ECG Biometrics Using Spectrograms and Deep Neural Networks

Bento, Nuno; Belo, David; Gamboa, Hugo

doi:10.18178/ijmlc.2020.10.2.929

Cited by 26 publications

(13 citation statements)

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“…Most of the available deep learning base methods used single session datasets for the evaluation of biometric recognition performance, as shown in Table 1. In [33,37,38,49,50], cross-session and same session records were tested. Table 1 summarizes state-of-theart approaches for the ECG biometrics based on segmentation type and length, feature extractor, and classification scheme.…”

Section: Related Workmentioning

confidence: 99%

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Alduwaile

Islam

2021

Entropy

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The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

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Section: Related Workmentioning

confidence: 99%

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Alduwaile

Islam

2021

Entropy

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“…al. 41 utilized two different DL classification models to study the classification of atrial fibrillation. Finally, RNN was used by Antczak 42 to automatically denoise ECG signals.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

Llanes-Jurado

Carrasco-Ribelles

Alcañíz

et al. 2021

Preprint

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Scholars are increasingly using electrodermal activity (EDA) to assess cognitive-emotional states in laboratory environments, while recent applications have recorded EDA in uncontrolled settings, such as daily-life and virtual reality (VR) contexts, in which users can freely walk and move their hands. However, these records can be affected by major artifacts stemming from movements that can obscure valuable information. Previous work has analyzed signal correction methods to improve the quality of the signal or proposed artifact recognition models based on time windows. Despite these efforts, the correction of EDA signals in uncontrolled environments is still limited, and no existing research has used a signal manually corrected by an expert as a benchmark. This work investigates different machine learning and deep learning architectures, including support vector machines, recurrent neural networks (RNNs), and convolutional neural networks, for the automatic artifact recognition of EDA signals. The data from 44 subjects during an immersive VR task were collected and cleaned by two experts as ground truth. The best model, which used an RNN fed with the raw signal, recognized 72% of the artifacts and had an accuracy of 87%. An automatic correction was performed on the detected artifacts through a combination of linear interpolation and a high degree polynomial. The evaluation of this correction showed that the automatically and manually corrected signals did not present differences in terms of phasic components, while both showed differences to the raw signal. This work provides a tool to automatically correct artifacts of EDA signals which can be used in uncontrolled conditions, allowing for the development of intelligent systems based on EDA monitoring without human intervention.

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“…The CNN models have been combined with sound spectrograms or mel-spectrograms for the generation of classification models [12][13][14][15][16][17]. Specifically, in [13], CNN and clustering techniques are combined to generate a dissimilarity space used to train an SVM for automated audio classification-in this case, audios of birds and cats.…”

Section: Convolutional Neural Network In Audio Classificationmentioning

confidence: 99%

“…Specifically, in [13], CNN and clustering techniques are combined to generate a dissimilarity space used to train an SVM for automated audio classification-in this case, audios of birds and cats. Ref [14] uses CNNs to identify healthy subjects using spectrograms of electrocardiograms. Ref [15] employs CNN with three audio attribute extraction techniques (mel-spectrogram, Mel Frequency Cepstral Coefficient and Log-Mel) in order to classify three datasets of environmental sounds.…”

Section: Convolutional Neural Network In Audio Classificationmentioning

confidence: 99%

Cyber-Physical System for Environmental Monitoring Based on Deep Learning

Monedero

Barbancho

Márquez

et al. 2021

Sensors

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Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS. This system is based on convolutional neural networks (CNNs) and is focused on the different types of vocalization of two species of anurans. CNNs, in conjunction with the use of mel-spectrograms for sounds, are shown to be an adequate tool for the classification of environmental sounds. The classification results obtained are excellent (97.53% overall accuracy) and can be considered a very promising use of the system for classifying other biological acoustic targets as well as analyzing biodiversity indices in the natural environment. The paper concludes by observing that the execution of this type of CNN, involving low-cost and reduced computing resources, are feasible for monitoring extensive natural areas. The use of CPS enables flexible and dynamic configuration and deployment of new CNN updates over remote IoT nodes.

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ECG Biometrics Using Spectrograms and Deep Neural Networks

Cited by 26 publications

References 0 publications

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Automatic Artifact Recognition and Correction for Electrodermal Activity in Uncontrolled Environments

Cyber-Physical System for Environmental Monitoring Based on Deep Learning

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