-Hsueh Lin
- School of Medicine, College of Medicine, China Medical University, No.91, Hsueh-Shih Road, North District, Taichung, 40402, Taiwan
- Department of Family Medicine, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 40447, Taiwan
| | - Hen-Hong Chang
- Department of Chinese Medicine, China Medical University Hospital, No. 2, Yude Road, North District, 40447, Taichung, Taiwan.
- Graduate Institute of Integrated Medicine, China Medical University, No.91, Hsueh-Shih Road, North District, Taichung, 40402, Taiwan.
- Chinese Medicine Research Centre, China Medical University, No.91, Hsueh-Shih RoadNorth District, Taichung, 40402, Taiwan.
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Liu N, Yuan Z, Tang Q. Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network. Front Public Health 2022; 9:835960. [PMID: 35310782 PMCID: PMC8927695 DOI: 10.3389/fpubh.2021.835960] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/28/2021] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Ning Liu
- School of Public Health, Hangzhou Normal University, Hangzhou, China
- Department of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, China
- Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University, Quanzhou, China
| | - Zhenming Yuan
- School of Public Health, Hangzhou Normal University, Hangzhou, China
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China
- *Correspondence: Zhenming Yuan
| | - Qingfeng Tang
- School of Computer and Information, Anqing Normal University, Anqing, China
- Qingfeng Tang
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11
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12
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Martinc M, Haider F, Pollak S, Luz S. Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech. Front Aging Neurosci 2021; 13:642647. [PMID: 34194313 PMCID: PMC8236853 DOI: 10.3389/fnagi.2021.642647] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/11/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Advances in machine learning (ML) technology have opened new avenues for detection and monitoring of cognitive decline. In this study, a multimodal approach to Alzheimer's dementia detection based on the patient's spontaneous speech is presented. This approach was tested on a standard, publicly available Alzheimer's speech dataset for comparability. The data comprise voice samples from 156 participants (1:1 ratio of Alzheimer's to control), matched by age and gender. Materials and Methods: A recently developed Active Data Representation (ADR) technique for voice processing was employed as a framework for fusion of acoustic and textual features at sentence and word level. Temporal aspects of textual features were investigated in conjunction with acoustic features in order to shed light on the temporal interplay between paralinguistic (acoustic) and linguistic (textual) aspects of Alzheimer's speech. Combinations between several configurations of ADR features and more traditional bag-of-n-grams approaches were used in an ensemble of classifiers built and evaluated on a standardised dataset containing recorded speech of scene descriptions and textual transcripts. Results: Employing only semantic bag-of-n-grams features, an accuracy of 89.58% was achieved in distinguishing between Alzheimer's patients and healthy controls. Adding temporal and structural information by combining bag-of-n-grams features with ADR audio/textual features, the accuracy could be improved to 91.67% on the test set. An accuracy of 93.75% was achieved through late fusion of the three best feature configurations, which corresponds to a 4.7% improvement over the best result reported in the literature for this dataset. Conclusion: The proposed combination of ADR audio and textual features is capable of successfully modelling temporal aspects of the data. The machine learning approach toward dementia detection achieves best performance when ADR features are combined with strong semantic bag-of-n-grams features. This combination leads to state-of-the-art performance on the AD classification task.
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Affiliation(s)
- Matej Martinc
- Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Fasih Haider
- Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, United Kingdom
| | - Senja Pollak
- Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Saturnino Luz
- Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, United Kingdom
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Martínez-Nicolás I, Llorente TE, Martínez-Sánchez F, Meilán JJG. Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article. Front Psychol 2021; 12:620251. [PMID: 33833713 PMCID: PMC8021952 DOI: 10.3389/fpsyg.2021.620251] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/15/2021] [Indexed: 11/25/2022] Open
Abstract
Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy. Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists. Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment. Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.
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Affiliation(s)
- Israel Martínez-Nicolás
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | - Thide E Llorente
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | | | - Juan José G Meilán
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
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Automated profiling of spontaneous speech in primary progressive aphasia and behavioral-variant frontotemporal dementia: An approach based on usage-frequency. Cortex 2020; 133:103-119. [DOI: 10.1016/j.cortex.2020.08.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 03/08/2020] [Accepted: 08/27/2020] [Indexed: 11/21/2022]
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15
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Petti U, Baker S, Korhonen A. A systematic literature review of automatic Alzheimer's disease detection from speech and language. J Am Med Inform Assoc 2020; 27:1784-1797. [PMID: 32929494 PMCID: PMC7671617 DOI: 10.1093/jamia/ocaa174] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/14/2020] [Accepted: 07/14/2020] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. MATERIALS AND METHODS We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? RESULTS AND DISCUSSION We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. CONCLUSION The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
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Affiliation(s)
- Ulla Petti
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Simon Baker
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Anna Korhonen
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
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16
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Momeni M, Rahmani M. Speech signal analysis of alzheimer's diseases in farsi using auditory model system. Cogn Neurodyn 2020; 15:453-461. [PMID: 34040671 DOI: 10.1007/s11571-020-09644-z] [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: 12/11/2019] [Revised: 09/21/2020] [Accepted: 10/06/2020] [Indexed: 11/25/2022] Open
Abstract
In recent years, extensive studies have been conducted on the diagnosis of Alzheimer's disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer's and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer's only using the few extracted features of the speech signal.
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Affiliation(s)
- Maryam Momeni
- Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran
| | - Mahdiyeh Rahmani
- Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran
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17
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Sidorova J, Carbonell P, Čukić M. Blood Glucose Estimation From Voice: First Review of Successes and Challenges. J Voice 2020; 36:737.e1-737.e10. [PMID: 33041176 DOI: 10.1016/j.jvoice.2020.08.034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 11/25/2022]
Abstract
The possibility to estimate glucose value from voice would make a breakthrough in diabetes treatment: namely, remove the delay in the nonintrusive instantaneous blood glucose estimation, relieve medical budgets and significantly improve wellbeing of diabetics. In this review, different approaches have been described and systematized, in order to provide an objective snapshot of the state of the art. Since nonintrusive glucose estimation is notoriously difficult, we included a Transparence and Reproducibility Score aimed at revealing the biases in the primary research articles. The review is completed with the discussion on future research pathways.
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Affiliation(s)
- Julia Sidorova
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clinic, Barcelona, Spain..
| | - Pablo Carbonell
- Instituto de Automatica e Informatica Industrial, Universidad Politecnica de Valencia, Valencia, Spain
| | - Milena Čukić
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, Madrid, Spain
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18
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Sidorova J, Anisimova M. Impact of Diabetes Mellitus on Voice : A Methodological Commentary. J Voice 2020; 36:294.e1-294.e12. [PMID: 32739034 DOI: 10.1016/j.jvoice.2020.05.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/14/2020] [Accepted: 05/26/2020] [Indexed: 11/18/2022]
Affiliation(s)
- Julia Sidorova
- Blekinge Institute of Technology, Vallhallavagän 1, Karlskrona, 37141, Sweden.
| | - Maria Anisimova
- Zurich University of Applied Sciences, Technikumstrasse, 9, 8400, Winterthur
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19
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Belmonte-Fernández Ó, Caballer-Miedes A, Chinellato E, Montoliu R, Sansano-Sansano E, García-Vidal R. Anomaly Detection in Activities of Daily Living with Linear Drift. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09740-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Lopez-de-Ipina K, Barroso N, Calvo PM, Hernandez C, Ezeiza A, Susperregi U, Fernández E. Multilingual audio information management system based on semantic knowledge in complex environments. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04618-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractThis paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.
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21
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Voleti R, Liss JM, Berisha V. A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2020; 14:282-298. [PMID: 33907590 PMCID: PMC8074691 DOI: 10.1109/jstsp.2019.2952087] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.
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Affiliation(s)
- Rohit Voleti
- School of Electrical, Computer, & Energy Engineering, Arizona State University, Tempe, AZ, 85281 USA
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22
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Khan S, Barve KH, Kumar MS. Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer's Disease. Curr Neuropharmacol 2020; 18:1106-1125. [PMID: 32484110 PMCID: PMC7709159 DOI: 10.2174/1570159x18666200528142429] [Citation(s) in RCA: 246] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/06/2020] [Accepted: 05/25/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The only conclusive way to diagnose Alzheimer's is to carry out brain autopsy of the patient's brain tissue and ascertain whether the subject had Alzheimer's or any other form of dementia. However, due to the non-feasibility of such methods, to diagnose and conclude the conditions, medical practitioners use tests that examine a patient's mental ability. OBJECTIVE Accurate diagnosis at an early stage is the need of the hour for initiation of therapy. The cause for most Alzheimer's cases still remains unknown except where genetic distinctions have been observed. Thus, a standard drug regimen ensues in every Alzheimer's patient, irrespective of the cause, which may not always be beneficial in halting or reversing the disease progression. To provide a better life to such patients by suppressing existing symptoms, early diagnosis, curative therapy, site-specific delivery of drugs, and application of hyphenated methods like artificial intelligence need to be brought into the main field of Alzheimer's therapeutics. METHODS In this review, we have compiled existing hypotheses to explain the cause of the disease, and highlighted gene therapy, immunotherapy, peptidomimetics, metal chelators, probiotics and quantum dots as advancements in the existing strategies to manage Alzheimer's. CONCLUSION Biomarkers, brain-imaging, and theranostics, along with artificial intelligence, are understood to be the future of the management of Alzheimer's.
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Affiliation(s)
- Sahil Khan
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
| | - Kalyani H. Barve
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
| | - Maushmi S. Kumar
- SVKM’S NMIMS, Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, V.L. Mehta Road, Vile Parle West, Mumbai-400056, India
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23
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de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2020; 78:1547-1574. [PMID: 33185605 PMCID: PMC7836050 DOI: 10.3233/jad-200888] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
BACKGROUND Language is a valuable source of clinical information in Alzheimer's disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. OBJECTIVE Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer's disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. METHODS Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. RESULTS From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). CONCLUSION Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.
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Affiliation(s)
| | - Craig W. Ritchie
- Centre for Clinical Brain Sciences, The University of Edinburgh, Scotland, UK
| | - Saturnino Luz
- Usher Institute, Edinburgh Medical School, The University of Edinburgh, Scotland, UK
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24
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Characterization of Parkinson’s disease dysarthria in terms of speech articulation kinematics. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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25
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Al-Hameed S, Benaissa M, Christensen H, Mirheidari B, Blackburn D, Reuber M. A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints. PLoS One 2019; 14:e0217388. [PMID: 31125389 PMCID: PMC6534304 DOI: 10.1371/journal.pone.0217388] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 05/11/2019] [Indexed: 11/18/2022] Open
Abstract
Neurodegenerative diseases causing dementia are known to affect a person's speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients' verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehension. By capturing neurodegeneration-associated characteristics in a person's voice, the incorporation of novel methods based on the automatic analysis of speech signals may give us more information about a person's ability to interact which could contribute to the diagnostic process. In this proof-of-principle study, we demonstrate that purely acoustic features, extracted from recordings of patients' answers to a neurologist's questions in a specialist memory clinic can support the initial distinction between patients presenting with cognitive concerns attributable to progressive neurodegenerative disorders (ND) or Functional Memory Disorder (FMD, i.e., subjective memory concerns unassociated with objective cognitive deficits or a risk of progression). The study involved 15 FMD and 15 ND patients where a total of 51 acoustic features were extracted from the recordings. Feature selection was used to identify the most discriminating features which were then used to train five different machine learning classifiers to differentiate between the FMD/ND classes, achieving a mean classification accuracy of 96.2%. The discriminative power of purely acoustic approaches could be integrated into diagnostic pathways for patients presenting with memory concerns and are computationally less demanding than methods focusing on linguistic elements of speech and language that require automatic speech recognition and understanding.
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Affiliation(s)
- Sabah Al-Hameed
- Dept of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Mohammed Benaissa
- Dept of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom
| | - Heidi Christensen
- Dept of Computer Science, University of Sheffield, Sheffield, United Kingdom
- Centre for Assistive Technology and Connected Healthcare, University of Sheffield, Sheffield, United Kingdom
| | - Bahman Mirheidari
- Dept of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Daniel Blackburn
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, United Kingdom
| | - Markus Reuber
- Academic Neurology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, United Kingdom
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26
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Impedovo D, Pirlo G, Vessio G, Angelillo MT. A Handwriting-Based Protocol for Assessing Neurodegenerative Dementia. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09642-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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27
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de la Fuente Garcia S, Ritchie CW, Luz S. Protocol for a conversation-based analysis study: PREVENT-ED investigates dialogue features that may help predict dementia onset in later life. BMJ Open 2019; 9:e026254. [PMID: 30918035 PMCID: PMC6475209 DOI: 10.1136/bmjopen-2018-026254] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 01/10/2019] [Accepted: 02/22/2019] [Indexed: 01/18/2023] Open
Abstract
INTRODUCTION Decreasing the incidence of Alzheimer's disease (AD) is a global public health priority. Early detection of AD is an important requisite for the implementation of prevention strategies towards this goal. While it is plausible that patients at the early stages of AD may exhibit subtle behavioural signs of neurodegeneration, neuropsychological testing seems unable to detect these signs in preclinical AD. Recent studies indicate that spontaneous speech data, which can be collected frequently and naturally, provide good predictors for AD detection in cohorts with a clinical diagnosis. The potential of models based on such data for detecting preclinical AD remains unknown. METHODS AND ANALYSIS The PREVENT-Elicitation of Dialogues (PREVENT-ED) study builds on the PREVENT Dementia project to investigate whether early behavioural signs of AD may be detected through dialogue interaction. Participants recruited through PREVENT, aged 40-59 at baseline, will be included in this study. We will use speech processing and machine learning methods to assess how well speech and visuospatial markers agree with neuropsychological, biomarker, clinical, lifestyle and genetic data from the PREVENT cohort. ETHICS AND DISSEMINATION There are no expected risks or burdens to participants. The procedures are not invasive and do not raise significant ethical issues. We only approach healthy consenting adults and all participants will be informed that this is an exploratory study and therefore has no diagnostic aim. Confidentiality aspects such as data encryption and storage comply with the General Data Protection Regulation and with the requirements from sponsoring bodies and ethical committees. This study has been granted ethical approval by the London-Surrey Research Ethics Committee (REC reference No: 18/LO/0860), and by Caldicott and Information Governance (reference No: CRD18048). PREVENT-ED results will be published in peer-reviewed journals.
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Affiliation(s)
- Sofia de la Fuente Garcia
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, Department of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
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28
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Gómez-Vilda P, Gómez-Rodellar A, Vicente JMF, Mekyska J, Palacios-Alonso D, Rodellar-Biarge V, Álvarez-Marquina A, Eliasova I, Kostalova M, Rektorova I. Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants. Int J Neural Syst 2019; 29:1850039. [DOI: 10.1142/s0129065718500399] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson’s Disease.
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Affiliation(s)
- Pedro Gómez-Vilda
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Andrés Gómez-Rodellar
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - José M. Ferrández Vicente
- Universidad Politécnica de Cartagena, Campus Universitario Muralla del Mar, Pza. Hospital 1, 30202 Cartagena, Spain
| | - Jiri Mekyska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Daniel Palacios-Alonso
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
- Escuela Técnica Superior de Ingeniería Informática - Universidad Rey Juan Carlos, Campus de Móstoles, Tulipán s/n, 28933 Móstoles, Madrid, Spain
| | - Victoria Rodellar-Biarge
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Agustín Álvarez-Marquina
- Neuromorphic Speech Processing Lab, Center for Biomedical Technology, Universidad Politécnica de, Madrid Campus de Montegancedo, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Ilona Eliasova
- First Department of Neurology, Faculty of Medicine and St. Anne’s University Hospital, Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
| | - Milena Kostalova
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
- Department of Neurology, Faculty Hospital and Masaryk University, Jihlavska 20, 63900 Brno, Czech Republic
| | - Irena Rektorova
- First Department of Neurology, Faculty of Medicine and St. Anne’s University Hospital, Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic
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29
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Ryan P, Luz S, Albert P, Vogel C, Normand C, Elwyn G. Using artificial intelligence to assess clinicians' communication skills. BMJ 2019; 364:l161. [PMID: 30659013 DOI: 10.1136/bmj.l161] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Padhraig Ryan
- Centre of Health Policy and Management School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Pierre Albert
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Carl Vogel
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Charles Normand
- Centre of Health Policy and Management School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Glyn Elwyn
- Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire, USA
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30
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Mirzaei S, El Yacoubi M, Garcia-Salicetti S, Boudy J, Kahindo C, Cristancho-Lacroix V, Kerhervé H, Rigaud AS. Two-Stage Feature Selection of Voice Parameters for Early Alzheimer's Disease Prediction. Ing Rech Biomed 2018. [DOI: 10.1016/j.irbm.2018.10.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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31
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Mekyska J, Galaz Z, Kiska T, Zvoncak V, Mucha J, Smekal Z, Eliasova I, Kostalova M, Mrackova M, Fiedorova D, Faundez-Zanuy M, Solé-Casals J, Gomez-Vilda P, Rektorova I. Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson's Disease. Cognit Comput 2018; 10:1006-1018. [PMID: 30595758 PMCID: PMC6294819 DOI: 10.1007/s12559-018-9575-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 06/13/2018] [Indexed: 12/27/2022]
Abstract
Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson's disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG-Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG-Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG-Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.
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Affiliation(s)
- Jiri Mekyska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Zoltan Galaz
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Tomas Kiska
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Vojtech Zvoncak
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Jan Mucha
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Zdenek Smekal
- Department of Telecommunications, Brno University of Technology, Technicka 10, 61600 Brno, Czech Republic
| | - Ilona Eliasova
- First Department of Neurology, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Milena Kostalova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
- Department of Neurology, Faculty Hospital and Masaryk University, Jihlavska 20, 63900 Brno, Czech Republic
| | - Martina Mrackova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Dagmar Fiedorova
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
| | - Marcos Faundez-Zanuy
- Escola Superior Politecnica, Tecnocampus, Avda. Ernest Lluch 32, 08302 Mataro, Barcelona Spain
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic – Central University of Catalonia, Perot Rocaguinarda 17, 08500 Vic, Catalonia Spain
| | - Pedro Gomez-Vilda
- Neuromorphic Processing Laboratory (NeuVox Lab), Center for Biomedical Technology, Universidad Politécnica de Madrid Campus de Montegancedo, s/n, 28223, Pozuelo de Alarcón, Madrid Spain
| | - Irena Rektorova
- First Department of Neurology, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Kamenice 5, 62500 Brno, Czech Republic
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Mood Impact on Automaticity of Performance: Handwriting as Exemplar. Cognit Comput 2018. [DOI: 10.1007/s12559-017-9540-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Gómez P, Londral ARM, Gómez A, Palacios D, Rodellar V. Monitoring ALS from speech articulation kinematics. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3538-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Badarna M, Shimshoni I, Luria G, Rosenblum S. The Importance of Pen Motion Pattern Groups for Semi-Automatic Classification of Handwriting into Mental Workload Classes. Cognit Comput 2017. [DOI: 10.1007/s12559-017-9520-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Alonso JB, Cabrera J, Travieso CM, López-de-Ipiña K, Sánchez-Medina A. Continuous tracking of the emotion temperature. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.06.093] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Alonso-Martinez C, Faundez-Zanuy M, Mekyska J. A Comparative Study of In-Air Trajectories at Short and Long Distances in Online Handwriting. Cognit Comput 2017; 9:712-720. [PMID: 30100928 PMCID: PMC6061233 DOI: 10.1007/s12559-017-9501-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 07/18/2017] [Indexed: 12/05/2022]
Abstract
Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to differentiate movements at two different heights (a) short distance: height lower or equal to 1 cm above a surface of digitizer, the digitizer provides x and y coordinates; (b) long distance: height exceeding 1 cm, the only information available is a time stamp that indicates the time that a specific stroke has spent at long distance. Although short distance has been used in several papers, long distances have been ignored and will be investigated in this paper. In this paper, we will analyze a large set of databases (BIOSECUR-ID, EMOTHAW, PaHaW, OXYGEN-THERAPY, and SALT), which contain a total amount of 663 users and 17,951 files. We have specifically studied (a) the percentage of time spent on-surface, in-air at short distance, and in-air at long distance for different user profiles (pathological and healthy users) and different tasks; (b) the potential use of these signals to improve classification rates. Our experimental results reveal that long distance movements represent a very small portion of the total execution time (0.5% in the case of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the largest database). In addition, significant differences have been found in the comparison of pathological versus control group for letter “l” in PaHaW database (p = 0.0157) and crossed pentagons in SALT database (p = 0.0122).
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Affiliation(s)
| | - Marcos Faundez-Zanuy
- 1ESUP Tecnocampus (Pompeu Fabra University), Av. Ernest Lluch 32, 08302 Mataró, Spain
| | - Jiri Mekyska
- 2Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 10, 616 00 Brno, Czech Republic
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C.K. Y, Hariharan M, Ngadiran R, Adom A, Yaacob S, Polat K. Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.03.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wang J, Ye K, Cao J, Wang T, Xue A, Cheng Y, Yin C. DOA Estimation of Excavation Devices with ELM and MUSIC-Based Hybrid Algorithm. Cognit Comput 2017. [DOI: 10.1007/s12559-017-9475-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Vancea M, Solé-Casals J. Population Aging in the European Information Societies: Towards a Comprehensive Research Agenda in eHealth Innovations for Elderly. Aging Dis 2015; 7:526-39. [PMID: 27493837 DOI: 10.14336/ad.2015.1214] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Accepted: 12/14/2015] [Indexed: 11/01/2022] Open
Abstract
Population ageing is one of the major social and economic challenges of our contemporary societies. With the advent of the information society, new research and technological developments have been promoted in the field of assistive technologies and information and communication technologies of benefit to elderly people. This article examines the potentialities of new informatics developments in generating solutions to better address elderly people's daily-life, especially those with chronic illness and/or low autonomy. The authours attempt to propose a research agenda, by exposing various strengts and weaknesses of eHealth innovations for elderly, mainly grounded in secondary sources analysis.
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Affiliation(s)
- Mihaela Vancea
- 1Núcleo de Investigación Científica y Desarrollo Tecnológico en Ciencias Sociales y las Humanidades Universidad de la Frontera, Chile
| | - Jordi Solé-Casals
- 2Data and Signal Processing Research Group, U Sciences Tech, University of Vic - Central University of Catalonia, Spain
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Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimers Dement 2014; 11:561-78. [PMID: 25443858 DOI: 10.1016/j.jalz.2014.06.004] [Citation(s) in RCA: 157] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 04/21/2014] [Accepted: 06/16/2014] [Indexed: 02/06/2023]
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