Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Yeong-Hyeon Byeon; Sung-Bum Pan; Keun-Chang Kwak

doi:10.3390/s19040935

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Sensors ◽

10.3390/s19040935 ◽

2019 ◽

Vol 19 (4) ◽

pp. 935 ◽

Cited By ~ 9

Author(s):

Yeong-Hyeon Byeon ◽

Sung-Bum Pan ◽

Keun-Chang Kwak

Keyword(s):

Frequency Domain ◽

Training Transfer ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

Ecg Signals ◽

Electrocardiogram Signals ◽

Noisy Signals ◽

The Time Domain ◽

Electrocardiogram Ecg

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG.

Download Full-text

Transfer learning for ECG classification

Scientific Reports ◽

10.1038/s41598-021-84374-8 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Kuba Weimann ◽

Tim O. F. Conrad

Keyword(s):

Transfer Learning ◽

Small Data ◽

Deep Convolutional Neural Networks ◽

Data Set ◽

Ecg Signals ◽

Heart Arrhythmia ◽

Public Data ◽

Electrocardiogram Ecg ◽

Monitoring Devices

AbstractRemote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to $$6.57\%$$ 6.57 % , effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning.

Download Full-text

Compression, Denoising and Classification of ECG Signals using the Discrete Wavelet Transform and Deep Convolutional Neural Networks

2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) ◽

10.1109/spmb50085.2020.9353618 ◽

2020 ◽

Author(s):

M. Chowdhury ◽

K. Poudel ◽

Y. Hu

Keyword(s):

Neural Networks ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Convolutional Neural Networks ◽

Discrete Wavelet ◽

Deep Convolutional Neural Networks ◽

Ecg Signals

Download Full-text

ECG Beats Classification Using Mixture of Features

International Scholarly Research Notices ◽

10.1155/2014/178436 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 21

Author(s):

Manab Kumar Das ◽

Samit Ari

Keyword(s):

Feature Extraction ◽

Extraction Methods ◽

Ectopic Beat ◽

Extraction Techniques ◽

Efficient System ◽

Ecg Signals ◽

Temporal Features ◽

S Transform ◽

Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

Download Full-text

PRINCIPLES OF DIGITAL FILTERING

Geophysics ◽

10.1190/1.1439370 ◽

1964 ◽

Vol 29 (3) ◽

pp. 395-404 ◽

Cited By ~ 45

Author(s):

E. A. Robinson ◽

S. Treitel

Keyword(s):

Frequency Domain ◽

Digital Filter ◽

Time Domain ◽

Digital Filtering ◽

Phase Lag ◽

Electronic Networks ◽

Versatile Tool ◽

The Time Domain ◽

Weighting Coefficients

The digital computer is a versatile tool that may be used to filter seismic traces. Conventional filtering is performed by means of analog‐type electronic networks, whose behavior is ordinarily studied in the frequency domain. Digital filtering, on the other hand, is more fruitfully treated in the time domain. A digital filter is represented by a sequence of numbers called weighting coefficients. The output of a digital filter is obtained by convolving the digitized input trace with the filter’s weighting coefficients. The mechanics of digital filtering in the time domain are described with the aid of discrete z‐transform theory. These ideas are then related to the more familiar interpretation of filter behavior in the frequency domain. An important criterion for the classification of filters is the notion of “minimum phase‐lag.” This paper ends with a new and simple presentation of this concept.

Download Full-text

Detection & Classification of Cardiac Arrhythmia

International Journal of Informatics and Communication Technology (IJ-ICT) ◽

10.11591/ijict.v6i1.pp31-36 ◽

2017 ◽

Vol 6 (1) ◽

pp. 31

Author(s):

Chetan M. Jadhav ◽

V. K. Bairagi

Keyword(s):

Cardiac Arrhythmia ◽

Early Stage ◽

Low Cost ◽

Simple Method ◽

Arrhythmia Detection ◽

Ecg Signals ◽

Electrocardiogram Signals ◽

Electrical Impulses ◽

Very High

The term Arrhythmia refers to any change from the normal sequence in the electrical impulses. It is also treated as abnormal heart rhythms or irregular heartbeats. The rate of growth of Cardiac Arrhythmia disease is very high & its effects can be observed in any age group in society. Arrhythmia detection can be done in many ways but effective & simple method for detection & diagnosis of Cardiac Arrhythmia is by doing analysis of Electrocardiogram signals from ECG sensors. ECG signal can give us the detail information of heart activities, so we can use ECG signals to detect the rhythm & behaviour of heart beats resulting into detection & diagnosis of Cardiac Arrhythmia. In this paper new & improved methodology for early Detection & Classification of Cardiac Arrhythmia has been proposed. In this paper ECG signals are captured using ECG sensors & this ECG signals are used & processed to get the required data regarding heart beats of the human being & then proposed methodology applies for Detection & Classification of Cardiac Arrhythmia. Detection of Cardiac Arrhythmia using ECG signals allows us for easy & reliable way with low cost solution to diagnose Arrhythmia in its prior early stage.

Download Full-text

Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽

10.3390/s18092835 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2835 ◽

Cited By ~ 3

Author(s):

Zhongjie Hou ◽

Jinxi Xiang ◽

Yonggui Dong ◽

Xiaohui Xue ◽

Hao Xiong ◽

...

Keyword(s):

Printed Circuit Board ◽

Experimental Tests ◽

Circuit Board ◽

Signal Acquisition ◽

Ecg Signal ◽

Capacitively Coupled ◽

Ecg Signals ◽

Printed Circuit ◽

Electrocardiogram Signals ◽

Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

Download Full-text

Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy

Sensors ◽

10.3390/s20216353 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6353

Author(s):

Pasquale Memmolo ◽

Pierluigi Carcagnì ◽

Vittorio Bianco ◽

Francesco Merola ◽

Andouglas Goncalves da Silva Junior ◽

...

Keyword(s):

Data Augmentation ◽

Imaging Modality ◽

Training Data ◽

Classification Problems ◽

Deep Convolutional Neural Networks ◽

Live Diatoms ◽

Freshwater Habitats ◽

Commercial Glass ◽

Holographic Microscopy

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Download Full-text

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

Bioscience Reports ◽

10.1042/bsr20180289 ◽

2018 ◽

Vol 38 (3) ◽

Cited By ~ 11

Author(s):

Miao Wu ◽

Chuanbo Yan ◽

Huiqiang Liu ◽

Qian Liu

Keyword(s):

Neural Networks ◽

Ovarian Cancer ◽

Convolutional Neural Networks ◽

Image Data ◽

Training Data ◽

Serous Carcinoma ◽

Deep Convolutional Neural Networks ◽

Ovarian Cancers ◽

Cancer Types

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

Download Full-text

ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6649970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Enbiao Jing ◽

Haiyang Zhang ◽

ZhiGang Li ◽

Yazhi Liu ◽

Zhanlin Ji ◽

...

Keyword(s):

State Of The Art ◽

Classification Performance ◽

Classification Models ◽

Heartbeat Classification ◽

Ecg Signals ◽

Residual Structure ◽

Proposed Model ◽

Model Training ◽

Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

Download Full-text

ADVERSARIAL DOMAIN ADAPTATION FOR THE CLASSIFICATION OF AERIAL IMAGES AND HEIGHT DATA USING CONVOLUTIONAL NEURAL NETWORKS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w7-197-2019 ◽

2019 ◽

Vol IV-2/W7 ◽

pp. 197-204 ◽

Cited By ~ 1

Author(s):

D. Wittich ◽

F. Rottensteiner

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Domain Adaptation ◽

Feature Representation ◽

Training Data ◽

Aerial Images ◽

Remotely Sensed Data ◽

Target Domain ◽

Deep Convolutional Neural Networks

Abstract. Domain adaptation (DA) can drastically decrease the amount of training data needed to obtain good classification models by leveraging available data from a source domain for the classification of a new (target) domains. In this paper, we address deep DA, i.e. DA with deep convolutional neural networks (CNN), a problem that has not been addressed frequently in remote sensing. We present a new method for semi-supervised DA for the task of pixel-based classification by a CNN. After proposing an encoder-decoder-based fully convolutional neural network (FCN), we adapt a method for adversarial discriminative DA to be applicable to the pixel-based classification of remotely sensed data based on this network. It tries to learn a feature representation that is domain invariant; domain-invariance is measured by a classifier’s incapability of predicting from which domain a sample was generated. We evaluate our FCN on the ISPRS labelling challenge, showing that it is close to the best-performing models. DA is evaluated on the basis of three domains. We compare different network configurations and perform the representation transfer at different layers of the network. We show that when using a proper layer for adaptation, our method achieves a positive transfer and thus an improved classification accuracy in the target domain for all evaluated combinations of source and target domains.

Download Full-text