Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition

Abstract

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.

Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors

“…As shown in Figure 4a,b, the data showed a high correlation coefficient, even for a relatively long 10 min period. It was confirmed that the sheet sensor had high measurement accuracy comparable to ECG [20,35,36]. As our sheet sensor employed a differential amplifier circuit, the elimination of the effects of power-supply noise and the measurement of biological vibrations with a high signal-to-noise ratio are possible.…”

“…Many unobstructive sensing methods observing vital signs, such as heartbeat and respiration, have been evaluated for integration into unobstructive vital sensors that are based on the detection principle of air pressure [6][7][8], water pressure [9], cameras [10,11], microphones [12], load cells [13][14][15], piezoresistive [16], capacitively-coupled electrodes [17], and piezoelectric films [1,[18][19][20][21][22][23][24][25][26][27][28]. Among these, the sheet-type vital sensor using polyvinylidene fluoride (PVDF) as the pressure-sensitive layer is one of the most promising vital 2 of 12 sensors from the perspective of sensitivity and adaptability to various measurement environments [1,18,[21][22][23][24][25][26][27][28].…”

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch

“… 20 However, the BCG signal can measure a good quality cardiac signal similar to an ECG signal with appropriate signal preprocessing and effectively be analyzed through nonlinear methods. 25 , 26 Nonlinear methods offer a more accurate modeling of physiological signal dynamics, providing greater insights into cardiac state and improving sleep stage classification with non-intrusive devices. 27 …”