Detection of Fetal Heart Rate Through 3-D Phase Space Analysis From Multivariate Abdominal Recordings

Abstract

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal ECG recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using band-pass filtering and phase space analysis. The maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs, which are eliminated in the first stage. The extraction of the fHR is accomplished in the third stage, using a histogram-based technique in order to identify the location of the fetal heart beats that overlap with the maternal QRSs. The methodology is evaluated on simulated multichannel ECG signals, generated by a recently proposed model with various SNRs, and on real signals, recorded from pregnant women in various weeks during gestation. In both cases, the obtained results indicate high performance; in the simulated ECGs, the accuracy ranges from 72.78% to 98.61%, depending on the employed SNR, while in the real recordings, the average accuracy is 95.45%. The proposed methodology is advantageous since it copes with the existence of noise from various sources while it is applicable in multichannel abdominal recordings.

Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG

“…The phase-space analysis technique has previously been used successfully as a method for the detection of coronary occlusion, the identification of ECG arrhythmias, analyses of QRS-complex time series, for distinguishing extrasystoles from normal heartbeats and for the understanding of heart rhythm dynamics [15][16][17][18][19][20][21][22][23][24][25][26]. However, in the above studies, the PSR method relies on visual examination of trajectories in PSR [27].…”

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

“…Using the nonlinear manifold structure to analyze a time series is certainly not a new idea [22,37,38]. However, in the past, the focus was on decoupling maECG and fECG by the locally linear projection on the sequential beats.…”

“…Most algorithms need multiple aECG channels and/or one maternal thoracic ECG (mtECG) signal, or at least one aECG channel and one mtECG; including: blind source separation (BSS) [6][7][8][9], semi-BSS like periodic component analysis (πCA), or πTucker decomposition, which takes the pseudo-periodic structure into account [10][11][12], echo state neural network [13], least mean square (LMS) [14], recursive least square (RLS) [13], and blind adaptive filtering [15], Kalman filter [16][17][18], channel selection approach based on features extracted by different methods, like discrete wavelet transform [19], timeadaptive Wiener-filter like filtering [20], principal component regression [21], phase space embedding [22], to name but a few. On the other hand, fewer algorithms depend on the singlelead aECG signal; e.g., template subtraction (TS) [13,[23][24][25][26], and its variation based on singular value decomposition (SVD) or principal component analysis [27,28], the time-frequency analysis, like wavelet transform, pseudo-smooth Wigner-Ville distribution [29][30][31][32] (in practice, three aECG channels are averaged in [30]), and S-transform [33], sequential total variation [34], adaptive neuro-fuzzy inference system and extended Kalman filter [35], particle swarm optimization and extended Kalman smoother [36] state space reconstruction via lag map [37,38], etc.…”

Non- Invasive Method of Fetal ECG Extraction Using Hilbert Transform

“…Various techniques have been employed to address this problem, including digital filtering and cross-correlation with template [2], template subtraction [3], matched filtering [4], linear combination of phase difference corrected signals [5], independent component analysis (ICA) [6,7] and time-frequency analysis [8][9][10][11][12]. In this paper, an integrated approach for fetal heart beat detection is proposed, combining the Hilbert transform (HT) and non-linear state-space projections (NSSPs).…”