The Impact of Bathtub Water Temperature on Personal Identification with ECG Signal based on Convolutional Neural Network

Xu, Jianbo; Li, Tianhui; Chen, Ying; Chen, Wenxi

doi:10.1109/compcomm.2018.8780637

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…What is more, the water pressure on the chest and thermal stimulus on hemodynamics induce varying stress levels to subjects during bathing. As confirmed in a previous study of ours [23], different WTs have an important impact on the recognition rate during bathing. The impact of ECG length during bathing on the recognition rate has remained unknown until now.…”

Section: Related Worksupporting

confidence: 88%

Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

El‐Shafai¹,

El-Nabi²,

El‐Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

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This study proposes a convolutional neural network (CNN)-based identity recognition scheme using electrocardiogram (ECG) at different water temperatures (WTs) during bathing, aiming to explore the impact of ECG length on the recognition rate. ECG data was collected using non-contact electrodes at five different WTs during bathing. Ten young student subjects (seven men and three women) participated in data collection. Three ECG recordings were collected at each preset bathtub WT for each subject. Each recording is 18 min long, with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. The R peaks were detected based on the processed ECG (baseline wandering eliminated, 50-Hz hum removed, ECG smoothing and ECG normalization) and the QRS complex waves were segmented. These segmented waves were then transformed into binary images, which served as the datasets. For each subject, the training, validation, and test data were taken from the first, second, and third ECG recordings, respectively. The number of training and validation images was 84297 and 83734, respectively. In the test stage, the preliminary classification results were obtained using the trained CNN model, and the finer classification results were determined using the majority vote method based on the preliminary results. The validation rate was 98.71%. The recognition rates were 95.00% and 98.00% when the number of test heartbeats was 7 and 17, respectively, for each subject.

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Section: Related Worksupporting

confidence: 88%

Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

El‐Shafai¹,

El-Nabi²,

El‐Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A deep convolutional neural network is proposed in [18] to classify 1.2 million images and shows good effect. In [19], Human ECG signals are converted into binary images and delivered to a CNN to do recognitions. The approach provides a new perspective for the application of CNNs and shows good performance in signal recognition.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

Chen

Yang

et al. 2020

IEEE Access

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The early detection of Diabetic Retinopathy (DR) is critical for diabetics to lower the blindness risks. Many studies represent that Deep Convolutional Neural Network (CNN) based approaches are effective to enable automatic DR detection through classifying retinal images of patients. Such approaches usually depend on a very large dataset composed of retinal images with predefined classification labels to support their CNN training. However, in some occasions, it is not so easy to get enough welllabelled images to act as model training samples. At the same time, when a CNN becomes deeper, its training will not only take much longer time, but also be more likely to lead to overfitting, especially on a large training dataset. Therefore, it is meaningful to explore a simpler CNN based approach that is still effective on small datasets to classify retinal images. In this paper, an approach to retinal image classification is proposed based on the integration of multi-scale shallow CNNs. Experiments on public datasets show that, on small datasets, the proposed approach can improve the classification accuracy by 3% compared with current representative integrated CNN learning approaches. On the bigger dataset, the proposed approach can improve the classification accuracy by 3% to 9% compared with other representative approaches such as traditional CNN, LCNN and VGG16noFC. The evaluation also represents that, though the classification accuracy of the proposed approach declines by 6% on the smallest dataset containing only 10% samples of the original dataset, its time cost declines to about 30% of that on the original dataset. INDEX TERMS convolutional neural network, diabetic retinopathy, image classification, integrated learning, performance integration.

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ECG-based Identity Validation during Bathing in Different Water Temperature

Cui

Chen

2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The Impact of Bathtub Water Temperature on Personal Identification with ECG Signal based on Convolutional Neural Network

Cited by 3 publications

References 20 publications

Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

Convolutional Neural Network-Based Identity Recognition Using ECG at Different Water Temperatures During Bathing

An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

ECG-based Identity Validation during Bathing in Different Water Temperature

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