PPG Derived Respiratory Rate Estimation in Daily living Conditions.

Respiratory rate (RR) derived from photoplethysmogram (PPG) during daily activities can be corrupted due to movement and other artefacts. We have investigated the use of ensemble empirical mode decomposition (EEMD) based smart fusion approach for improving the RR extraction from PPG. PPG was recorded while subjects performed five different activities: sitting, standing, climbing and descending stairs, walking, and running.

Sleep-Wake Classification using Statistical Features Extracted from Photoplethysmographic Signals.

Sleep quality has a significant impact on human mental and physical health. Detecting sleep-wake stages is of paramount importance in the study of sleep. The gold standard method for sleep-wake stages classification is the multi-sensors based polysomnography (PSG) systems, which is normally recorded in clinical settings.

The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG.

Non-invasive fetal electrocardiography (NI-FECG) is an emerging technology that demonstrates potential for providing novel physiological information compared to traditional ultrasound-based cardiotocography (CTG). However, few studies have investigated the reliability of signal features derived via this technique for diagnostic use. One feature of NI-FECG recordings proposed for the purpose of identifying fetal distress is the T/QRS ratio, which has been indicated to change in response to fetal hypoxia.

Robust Heart Rate Estimation During Physical Exercise Using Photoplethysmographic Signals.

A method for estimating heart rate (HR) from photoplethysmographic (PPG) signal, during physical exercise, is presented in this paper. Accurate and reliable estimation of HR from PPG during intensive physical activity is challenging because intense motion artifacts can easily mask the true HR. If PPG signal is contaminated by intense motion artifacts, the highest peak of PPG spectrum is shifted from true HR due to motion artifacts.

The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction.

Objective: Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, significant deterioration in NI-FECG signal quality occurs during the presence of a highly non-conductive layer known as vernix caseosa which forms on the fetal body surface beginning in approximately the 28th week of gestation. This work investigates asymmetric modeling of vernix caseosa and other maternal-fetal tissues in accordance with clinical observations and assesses their impacts for NI-FECG signal processing.

Multi-lag tone-entropy in neonatal stress.

Heart rate variability (HRV) has been analysed using linear and nonlinear methods. In the framework of a controlled neonatal stress model, we applied tone-entropy (T-E) analysis at multiple lags to understand the influence of external stressors on healthy term neonates. Forty term neonates were included in the study.

Modified thresholding technique of MMSPCA for extracting respiratory activity from short length PPG signal.

In this paper, we propose an automatic threshold selection of modified multi scale principal component analysis (MMSPCA) for reliable extraction of respiratory activity (RA) from short length photoplethysmographic (PPG) signals. MMSPCA was applied to the PPG signal with a varying data length, from 30 seconds to 60 seconds, to extract the respiratory activity. To examine the performance, we used 100 epochs of simultaneously recorded PPG and respiratory signals extracted from the MIMIC database (Physionet ATM data bank).

Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal.

The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration, and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal.

An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal.

The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal.