A Smartphone Application for Automated Decision Support in Cognitive Task Based Evaluation of Central Nervous System Motor Disorders

Lauraitis, Andrius; Maskeliūnas, Rytis; Damaševičius, Robertas; Połap, Dawid; Woźniak, Marcin

doi:10.1109/jbhi.2019.2891729

Cited by 44 publications

(26 citation statements)

References 43 publications

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“…SpectralSlope evaluates the spectral shape slope by using a linear approximation of the magnitude spectrum. A linear function is modelled from the magnitude spectrum as defined in (10)…”

Section: Audio Signal Feature Extraction Methodsmentioning

confidence: 99%

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

et al. 2020

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We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early stage CNSD (3 Huntington, 1 Parkinson, 1 cerebral palsy, 1 post stroke, 1 early dementia), other 8 subjects were healthy. Speech data is collected using voice recorder from Neural Impairment Test Suite (NITS) mobile app. Features are extracted from pitch contours, Mel-frequency cepstral coefficients (MFCC), Gammatone cepstral coefficients (GTCC), Gabor (analytic Morlet) wavelet and auditory spectrograms. 94.50% (BiLSTM) and 96.3% (WST-SVM) accuracy is achieved for solving healthy vs. impaired classification problem. The developed method can be applied for automated CNSD patient health state monitoring and clinical decision support systems as well as a part of Internet of Medical Things (IoMT). INDEX TERMS Neural impairment, mobile app, deep learning, wavelet scattering, decision support, speech processing, digital health, Internet of Medical Things.

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“…SpectralSlope evaluates the spectral shape slope by using a linear approximation of the magnitude spectrum. A linear function is modelled from the magnitude spectrum as defined in (10)…”

Section: Audio Signal Feature Extraction Methodsmentioning

confidence: 99%

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

et al. 2020

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“…Mobile applications have emerged as the core components that have led to these tremendous changes, and various health-related fields are rapidly embracing the use of these technologies in their research investigations. Several investigators in these fields have developed new intervention strategies that use mobile phone applications and have found them to be effective [2][3][4][5][6] Cognitive control is the most fundamental psychological function that underlies the execution of many other psychological functions. Gratton, Cooper, Fabiani, Carter, and Karayanidis [7] described that cognitive control is a core concept in modern cognitive neuroscience.…”

Section: Introductionmentioning

confidence: 99%

Validation of a mobile game-based assessment of cognitive control among children and adolescents

Song

2020

PLoS ONE

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Cognitive control is the most fundamental psychological function that underlies the execution of many other psychological functions. A mobile game application could be a useful strategy to evaluate cognitive control in the groups of children and adolescents. Although a serious game that is based on gamification would be an optimal platform for the administration of behavioral and clinical assessments of children and adolescents, most studies on gamification have been conducted among adults and older adults than among children and adolescents. This study aimed to assess cognitive control using a mobile game that used gamification and compared the results to those from traditional neuropsychological tests for children and adolescents. In order to address this objective, this study used a serious game, namely, "CoCon," which was developed to assess cognitive control in children and adolescents. This study included 100 participants from a community sample (mean age = 11.75 years, ranged from 9 to 16 years, SD = 1.40 years; Male = 59(59%), Female = 41(41%)). The analyses interrogated the relationships among various game behaviors scores of CoCon, the standardized neuropsychological tests (K-WISC-IV, CTT, and Stroop), and selfreporting executive function difficulty questionnaire. As results, a mobile game applicationbased assessment proved to be a reliable and valid measure of the cognitive control in children and adolescents. The index scores from the CoCon were significantly related to various cognitive control functions and differentiated between the high and low cognitive control groups. Specifically, even though the participants completed the mobile game 'CoCon' in their natural habitats, the CoCon scores were comparable to the measures from standard neuropsychological tests. In conclusion, the present findings suggest that mobile games that use advanced technology and sophisticated psychological strategies can serve as a new and expanded platform for the administration of psychological assessments.

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“…Artificial neural networks inspired from human biological learning systems are widely used machine learning algorithms. They have shown promising performance in diverse fields such as automatic decision support in medical diagnosis and classification of archaeological artifacts [ 5 , 6 ]. In [ 7 ] the use of temporal features for Tobacco crop estimation and detection using feed forward neural network resulted in the achievement of more than 95% accuracy.…”

Section: Introductionmentioning

confidence: 99%

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network

et al. 2020

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This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.

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A Smartphone Application for Automated Decision Support in Cognitive Task Based Evaluation of Central Nervous System Motor Disorders

Cited by 44 publications

References 43 publications

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

Detection of Speech Impairments Using Cepstrum, Auditory Spectrogram and Wavelet Time Scattering Domain Features

Validation of a mobile game-based assessment of cognitive control among children and adolescents

On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network

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