Cuff-less and continuous blood pressure measurement based on pulse transit time from carotid and toe photoplethysmograms.

Blood pressure (BP) is a vital health parameter that varies throughout the day. As a single reading of high BP may not indicate hypertension, continuous monitoring of BP is usually recommended by medical doctors to confirm the diagnosis of hypertension. In the last few decades, researchers have investigated cuff-less and continuous BP measurements based on pulse transit time (PTT). The main purpose of this research is to develop an autoregressive (ARX) system identification (SI)-based PTT calculation model using two PPG signals acquired from carotid and toe. The signals were recorded from 65 subjects with an age range between 20 and 60 years. The results of the study have been validated in two stages. The first validation comprised the estimated BP from PTT using SI compared to the measured BP using the cuff-based method for all subjects. The results of the estimated BP using the proposed method compared to the measured BP obtained using the standard BP cuff measurement method are highly correlated to both systolic blood pressure (R2 = 0.8132) and diastolic blood pressure (R2 = 0.8357). The second validation consisted of comparing PTT values using system identification to the results of the PTT derived from the ECG-PPG method. The results showed that both methods are highly correlated (R2 = 0.7808), and there is no significant difference between them (p < 0.05) with a slightly better PTT estimation related to DBP in the proposed method. Our results have proven that the PTT obtained from the carotid PPG and toe PPG using the system identification approach yielded SBP and DBP estimations that are consistent with the values of the conventional BP cuff method. The newly proposed method has the advantage of being cuff-less and able to provide continuous BP measurements.

Time-varying assessment of heart rate variability parameters using respiratory information.

Analysis of heart rate variability (HRV) is commonly used for characterization of autonomic nervous system. As high frequency (HF, known as the respiratory-related) component of HR, overlaps with the typical low frequency (LF) band when the respiratory rate is low, a reference signal for HF variations would help in better discriminating the LF and HF components of HR. The present study proposes a model for time-varying separation of HRV components as well as estimation of HRV parameters using respiration information. An autoregressive moving average with exogenous input (ARMAX) model of HRV is considered with a parametrically modeled respiration signal as the input. The model parameters are estimated using smoothed extended Kalman filtering. Results for different synthetic data show that our proposed joint model outperforms the classical AR modeling in estimation of HRV parameters especially in the case of low respiration rate. In addition, the possibility of using pulse transit time (PTT) and the amplitude of photoplethysmogram (PPGamp) as surrogates of the input respiratory signal has been investigated. To this end, electrocardiogram (ECG), PPG and respiration have been recorded from 21 healthy subjects (10 males and 11 females, mean age 27.5 ± 4.1) during normal and deep respiration. Results show that indeed PTT and PPGamp offer good potential to be used as references for respiratory-related variations of HR, thus avoiding additional devices for recording respiration.

Experimental feasibility study of estimation of the normalized central blood pressure waveform from radial photoplethysmogram.

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.

Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model.

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta band of AD subjects shows an increase in the power, whereas the alpha band shows a decreased activity (P-value <0.05). In the right brain hemisphere of AD subjects, a decreased activity is observed in all frequency bands. It was also noticed that the right temporal region shows a significant difference between the two groups in all frequency bands. Using a support vector machine, control and patient groups are discriminated with an accuracy of 84.4%, sensitivity 75.0%, and specificity of 93.7%.

Discrimination of bilateral finger photoplethysmogram responses to reactive hyperemia in diabetic and healthy subjects using a differential vascular model framework.

Endothelial dysfunction assessment has received considerable attention due to its potential in early screening of cardiovascular diseases. Since the seminal work by Celermajer in flow-mediated dilation (FMD) based on B-mode ultrasound measurement of the brachial artery dilation following limb ischemia, many attempts have been made toward applying this method to clinical, non-invasive endothelial dysfunction assessment. One major obstacle toward achieving this objective has been the relative high cost of the required setup and skilled manpower. Such limitations have prompted the investigation of other non-invasively accessible signals such as the photoplethysmogram (PPG) in relation to FMD. It is in the above context that this paper proposes to use a modified version of an existing differential model of the human upper vasculature in order to discriminate between healthy and diabetic subjects. PPG from 46 subjects (23 healthy and 23 diabetic) were utilized to identify the model parameters. Once the model parameters were identified, singular value decomposition was applied to reduce the number of features and increase the separability. Finally, a naive Bayes classifier resulted in an overall accuracy of 93.5% (Spec. 87.0% and Sens. 100%). Taking into account subjects' gender further improved the overall accuracy. It is thought that the application of the proposed method to endothelial dysfunction assessment may positively impact the deployment of FMD in clinical settings.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine.

BACKGROUND: 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'. METHODS: The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. RESULTS: For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. CONCLUSIONS: Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.

The dilemma of BME research projects in developing countries: a case study.

Researchers are faced with huge challenges when undertaking BME research projects in developing countries. Various administrative, technical, economic and even cultural barriers have to be overcome whereas the quality and quantity of the output has to be comparable with the developed world in order to make results publishable. This paper uses a real project context to highlight the major problems and the necessity of a holistic approach which would take into consideration all stakeholders interests. It is only by tackling problems such as relationship between academia-industry and administration efficiency at their root that significant progress can be achieved.

Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results.

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.

Classification of vascular function in upper limb using bilateral photoplethysmographic signals.

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.

Applicability of adaptive noise cancellation to fetal heart rate detection using photoplethysmography.

In this paper, an approach based on adaptive noise cancellation (ANC) is evaluated for extraction of the fetal heart rate using photoplethysmographic signals from the maternal abdomen. A simple optical model is proposed in which the maternal and fetal blood pulsations result in emulated signals where the lower SNR limit (fetal to maternal) is -25dB. It is shown that a recursive least-squares algorithm is capable of extracting the peaks of the fetal PPG from these signals, for typical values of maternal and fetal tissues.

Analysis of the effect of ageing on rising edge characteristics of the photoplethysmogram using a modified Windkessel model.

Ageing is one of the main contributing factors towards increasing arterial stiffness, leading to changes in peripheral pulses propagation. Therefore the characteristics of the photoplethysmogram (PPG) pulse, especially the rising edge and peak position, are greatly affected. In this study, the PPG pulse rising edge and corresponding peak position have been investigated non-invasively in human subjects as a function of age. Fifteen healthy subjects were selected and grouped in five age intervals, from 20 to 59 years, based on their comparable systolic-diastolic blood pressure and PPG amplitude. As expected, the peripheral pulse shows a steep rise and early peak in younger subjects. With age, the slope becomes blunted and in older subjects, the rise is very gradual and the pulse peak appears much later. Qualitative results were further verified by a modified 10-element Windkessel model to quantify the lumped parameter changes with ageing. This verification highlighted some specific changes in vascular parameters with aging. The rising edge could be considered as one parameter in determining the age-dependent vascular state.

Fetal heart rate monitoring based on independent component analysis.

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.

Parametric differential approach for modeling the upper limb human vasculature.

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.

Real-time signal processing for fetal heart rate monitoring.

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.

A comparison of abdominal ECG and Doppler ultrasound for fetal heart rate detection.

A fetal monitor has been developed for the measurement of the fetal and maternal heart rates from maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. Developed algorithm of the fetal monitor is based on digital filtering, adaptive thresholding. statistical properties in the time domain and differencing of local maxima and minima. Five volunteers with low risk pregnancies, between 35 to 40 weeks of gestation and no evidence of labor, were studied for the fetal heart rate detection. A Doppler ultrasound fetal monitor was used to compare the accuracy of the measurement system. Results showed an average percent rms difference (PRD) of 5.32% in comparison with the reference ultrasound method. The fetal heart rates curve remained inside a +/- 5 beats/min limit relative to the reference ultrasound method for 84.1% of the time.