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

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Online Apnea-Bradycardia Detection Using Recursive Order Estimation for Auto-Regressive Models.

International audienceThis study aims to detect apnea-bradycardia (AB) episodes from preterm newborns, based on the analysis of electrocardiographic signals (ECG). We propose the use of an auto-regressive (AR) model with undetermined orders to capture all possible linear dependency of the RR interval time series extracted from ECG. An on-line algorithm inspired from the Kalman filtering technique is designed to follow the evolution of the AR model's order distribution. The detection sensitivity (TP/(TP + FN)) reaches 91:5% over a total of 50 episodes with perfect specificity (TN/(FP+TN)=100%). From the clinical point of view, it is essential to achieve reliable early stage detection of AB episodes to enable the initiation of quick nursing actions. Our proposed method achieves a delay of 5:08s 2:90 compared with the experts' off-line annotations, knowing that the mean intervention time (duration from the generation of the alarm to the initiation of manual stimulation) is reported to be 33 seconds from a recent study [5]

TIME SERIES ANALYSIS AND CLUSTERING TO CHARACTERIZE CARDIORESPIRATORY INSTABILITY PATTERNS IN STEP-DOWN UNIT PATIENTS

Background: Cardiorespiratory instability (CRI) in noninvasively monitored step-down unit (SDU) patients has a variety of etiologies, and therefore likely manifests in different patterns of vital signs (VS) changes. Objective: We sought to describe differences in admission characteristics and outcomes between patients with and without CRI. We explored use of clustering techniques to identify VS patterns within initial CRI epoch (CRI1) and assessed inter-cluster differences in admission characteristics, outcomes and medications. Methods: Admission characteristics and continuous monitoring data (frequency 1/20 Hz) were recorded in 307 patients. Vital sign (VS) deviations beyond local instability trigger criteria for 3 consecutive minutes or for 4 out of a 5 minute moving window were classified as CRI events. We identified CRI1 in 133 patients, derived statistical features of CRI1 epoch and employed hierarchical and k-means clustering techniques. We tested several clustering solutions and used 10-fold cross validation and ANOVA to establish best solution. Inter-cluster differences in admission characteristics, outcomes and medications were assessed. Main Results: Patients transferred to the SDU from units with higher monitoring capability were more likely to develop CRI (n=133, CRI 44% vs no CRI n=174, 31%, p=.042). Patients with at least one event of CRI had longer hospital length of stay (CRI 11.3 + 10.2 days vs no CRI 7.8 + 9.2, p=.001) and SDU unit stay (CRI 6.1 + 4.9 days vs no CRI 3.5 + 2.9, p< .001). Four main clusters(C) were derived. Clusters were significantly different based on age (p=0.001; younger patients in C1 and older in C2), number of comorbidities (p<0.01; more C2 patients had ≥2), and admission source (p=0.008; more C1 and C4 patients transferred in from a higher intensity monitoring unit). Patients with CRI differed significantly (p<.05) from those without CRI based on medication categories. Conclusions: CRI1 was associated with prolonged hospital and SDU length of stay. Patients transferred from a higher level of care were more likely to develop CRI, suggesting that they are sicker. Future study will be needed to determine if there are common physiologic underpinnings of VS clusters which might inform monitoring practices and clinical decision-making when CRI first manifests

Muscle activation patterns in shoulder impingement patients

Introduction: Shoulder impingement is one of the most common presentations of shoulder joint problems 1. It appears to be caused by a reduction in the sub-acromial space as the humerus abducts between 60o -120o – the 'painful arc'. Structures between the humeral head and the acromion are thus pinched causing pain and further pathology 2. Shoulder muscle activity can influence this joint space but it is unclear whether this is a cause or effect in impingement patients. This study aimed to observe muscle activation patterns in normal and impingement shoulder patients and determine if there were any significant differences. Method: 19 adult subjects were asked to perform shoulder abduction in their symptomatic arm and non-symptomatic. 10 of these subjects (age 47.9 ± 11.2) were screened for shoulder impingement, and 9 subjects (age 38.9 ± 14.3) had no history of shoulder pathology. Surface EMG was used to collect data for 6 shoulder muscles (Upper, middle and lower trapezius, serratus anterior, infraspinatus, middle deltoids) which was then filtered and fully rectified. Subjects performed 3 smooth unilateral abduction movements at a cadence of 16 beats of a metronome set at 60bpm, and the mean of their results was recorded. T-tests were used to indicate any statistical significance in the data sets. Significance was set at P<0.05. Results: There was a significant difference in muscle activation with serratus anterior in particular showing a very low level of activation throughout the range when compared to normal shoulder activation patterns (<30%). Middle deltoid recruitment was significantly reduced between 60-90o in the impingement group (30:58%).Trends were noted in other muscles with upper trapezius and infraspinatus activating more rapidly and erratically (63:25%; 60:27% respectively), and lower trapezius with less recruitment (13:30%) in the patient group, although these did not quite reach significance. Conclusion: There appears to be some interesting alterations in muscle recruitment patterns in impingement shoulder patients when compared against their own unaffected shoulders and the control group. In particular changes in scapula control (serratus anterior and trapezius) and lateral rotation (infraspinatus), which have direct influence on the sub-acromial space, should be noted. It is still not clear whether these alterations are causative or reactionary, but this finding gives a clear indication to the importance of addressing muscle reeducation as part of a rehabilitation programme in shoulder impingement patients

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page