HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications

Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

doi:10.1109/access.2017.2707460

Cited by 238 publications

(143 citation statements)

References 28 publications

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“…Applications of DL to cardiac signals are introduced very recently [6][7][8]. CNNs have been used for normal/abnormal PCG classification using input features such as spectrogram and Mel-frequency cepstrum coefficients (MFCCs) in [9] on 5-second windowed segments, and MFCC heatmaps of 3-second segments in [10].…”

Section: Introductionmentioning

confidence: 99%

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman

Ting

Salleh

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of twodimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time-and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.

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Section: Introductionmentioning

confidence: 99%

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman

Ting

Salleh

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Moreover, recent work [27] extended this approach to PPG (PhotoPlethysmoGram) signals, using wearable sensors. Furthermore, authors in [28] propose a multi-resolution CNN in the wavelet domain that extracts features independent of phase shifts. Our proposed methodology, instead, leverages a 1D-CNN based Variational AutoEncoder to extract relevant information from the morphology of SCG heartbeats, previously segmented by means of an unsupervised technique; the use of VAE implies a generative model, which may prove useful, e.g., in the context of anomaly detection [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Detection and Analysis of Heartbeats in Seismocardiogram Signals

Mora

Cocconcelli

Matrella

et al. 2020

Sensors

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This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).

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“…This dataset consists of 26, 000 face images from 2284 individuals. The dataset is categorized into eight age groups: [4,6], [8,13], [15,20], [25,32], [38,43], [48,53], [60, -]}. In our study, we assigned integer classes from 1 to 8 to the age groups sorted in increasing order.…”

Section: A Database Descriptionmentioning

confidence: 99%

“…Face images can also be used to infer a wide set of soft biometric characteristics, such as the emotional state, ethnicity, gender, and age. Among this set of characteristics, the automatic age estimation can be particularly important in different scenarios [3], such as security and defense applications [4], surveillance [5], health-care systems [6], [7], entertainment [8], automated…”

Section: Introductionmentioning

confidence: 99%

Age estimation based on face images and pre-trained convolutional neural networks

Anand

Labati

Genovese

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Age estimation based on face images plays an important role in a wide range of scenarios, including security and defense applications, border control, human-machine interaction in ambient intelligence applications, and recognition based on soft biometric information. Recent methods based on deep learning have shown promising performance in this field. Most of these methods use deep networks specifically designed and trained to cope with this problem. There are also some studies that focus on applying deep networks pre-trained for face recognition, which perform a fine-tuning to achieve accurate results. Differently, in this paper, we propose a preliminary study on increasing the performance of pre-trained deep networks by applying postprocessing strategies. The main advantage with respect to finetuning strategies consists of the simplicity and low computational cost of the post-processing step. To the best of our knowledge, this paper is the first study on age estimation that proposes the use of post-processing strategies for features extracted using pretrained deep networks. Our method exploits a set of pre-trained Convolutional Neural Networks (CNNs) to extract features from the input face image. The method then performs a feature level fusion, reduces the dimensionality of the feature space, and estimates the age of the individual by using a Feed-Forward Neural Network (FFNN). We evaluated the performance of our method on a public dataset (Adience Benchmark of Unfiltered Faces for Gender and Age Classification) and on a dataset of nonideal samples affected by controlled rotations, which we collected in our laboratory. Our age estimation method obtained better or comparable results with respect to state-of-the-art techniques and achieved satisfactory performance in non-ideal conditions. Results also showed that CNNs trained on general datasets can obtain satisfactory accuracy for different types of validation images, also without applying fine-tuning methods.

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HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications

Cited by 238 publications

References 28 publications

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Detection and Analysis of Heartbeats in Seismocardiogram Signals

Age estimation based on face images and pre-trained convolutional neural networks

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