A Novel Electrocardiogram Biometric Identification Method Based on Temporal-Frequency Autoencoding

Abstract

For good performance, most existing electrocardiogram (ECG) identification methods still need to adopt a denoising process to remove noise interference beforehand. This specific signal preprocessing technique requires great efforts for algorithm engineering and is usually complicated and time-consuming. To more conveniently remove the influence of noise interference and realize accurate identification, a novel temporal-frequency autoencoding based method is proposed. In particular, the raw data is firstly transformed into the wavelet domain, where multi-level time-frequency representation is achieved. Then, a prior knowledge-based feature selection is proposed and applied to the transformed data to discard noise components and retain identity-related information simultaneously. Afterward, the stacked sparse autoencoder is introduced to learn intrinsic discriminative features from the selected data, and Softmax classifier is used to perform the identification task. The effectiveness of the proposed method is evaluated on two public databases, namely, ECG-ID and Massachusetts Institute of Technology-Biotechnology arrhythmia (MIT-BIH-AHA) databases. Experimental results show that our method can achieve high multiple-heartbeat identification accuracies of 98.87%, 92.3%, and 96.82% on raw ECG signals which are from the ECG-ID (Two-recording), ECG-ID (All-recording), and MIT-BIH-AHA database, respectively, indicating that our method can provide an efficient way for ECG biometric identification.

Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification

“…In the previous studies [11], [28], the authors used the daubechies wavelet function for filtering ECG signals. In our study, the dmey wavelet function was chosen because its shape is close to that of a heartbeat.…”

“…xn is the ECG signals and m is the level of the wavelet decomposition. Moreover, the expressions for calculating the approximation d m and detail a m coefficients are expressed as follows [28]:…”

“…In addition, the convolutional layers are utilized to extract features in the ECG signals, and the fully connected layers, which are based on the features extracted from the convolutional layers, are used for classifying heart disease [26], [27]. In practice, a combination of convolutional layers and Long Short-Term Memory layers was employed for extraction of ECG features, and these features are used for the input of the fully connected layers to identify five types of heart disease using the MIT-BIH Arrhythmia Database (AD) [28]. The result is that the accuracy of 98.10 % was achieved using this classifier.…”

Deep Learning-Based Stacked Denoising and Autoencoder for ECG Heartbeat Classification

“…However, the ECG signal acquisition involves high-gain instrumentation amplifiers that are easily contaminated by different sources of noise, with characteristic frequency spectrums depending on the source [2]. ECG contaminants can be classified into different categories, including [2][3][4]; (i) power line interference at 60 or 50 Hz, depending on the power supply frequency; (ii) electrode contact noise of about 1 Hz, caused by improper contact between the body and electrodes; (iii) motion artifacts that produce long distortions at 100-500 ms, caused by patient's movements, affecting the electrode-skin impedance; (iv) muscle contractions, producing noise up to 10% of regular peak-to-peak ECG amplitude and frequency up to 10 kHz around 50 ms; and (v) baseline wander caused by respiratory activity at 0-0.5 Hz. All of these kinds of noise can interfere with the original ECG signal, which may cause deformations on the ECG waveforms and produce an abnormal signal.…”

“…To keep as much of the ECG signal as possible, the noise must be removed from the original signal to provide an accurate diagnosis. Unfortunately, the denoising process is a challenging task due to the overlap of all the noise signals at both low and high frequencies [4]. To prevent noise interference, several approaches have been proposed to denoise ECG signals based on adaptive filtering [5][6][7], wavelet methods [8,9], and empirical mode decomposition [10,11].…”