ECG image representation of normal sinus rythm

Soualhi, Kawther; Oueslati, Afef Elloumi; Ellouze, Noureddine

doi:10.1109/atsip.2014.6834611

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In general, we estimate HR by electrocardiography (ECG) in the clinic, which is measured by sensors that capture weak electrical activity generated by the cardiac cycle when the heart beats. ECG signals have been of great interest to researchers [ 4 , 5 , 6 ], because they include noticeable temporal features for human physical and psychological analysis under different circumstances. The explorations for heartbeats and ECG morphological features provide vital biomedical information for human status.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Estimation from Incomplete Electrocardiography Signals

Song

Chen

Zhang

2023

Sensors

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As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.

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Section: Introductionmentioning

confidence: 99%

Heart Rate Estimation from Incomplete Electrocardiography Signals

Song

Chen

Zhang

2023

Sensors

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“…Normally, the results of those transforms are 2-dimensional with axes of time and frequency. As studies to try signal processing in two dimensions, Soualhi et al changed the electrocardiogram into a synchronous representation, then rearranged it every period to construct a two-dimensional image [ 25 ]. Zhai and Tin constructed a dual beat coupling matrix with beat morphology and beat-to-beat correlation, using two adjacent electrocardiogram beats [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Byeon

Pan

Kwak

2019

Sensors

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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.

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