Mathematical Methods of Fetal Activity Signal Processing

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

“…To validate the strength of proposed fetal movement signals preprocessing algorithm, the experimental analysis is performed using tenfold cross-validation. The performance of the proposed Kalman filter (KF) preprocessing algorithm is compared with the band-pass filter (BPF) algorithm with a bandwidth of 0.5 Hz–20 Hz [ 42 ], Singular Spectrum Analysis (SSA) algorithm [ 44 ], Empirical Mode Decomposition (EMD) algorithm, Ensemble Empirical Mode Decomposition (EEMD) algorithm, and Adaptive Wavelet Transform (AWT) algorithm [ 43 ]. The existing BPF, SSA, EMD, EEMD, AWT fetal movement signal preprocessing algorithms, and the proposed KF algorithm combined with time domain, frequency domain, and wavelet domain feature extraction methods and Bayesian-optimized LightGBM model are analyzed for tenfold cross validation.…”

“…To validate the strength of proposed fetal movement signals preprocessing algorithm, the experimental analysis is performed using tenfold cross-validation. e performance of the proposed Kalman filter (KF) preprocessing algorithm is compared with the band-pass filter (BPF) algorithm with a bandwidth of 0.5 Hz-20 Hz [42], Singular Spectrum Analysis (SSA) algorithm [44] 4 shows the comparative analysis of the proposed preprocessing algorithm with the previously studied algorithms, where the parameter type of the EMD technique is the intrinsic modal function (IMF), the parameter type of the EEMD method is the noise standard deviation (STD) and the intrinsic modal function (IMF), and the parameter type of the AWT technique is the wavelet type (WT) and the thresholding (THR).…”

“…Zhao et al [ 41 ] used time domain and discrete wavelet domain (TWD) methods to extract potential fetal movement features. Bobrova et al [ 42 ] used a band-pass filter (BPF) algorithm with a bandwidth of 0.5 Hz–20 Hz for denoising. This method has a good suppression effect on the noise outside the frequency band, but it is difficult to filter out the noise signal that overlaps with the spectrum of the fetal movement signal.…”

“…In recent years, with the rapid development of intelligent sensing devices and the advancement of modern digital information processing technology, automatic recognition of fetal movements using microacceleration sensors and efficient signal processing algorithms has received wide attention [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. e accelerometer sensor is embedded in a wearable device and worn on the abdomen of pregnant women to detect a series of micromovements on the surface of the abdomen.…”

“…Zhao et al [41] used time domain and discrete wavelet domain (TWD) methods to extract potential fetal movement features. Bobrova et al [42] used a band-pass filter (BPF) algorithm with a bandwidth of 0.5 Hz-20 Hz for denoising.…”