Combination of ECG and PPG Signals for Healthcare Applications: A Survey

Bassiouni, Mahmoud M.; Hegazy, Islam; Rizk, Nouhad; El-Dahshan, Sayed A.; Salem, Abdel-Badeeh M.

doi:10.18280/ama_b.641-409

Cited by 3 publications

(2 citation statements)

References 38 publications

(71 reference statements)

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“…A new ECG was produced by the suggested GAN using noise as input. Last but not least, related research [28] improved arrhythmia detection performance by generating ECG from input noise to supplement the existing ECG training set.…”

Section: Related Workmentioning

confidence: 99%

ECG Signal Reconstruction from PPG Using Hybrid Deep Neural Networks

Ezzat,

Omer,

Mohamed

et al. 2024

RIA

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Electrocardiograms (ECGs) and photoplethysmography (PPG) facilitate non-invasive cardiovascular monitoring; however, the correlation between their respective waveforms, which exhibit high cycle correlation, remains underexplored. This study aims to estimate ECG signals from PPG data using an array of Deep Neural Networks (DNNs) across varied transformation feature domains, thereby making PPG measurements a more expedient and less effort-intensive alternative to ECG acquisition. A novel, subject-specific deep learning model is introduced, combining the architectures of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory (BiLSTM), termed ConvBiLSTM. This hybrid model proposes an automatic method for ECG signal reconstruction. To ensure model robustness against deformation, spatial characteristics are first extracted using CNNs, followed by the extraction of temporal characteristics from the CNN output via BiLSTM. The BiLSTM approach mitigates the issues of gradient disappearance and expansion without compromising accuracy, an improvement over traditional RNN and LSTM methods. The performance of four distinct feature domains, namely the Time Domain (TD), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Wavelet Scattering Transform (WST), is evaluated with regards to their efficacy in ECG signal reconstruction from PPG data using the ConvBiLSTM model. Superiority of the proposed DNN combination over individual DNNs was demonstrated through comparison of ConvBiLSTM performance. Simulation results reveal that our method achieves superior root mean square error (RMSE) in ECG signal reconstruction across all feature domains. Given the widespread application of RMSE in ECG monitoring, this metric was chosen as the key evaluation criterion. The combination of WST for PPG signals and DWT for ECG signal features demonstrated the lowest RMSE at 0.0654, indicating the potential of this approach for effective ECG signal reconstruction using PPG data.

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Section: Related Workmentioning

confidence: 99%

ECG Signal Reconstruction from PPG Using Hybrid Deep Neural Networks

Ezzat,

Omer,

Mohamed

et al. 2024

RIA

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“…It is recommended to learn information or features automatically using deep learning. Therefore, the CNN model is developed to obtained robust features from the ECG records [16].…”

Section: Feature Extractionmentioning

confidence: 99%

1D CNN model for ECG diagnosis based on several classifiers

Bassiouni

Hegazy

Rizk

et al. 2022

SRIT

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One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.

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Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy

Pan

Wang

et al. 2024

THC

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BACKGROUND: Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. OBJECTIVE: Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. METHODS: Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). RESULTS: Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEI𝑆𝑆 (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEI𝐿𝑆 (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. CONCLUSION: Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.

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Combination of ECG and PPG Signals for Healthcare Applications: A Survey

Cited by 3 publications

References 38 publications

ECG Signal Reconstruction from PPG Using Hybrid Deep Neural Networks

ECG Signal Reconstruction from PPG Using Hybrid Deep Neural Networks

1D CNN model for ECG diagnosis based on several classifiers

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy

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