Signal processing techniques for cardiovascular monitoring applications using conventional and video-based photoplethysmography

Photoplethysmography (PPG)-based monitoring devices will probably play a decisive role in healthcare environment of the future, which will be preventive, predictive, personalized and participatory. Indeed, this optical technology presents several practical advantages over gold standard methods based on electrocardiography, because PPG wearable devices can be comfortably used for long-term continuous monitoring during daily life activities. Contactless video-based PPG technique, also known as imaging photoplethysmography (iPPG), has also attracted much attention recently. In that case, the cardiac pulse is remotely measured from the subtle skin color changes resulting from the blood circulation, using a simple video camera. PPG/iPPG have a lot of potential for a wide range of cardiovascular applications. Hence, there is a substantial need for signal processing techniques to explore these applications and to improve the reliability of the PPG/iPPG-based parameters. \par A part of the thesis is dedicated to the development of robust processing schemes to estimate heart rate from the PPG/iPPG signals. The proposed approaches were built on adaptive frequency tracking algorithms that were previously developed in our group. These tools, based on adaptive band-pass filters, provide instantaneous frequency estimates of the input signal(s) with a very low time delay, making them suitable for real-time applications. In case of conventional PPG, a prior adaptive noise cancellation step involving the use of accelerometer signals was also necessary to reconstruct clean PPG signals during the regions corrupted by motion artifacts. Regarding iPPG, after comparing different regions of interest on the subject face, we hypothesized that the simultaneous use of different iPPG signal derivation methods (i.e. methods to derive the iPPG time series from the pixel values of the consecutive frames) could be advantageous. Methods to assess signal quality online and to incorporate it into instantaneous frequency estimation were also examined and successfully applied to improve system reliability. \par This thesis also explored different innovative applications involving PPG/iPPG signals. The detection of atrial fibrillation was studied. Novel features derived directly from the PPG waveforms, designed to reflect the morphological changes observed during arrhythmic episodes, were proposed and proven to be successful for atrial fibrillation detection. Arrhythmia detection and robust heart rate estimation approaches were combined in another study aimed at reducing the number of false arrhythmia alarms in the intensive care unit by exploiting signals from independent sources, including PPG. Evaluation on a hidden dataset demonstrated that the number of false alarms was drastically reduced while almost no true alarm was suppressed. Finally, other aspects of the iPPG technology were examined, such as the measurement of pulse rate variability indexes from the iPPG signals and the estimation of respiratory rate from the iPPG interbeat intervals

Real-Time Respiratory Rate Estimation using Imaging Photoplethysmography Inter-Beat Intervals

Imaging photoplethysmography (iPPG) has emerged as a contactless heart-rate monitoring technique. As the respiratory activity modulates the heart rate, we investigate the accuracy of iPPG in conveying the inter-beat variation due to the respiratory modulation of the heart rate. The instantaneous respiratory rate was estimated in real-time from the iPPG inter-beat variations with an algorithm based on a bank of short FIR notch filters. The comparison of the iPPG-based respiratory rate estimates to ECG-based estimates showed that the iPPG ones were only slightly less accurate in spite of the challenging conditions related to this contacless technique

False arrhythmia alarms reduction in the intensive care unit: a multimodal approach

The purpose of this study was to develop algorithms to lower the incidence of false arrhythmia alarms in the ICU using information from independent sources, namely electrocardiogram (ECG), arterial blood pressure (ABP) and photoplethysmogram (PPG). Our approach relies on robust adaptive signal processing techniques in order to extract accurate heart rate (HR) values from the different waveforms. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we developed a supplementary measure based on the spectral purity of the ECGs to determine whether a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on HR and spectral purity values. The proposed method was evaluated on the PhysioNet/CinC Challenge 2015 database, which is composed of 1250 life-threatening alarm recordings, each categorized into either bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. This resulted in overall true positive rates of 95%/99% and overall true negative rates of 76%/80% on the real-time and retrospective subsets of the test dataset, respectively

A Multimodal Approach to Reduce False Arrhythmia Alarms in the Intensive Care Unit

As part of the 2015 PhysioNet/CinC Challenge, this work aims at lowering the number of false alarms, which are a persistent concern in the intensive care unit. The multimodal database consists of 1250 life-threatening alarm recordings, each categorized as a bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms (photoplethysmogram and/or arterial blood pressure) using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we introduced a supplementary measure based on the spectral purity of the ECGs to determine if a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on heart rate and spectral purity values. Our method achieved overall scores of 76.11 and 85.04 on the real-time and retrospective subsets, respectively

Imaging Photoplethysmography: What are the Best Locations on the Face to Estimate Heart Rate?

The potential of imaging photoplethysmography for cardiovascular monitoring applications has been demonstrated recently. Various processing schemes have been proposed to extract heart rate (HR) from a defined region of interest (ROI) on the face. However, the reasons that motivate the choice of the ROI are often unclear. This study aimed at investigating the spatial distribution of the HR-related information on the subject face, based on a partition into 260 small ROIs. A power spectral density (PSD) analysis was performed to determine the amount of HR-related information in each ROI. Normalized color maps were used to visualize the spatial distribution of the HR-information and clearly showed that color fluctuations due to blood volume changes are always more pronounced in the forehead region. After face segmentation, for the R/G/B/NIR channels, average percentages of power of 27%/43%/27%/36% for the forehead region, 17%/28%/18%/21% for the cheek region and 16%/24%/16%/20% for the whole face were obtained

Real-Time Approaches for Heart Rate Monitoring using Imaging Photoplethysmography

Imaging photoplethysmography (iPPG) has gained a lot of popularity as a contactless heart rate (HR) monitoring technique. However, most of the existing approaches to estimate HR are based on block-wise processing schemes, which are not optimal for real-time applications. The aim of this study was to investigate robust HR estimation methods having a short estimation delay, which would be suitable for real-time HR monitoring applications using iPPG. The three following algorithms were evaluated: 1) an algorithm based on adaptive sliding-window singular value decomposition (SWASVD), 2) an adaptive band-pass filter (OSC-ANF-W), 3) a notch-filter bank (NFB) estimation method. The database used to evaluate these algorithms was composed of 46 records, acquired in the light using an RGB camera or the dark using an NIR camera. The subjects were asked to perform different tasks to induce HR fluctuations. For the visible/dark sequences, average absolute errors (AAE) of 3.42/5.25, 3.14/4.21 and 3.98/6.02 bpm were obtained for the SWASVD, the OSC-ANF-W and the NFB algorithms, respectively. The corresponding averaged estimation delays were 4 seconds for the SWASVD and OSC-ANF-W, and 3 seconds for the NFB

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively

An Adaptive Organization Index to Characterize Atrial Fibrillation using Wrist-Type Photoplethysmographic Signals

The performance of photoplethysmography (PPG)- based wearable monitors to diagnose atrial fibrillation (AF) remains unknown to date. This study aims at assessing the performance of new indices quantifying the level of organization in PPG signals to diagnose AF. A database made of 18 adult patients undergoing catheter ablation of various cardiac arrhythmias was used. PPG signals were recorded using a wrist-type sensor. A 12-lead ECG was used as gold standard. ECGs were annotated by experts and selected segments were divided into 4 categories: sinus rhythm (SR), regularly paced rhythm (RPR), irregularly paced rhythm (IPR) and AF. The level of organization of the various PPG signals was measured using an adaptive organization index (AOI), defined as the ratio of the power of the fundamental frequency and the first harmonic to the total power of the PPG signal, computed with adaptive band-pass filters. A total of 2806/803/852/287 10-second epochs were considered for AF/SR/RPR/IPR classes. The following mean AOI values were measured: 0.45±0.11 for AF, 0.73±0.19 for SR, 0.78±0.20 for RPR and 0.610.19 for IPR classes. Importantly, the AF AOI was significantly smaller than that of the other categories (p<0.001), indicating a higher degree of disorganization