A Barker sequence is employed for the synchronization of two photoplethysmogram (PPG) channels. The correctness of this technique is demonstrated by recording a PPG signal, injecting a Barker sequence at the start of this trace and producing a delayed version of it. After preprocessing, cross-correlation techniques are utilized for accurate time alignment of the two traces. The algorithm can correct for any time misalignment as long as the synchronization sequence appears on both channels.
A parametric approach is proposed for the characterization of the upper vascular tree. The novelty of the approach resides in it being differential: the processed signals are the photoplethysmograms obtained non-invasively from the left and right indexes. The investigated model is an ARX (auto-regressive with exogenous input) where the order has been estimated utilizing standard techniques. The model coefficients show a relatively high degree of stability.
In obstetrics, fetal heart rate (FHR) detection remains the standard for intrapartum assessment of fetal well-being. In this paper, a low-power (< 55 mW) optical technique is proposed for transabdominal FHR detection using near-infrared photoplesthysmography (PPG). A beam of IR-LED (890 nm) propagates through to the maternal abdomen and fetal tissues, resulting in a mixed signal detected by a low-noise detector situated at a distance of 4 cm. Low-noise amplification and 24-bit analog-to-digital converter resolution ensure minimum effect of quantization noise. After synchronous detection, the mixed signal is processed by an adaptive filter to extract the fetal signal, whereas the PPG from the mother's index finger is the reference input. A total of 24 datasets were acquired from six subjects at 37 +/- 2 gestational weeks. Results show a correlation coefficient of 0.96 (p-value < 0.001) between the proposed optical and ultrasound FHR, with a maximum error of 4%. Assessment of the effect of probe position on detection accuracy indicates that the probe should be close to fetal tissues, but not necessarily restricted to head or buttocks.
Cardiotocograph (CTG) is widely used in everyday clinical practice for fetal surveillance, where it is used to record fetal heart rate (FHR) and uterine activity (UA). These two biosignals can be used for antepartum and intrapartum fetal monitoring and are, in fact, nonlinear and non-stationary. CTG recordings are often corrupted by artifacts such as missing beats in FHR, high-frequency noise in FHR and UA signals. In this paper, an empirical mode decomposition (EMD) method is applied on CTG signals. A recursive algorithm is first utilized to eliminate missing beats. High-frequency noise is reduced using EMD followed by the partial reconstruction (PAR) method, where the noise order is identified by a statistical method. The obtained signal enhancement from the proposed method is validated by comparing the resulting traces with the output obtained by applying classical signal processing methods such as Butterworth low-pass filtering, linear interpolation and a moving average filter on 12 CTG signals. Three obstetricians evaluated all 12 sets of traces and rated the proposed method, on average, 3.8 out of 5 on a scale of 1(lowest) to 5 (highest).
Bilateral PPG signals have been used for comparative study of two groups of healthy (free from any cardiovascular risk factors) and diabetic (as cardiovascular disease risk group) subjects in the age-matched range 40-50 years. The peripheral blood pulsations were recorded simultaneously from right and left index fingers for 90 s. Pulses have been modeled with the ARX440 model in the interval of 300 sample points with 100 sample points overlap between segments. Model parameters of three segments based on the highest fitness (higher than 80%) of modeled segments were retained for each subject. Subsequently, principal component analysis (PCA) was applied to the parameters of retained segments to eliminate the existing correlation among parameters and provide uncorrelated variables. The first principal component (contains 78.2% variance of data) was significantly greater in diabetic than in control groups (P < 0.0001, 0.74 +/- 2.01 versus -0.53 +/- 1.66). In addition the seventh principal component, which contains 0.02% of the data variance, was significantly lower in diabetic than in control groups (P < 0.05, -0.007 +/- 0.03 versus 0.005 +/- 0.03). Finally, linear discrimination analysis (LDA) was used to classify the subjects. The classification was done using the robust leaving-one-subject-out method. LDA could classify the subjects with 71.7% sensitivity and 70.2% specificity while the male subjects resulted in a highly acceptable result for the sensitivity (81%). The present study showed that PPG signals can be used for vascular function assessment and may find further application for detection of vascular changes before onset of clinical diseases.
In this paper, an algorithm based on independent component analysis (ICA) for extracting the fetal heart rate (FHR) from maternal abdominal electrodes is presented. Three abdominal ECG channels are used to extract the FHR in three steps: first preprocessing procedures such as DC cancellation and low-pass filtering are applied to remove noise. Then the algorithm for multiple unknown source extraction (AMUSE) algorithm is fed to extract the sources from the observation signals include fetal ECG (FECG). Finally, FHR is extracted from FECG. The method is shown to be capable of completely revealing FECG R-peaks from observation leads even with a SNR=-200dB using semi-synthetic data.
Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.
An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a +/- 5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the time.
The feasibility of a novel system to reliably estimate the normalized central blood pressure (CBPN) from the radial photoplethysmogram (PPG) is investigated. Right-wrist radial blood pressure and left-wrist PPG were simultaneously recorded in five different days. An industry-standard applanation tonometer was employed for recording radial blood pressure. The CBP waveform was amplitude-normalized to determine CBPN. A total of fifteen second-order autoregressive models with exogenous input were investigated using system identification techniques. Among these 15 models, the model producing the lowest coefficient of variation (CV) of the fitness during the five days was selected as the reference model. Results show that the proposed model is able to faithfully reproduce CBPN (mean fitness = 85.2% ± 2.5%) from the radial PPG for all 15 segments during the five recording days. The low CV value of 3.35% suggests a stable model valid for different recording days.