M2ECG: Wearable Mechanocardiograms to Electrocardiogram Estimation Using Deep Learning

Abstract

Chest surface vibrations induced by cardiac activities can provide valuable insights into various heart conditions. Seismocardiogram (SCG) and Gyrocardiogram (GCG) signals, collectively referred to as Mechanocardiograms (MCG) and collected using a chest-mounted accelerometer and gyroscope, respectively, have the potential to serve as an effective alternative to Electrocardiograms (ECG) for continuous cardiac monitoring. In many cases, both modalities (MCG and ECG) can be used in tandem to monitor cardiac functions in both healthy subjects and Intensive Care Unit (ICU) patients. Direct acquisition of ECGs can be challenging in certain scenarios, such as with wearable devices, or due to issues with disconnections arising from loose contact surfaces or gel corrosion during long-term usage. ECG considered the gold standard for heart monitoring, is essential for a comprehensive assessment of cardiac parameters and patient health. MCGs have the potential to reliably estimate ECGs and can replace direct ECG acquisition procedures in such cases. In this study, we introduce M2ECG, a 1D-segmentation-based approach for translating ECG signals from the corresponding MCG signals acquired by an Inertial Measurement Unit (IMU) attached to the chest wall. Using the proposed SA-UNet, we achieved an average Pearson Correlation Coefficient (PCC) of 81.76% on a subject-independent test set. We also compared the estimated heart rates (HR) from the reconstructed ECGs to the ground truth ECGs to validate our model's performance. The overall HR correlation achieved on the subject-independent test set was around 94.167%. The highest correlation of the HR and HRV calculated from the translated and the ground truth ECGs were around 99.073% and 96.289%, respectively for the best test case. The strong correlation observed in cardiac parameters (HR, HRV) underscores the effectiveness of MCG, suggesting its potential use for continuous monitoring of cardiac patients.

Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study

“…Both strategies are highly dependent on the cleanliness of the signal. Some papers propose machine-learning techniques to detect heartbeat intervals, such as convolutional neural networks (CNNs) for classification [ 12 ], U-Net neural networks for semantic segmentation [ 13 , 14 ], unsupervised segmentation [ 15 ], deep dominant frequency regressor [ 16 ], BiLSTM networks [ 17 ], and data-adaptive variational mode decomposition (VMD) [ 18 ]. Recent studies have also proved the possibility of recording SCG using cameras with a precision that exceeds the PPG method [ 19 ].…”