Gender classification of subjects from cerebral blood flow changes using Deep Learning

Hiroyasu, Tomoyuki; Hanawa, Kenya; Yamamoto, Utako

doi:10.1109/cidm.2014.7008672

Cited by 11 publications

(13 citation statements)

References 2 publications

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“…The extracted local features are mixed and integrated into a global feature in the fully connected output layer. The SDA and DNN, which have been used in a previous study [5], do not extract local features because they employ a fully connected architecture.…”

Section: Methodsmentioning

confidence: 99%

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Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Hiwa

Hanawa

Tamura

et al. 2016

Computational Intelligence and Neuroscience

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Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

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Section: Methodsmentioning

confidence: 99%

“…Previously, the authors have proposed a gender classification method for fNIRS time series data using deep learning [5], a type of machine learning. In the proposed method, a stacked denoising autoencoder (SDA) [6] and a deep neural network (DNN) are used and trained to classify the gender of a subject from given fNIRS data.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Hiwa

Hanawa

Tamura

et al. 2016

Computational Intelligence and Neuroscience

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“…Most DL applications to functional optical brain imaging have focused on classification tasks based on fNIRS. Hiroyasu et al [256] reported a DNN to perform gender classification on subjects performing a numerical memory task while subjecting to a white‐noise sound environment to elicit gender‐based differences in cortical activations. Using time series data of oxygenated hemoglobin of the inferior frontal gyrus on the left side of the head captured by 4 fNIRS channels, they reported a 81% accuracy in gender classification.…”

Section: Applications In Biomedical Opticsmentioning

confidence: 99%

Deep Learning in Biomedical Optics

et al. 2021

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This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC

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“…The most widely used classification methods in this domain are Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Hidden Markov Models (HMM), and Artificial Neural Networks (ANN). Hiroyasu et al [9] proposed a gender classification method using the human brain's blood flow change data that are measured by fNIRS. Firstly, numerical memory task was applied to 22 subjects during the measurements.…”

Section: Related Workmentioning

confidence: 99%

“…Hiroyasu et al [9] proposed a gender classification method using the human brain's blood flow change data that are measured by fNIRS. Firstly, numerical memory task was applied to 22 subjects during the measurements.…”

Section: Related Workmentioning

confidence: 99%

Person ıdentıfıcatıon usıng functıonal near- ınfrared spectroscopy sıgnals usıng a fully connected deep neural network

KELES¹

2017

Communications Faculty of Science University of Ankara

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Key word and phrases: functional near-infrared spectroscopy (fNIRS), person identification, PCA, SVD, fully connected deep neural network, random forest. Abstract. In this study, we investigate the suitability of functional near-infrared spectroscopy signals (fNIRS) for person identification using data visualization and machine learning algorithms. We first applied two linear dimension reduction algorithms: Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) in order to reduce the dimensionality of the fNIRS data. We then inspected the clustering of samples in a 2d space using a nonlinear projection algorithm. We observed with the SVD projection that the data integrity associated with each person is high in the reduced space. In the light of these observations, we implemented a random forest algorithm as a baseline model and a fully connected deep neural network (FCDNN) as the primary model to identify person from their brain signals. We obtained %85.16 accuracy with our FCDNN model using SVD reduction. Our results are in parallel with the neuroscience researches, which state that brain signals of each person are unique and can be used to identify a person.

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Gender classification of subjects from cerebral blood flow changes using Deep Learning

Cited by 11 publications

References 2 publications

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Deep Learning in Biomedical Optics

Person ıdentıfıcatıon usıng functıonal near- ınfrared spectroscopy sıgnals usıng a fully connected deep neural network

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