Deep Learning Based on 1-D Ensemble Networks Using ECG for Real-Time User Recognition

Kim, Mingu; Pan, Sung Bum

doi:10.1109/tii.2019.2909730

Cited by 42 publications

(22 citation statements)

References 24 publications

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“…where N c denotes the number of pixels reconstructed correctly and N denotes the total number of pixels [50]. Cross entropy loss between network predictions and target values is defined as Equation (13).…”

Section: Lstm With a Singular Raw Ecg Signal Inputmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.

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Section: Lstm With a Singular Raw Ecg Signal Inputmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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show abstract

“…The recently introduced ResNet and DenseNet were proposed to solve the problem of the initial characteristics of images being lost at the final output stage as the CNN model becomes deeper. However, the ECG signal consists of simple waveforms, unlike general data which feature complex patterns [24], [25].…”

Section: B Parallel Architecture Based Ensemble Networkmentioning

confidence: 99%

Ensemble Networks for User Recognition in Various Situations Based on Electrocardiogram

2020

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Research on electrocardiogram (ECG) signals has been actively undertaken to assess their value as a next generation user recognition technology, because they require no stimulation and are robust against forgery and modification. However, even within the same user, the heart rate and waveform of ECG signals will vary depending on physical activity, mental effects, and measurement time. Therefore, when data acquired across changes in the user state is used as registered data, an overfitting problem occurs due to data generalization, which degrades the recognition performance for newly acquired data. Therefore, in this paper, we propose parallel ensemble networks to solve the overfitting problem and prevent the degradation. First, ECG signals acquired in various environments are used as the input data of parallel convolutional neural networks (CNNs). Each CNN is set up with different parameters to detect different features. The ECG signals outputted from each network are classified for each subject, and then fused into one database to be used as registered data for retraining. Instead of fusing all the output signals from each network, only the ECG signals of Top-3 networks showing excellent performance are fused and composed of registered data. The reconstructed registered data are used for user recognition, by retraining with time independent comparison data in the CNN. The experimental results of comparing the proposed parallel ensemble networks with those of previous studies using the self-acquired actual ECG signals show that the proposed method achieves recognition performance higher than the previous studies, with an accuracy rate of 98.5%. INDEX TERMS ECG, biometrics, user recognition, various situations, parallel ensemble networks.

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“…Maguolo [33] studied an ensemble of convolutional neural networks trained with different activation functions using sum rule to improve the performance in smallor medium-sized biomedical datasets. Kim [34] studied deep learning based on 1D ensemble networks using an electrocardiogram for user recognition. The ensemble network is composed of three CNN models with different parameters and their outputs are combined into single data.…”

Section: Introductionmentioning

confidence: 99%

Posture Recognition Using Ensemble Deep Models under Various Home Environments

et al. 2020

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This paper is concerned with posture recognition using ensemble convolutional neural networks (CNNs) in home environments. With the increasing number of elderly people living alone at home, posture recognition is very important for helping elderly people cope with sudden danger. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points, depth, frame information of video, and so on. In conventional machine learning, there is a limitation in recognizing posture directly using only an image. However, with advancements in the latest deep learning, it is possible to achieve good performance in posture recognition using only an image. Thus, we performed experiments based on VGGNet, ResNet, DenseNet, InceptionResNet, and Xception as pre-trained CNNs using five types of preprocessing. On the basis of these deep learning methods, we finally present the ensemble deep model combined by majority and average methods. The experiments were performed by a posture database constructed at the Electronics and Telecommunications Research Institute (ETRI), Korea. This database consists of 51,000 images with 10 postures from 51 home environments. The experimental results reveal that the ensemble system by InceptionResNetV2s with five types of preprocessing shows good performance in comparison to other combination methods and the pre-trained CNN itself. 15 of 26 preprocessed posture images for the testing was 500 for standing normal, 497 for standing bent, 500 for sitting sofa, 500 for sitting chair, 497 for sitting floor, 498 for sitting squat, 489 for lying face, 489 for lying back, 482 for lying side, 481 for lying crouched, and 2001 for other images. The total number of images for the training, validation, and testing was 44,874, 7913, and 6934, respectively. detector [39], and the segmented person images were filtered manually. The number of preprocessed posture images for the training was 3910 for standing normal, 3802 for standing bent, 3899 for sitting sofa, 3899 for sitting chair, 3896 for sitting floor, 3859 for sitting squat, 3752 for lying face, 3777 for lying back, 3773 for lying side, 3505 for lying crouched, and 6802 for other images. The number of preprocessed posture images for the validation was 689 for standing normal, 670 for standing bent, 688 for sitting sofa, 687 for sitting chair, 687 for sitting floor, 681 for sitting squat, 662 for lying face, 666 for lying back, 665 for lying side, 618 for lying crouched, and 1200 for other images. The number of preprocessed posture images for the testing was 500 for standing normal, 497 for standing bent, 500 for sitting sofa, 500 for sitting chair, 497 for sitting floor, 498 for sitting squat, 489 for lying face, 489 for lying back, 482 for lying side, 481 for lying crouched, and 2001 for other images. The total number of images for the training, validation, and testing was 44,874, 7913, and 6934, respectively.

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Deep Learning Based on 1-D Ensemble Networks Using ECG for Real-Time User Recognition

Cited by 42 publications

References 24 publications

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Ensemble Networks for User Recognition in Various Situations Based on Electrocardiogram

Posture Recognition Using Ensemble Deep Models under Various Home Environments

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