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05 October 2019, Volume 1 Issue 1
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  • 05 October 2019, Volume 1 Issue 1
    An analysis of one-shot screening methods of ECG with different types
    Sanshiro Ishihara, Katsuhiko Fujiu, Takeshi Imai
    2019, 1(1):  1-9. 
    Abstract ( 201 )  

    Abstract:
    Background: The automatic classification of electrocardiogram (ECG) data using a convolutional neural network (CNN) model has been practiced earlier, but there are only a few studies on a 12-lead ECG dataset with various class labels. A large amount of ECG data is stored in hospital information systems in Japan, and this data can be used for machine learning. However, each sample in the data is mostly recorded for ten seconds and labelled with the corresponding abnormal classes, not for each lead or waveform, but for the entire 12-lead dataset. Therefore, the one-shot screening method using 2-D images of superimposed PQRST waveforms can be a solution in the given condition that all waveforms in a sample within a certain duration must be processed simultaneously.
    Objective: We propose the one-shot screening method with different types of 2-D images of superimposed PQRST waveforms using CNN.
    Methods: CNN and ensemble learning were applied to the ECG dataset, which contains over 9,000 samples with two classes, normal and abnormal, consolidated from 130 abnormal class labels for binary classification. We prepared three types of ECG images that were different in the manner in which they superimposed the PQRST waveforms of a single heartbeat: left-aligned, right-aligned, and centered. We compared the results of the three different images and analyzed false negative patterns to ascertain the characteristics of different types of 2D-CNN.
    Results: The accuracy of all the frameworks were found to be above 0.867. The framework with the centered ECG images achieved the highest accuracy of 0.938 among the three. The listed abnormal classes with a high false negative ratio differed on the basis of the type of model.
    Conclusion: The model with centered images showed the best score with the application of the one-shot method; however, the error analysis demonstrated that the characteristics of these models are varied.

    Mobile Application to Improve Parents’ Knowledge for Maternal and Under-Five Children Health’s in Rwanda
    Eraste Rurangwa, Ryosuke Okuda
    2019, 1(1):  10-16. 
    Abstract ( 73 )  
    Abstract: Background: The automatic classification of electrocardiogram (ECG) data using a convolutional neural network (CNN) model has been practiced earlier, but there are only a few studies on a 12-lead ECG dataset with various class labels. A large amount of ECG data is stored in hospital information systems in Japan, and this data can be used for machine learning. However, each sample in the data is mostly recorded for ten seconds and labelled with the corresponding abnormal classes, not for each lead or waveform, but for the entire 12-lead dataset. Therefore, the one-shot screening method using 2-D images of superimposed PQRST waveforms can be a solution in the given condition that all waveforms in a sample within a certain duration must be processed simultaneously. Objective: We propose the one-shot screening method with different types of 2-D images of superimposed PQRST waveforms using CNN. Methods: CNN and ensemble learning were applied to the ECG dataset, which contains over 9,000 samples with two classes, normal and abnormal, consolidated from 130 abnormal class labels for binary classification. We prepared three types of ECG images that were different in the manner in which they superimposed the PQRST waveforms of a single heartbeat: left-aligned, right-aligned, and centered. We compared the results of the three different images and analyzed false negative patterns to ascertain the characteristics of different types of 2D-CNN. Results: The accuracy of all the frameworks were found to be above 0.867. The framework with the centered ECG images achieved the highest accuracy of 0.938 among the three. The listed abnormal classes with a high false negative ratio differed on the basis of the type of model. Conclusion: The model with centered images showed the best score with the application of the one-shot method; however, the error analysis demonstrated that the characteristics of these models are varied.

    AI technique to detemine the level of cognitive/intelligent level.

    Akinori Abe, Yuki Hayashi and Shusaku Tsumoto
    2019, 1(1):  17-27. 
    Abstract ( 55 )  


    Recently according to the long life of us, it has been pointed out that one of the serious problem is dementia. Accordingly it is necessary to support such person to understand things to spend daily lives with understanding their cognitive level. For that we are planning to develop a cognitive level estimation system. For that we have collected several size of data during experiments of the online shopping game. Where we could collect various information about the participants' behaviour. For instance, how they moved in the supermarket and which things (products) they checked, returned or bought. In addition we could collect their voice data. We have an intention to use those data to determine how human beings think during shopping. In addition, we think we can estimate the intelligent level (cognitive function) from the data. In order to do so, it will be necessary to deal with the collected data correctly. In this paper, we will discuss how to collect data, what data should be collected, and how to store data in the data base. We will also show the feature of collated data and the construction of corpus from the data. The corpus can be transferred to the knowledge base to perform the system for both online shopping and cognitive function/level estimation. Then we will discuss how to estimate the cognitive function. In addition, we will discuss the AI techniques such as abduction to realise the system with cognitive level estimation and dementia person support.


    An Atlas-based Whole-brain Fiber-tracking Method with Automatic Setting of an Optimal Starting Plane in all Parcels
    Shiho Okuhata, Hodaka Miki, Ryusuke Nakai, Tetsuo Kobayashi
    2019, 1(1):  28-36. 
    Abstract ( 59 )  

    Background: The fiber-tracking based on diffusion magnetic resonance imaging technique is one of the methods that indirectly examine the structural information about the cerebral white matter in vivo. Conventionally, fiber-tracking approaches are accompanied with manual settings of the starting regions of fiber tracking, which is usually very time-consuming and unsuitable for large-scale data analysis. 

    Objective: In order to clarify the physical fiber connections associated with disease-specific neural circuits for various neuropsychiatric disorders, we proposed an automated diffusion magnetic resonance imaging based whole-brain fiber-tracking method combined with an atlas separating 54 regions of the white matter. 

    Methods: The propose method automatically set the fiber-tracking starting plane in each parcel and rotated along the running direction of fibers as well so that it enabled swift, objective, and reproducible analysis. The method was verified with real diffusion magnetic resonance imaging data recorded from three healthy volunteers. 

    Results: The mean fiber direction for each parcel was confirmed to fit the anatomical configuration. The fiber-tracking streamlines were confirmed to run along the first eigenvector of diffusion tensor and to terminate according to the pre-set termination condition. The major fiber tracts of the 54 white matter parcels were relevantly reconstructed. 

    Conclusion: The results demonstrate the feasibility of the proposed automated whole-brain atlas-based fiber-tracking method for investigating white matter disruptions associated with neuropsychiatric disorders especially in large-scale datasets.

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Editor-in-Chiefs:
Jinglong Wu
Kewei Chen
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