Key result
The novel algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition accurately estimated heart rate from BCG signals without relying on ECG, achieving a mean absolute error of 0.99 bpm.
Why the study?
Most existing BCG algorithms only perform inter-beat interval estimation rather than identifying the clinically significant IJK-complex and detailed wave morphology.
Effect estimate: MAE 0.99 bpm
A novel algorithm integrating STFT and EEMD accurately identifies the IJK-complex in ballistocardiogram signals, enabling detailed non-invasive monitoring of heart rate and cardiac function.
The Ballistocardiogram (BCG) is a vibration signal that is generated by the displacement of the entire body due to the injection of blood during each heartbeat. It has been extensively utilized to monitor heart rate. The morphological features of the BCG signal serve as effective indicators for the identification of atrial fibrillation and heart failure, holding great significance for BCG signal analysis. The IJK-complex identification allows for the estimation of inter-beat intervals (IBI) and enables a more detailed analysis of BCG amplitude and interval waves. This study presents a novel algorithm for identifying the IJK-complex in BCG signals, which is an improvement over most existing algorithms that only perform IBI estimation. The proposed algorithm employs a short-time Fourier transform and summation across frequencies to initially estimate the occurrence of the J wave using peak finding, followed by Ensemble Empirical Mode Decomposition and a regional search to precisely identify the J wave. The algorithm's ability to detect the morphological features of BCG signals and estimate heart rates was validated through experiments conducted on 10 healthy subjects and 2 patients with coronary heart disease. In comparison to commonly used methods, the presented scheme ensures accurate heart rate estimation and exhibits superior capability in detecting BCG morphological features. This advancement holds significant value for future applications involving BCG signals.
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Feng et al. (2023) studied Healthy and Coronary Heart Disease (n=12). STFT and EEMD-based BCG signal processing algorithm vs. ECG-based IJK-complex localization algorithm was evaluated on Mean absolute error (MAE) of heart rate estimation (MAE 0.99 bpm). The novel algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition accurately estimated heart rate from BCG signals without relying on ECG, achieving a mean absolute error of 0.99 bpm.
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