Signal Processing Approaches for Cardio-Respiratory Biosignals with an Emphasis on Mobile Health Applications

We humans are constantly preoccupied with our health and physiological status. From precise measurements such as the 12-lead electrocardiograms recorded in hospitals, we have moved on to mobile acquisition devices, now as versatile as smart-watches and smart-phones. Established signal processing techniques do not cater to the particularities of mobile biomedical health monitoring applications. Moreover, although our capabilities to acquire data are growing, many underlying physiological phenomena remain poorly understood. This thesis focuses on two aspects of biomedical signal processing. First, we investigate the physiological basis of the relationship between cardiac and breathing biosignals. Second, we propose a methodology to understand and use this relationship in health monitoring applications. Part I of this dissertation examines the physiological background of the cardio-respiratory relationship and indexes based on this relationship. We propose a methodology to extract the respiratory sinus arrhythmia (RSA), which is an important aspect of this relationship. Furthermore, we propose novel indexes incorporating dynamics of the cardio-respiratory relationship, using the RSA and the phase lag between RSA and breathing. We then evaluate, systematically, existing and novel indexes under known autonomic stimuli. We demonstrate our indexes to be viable additions to the existing ones, thanks to their performance and physiological merits. Part II focuses on real-time and instantaneous methods for the estimation of the breathing parameters from cardiac activity, which is an important application of the cardio-respiratory relationship. The breathing rate is estimated from electrocardiogram and imaging photoplethysmogram recordings, using two dedicated filtering schemes, one of which is novel. Our algorithm measures this important vital rhythm in a truly real-time manner, with significantly shorter delays than existing methods. Furthermore, we identify situations, in which an important assumption regarding the estimation of breathing parameters from cardiac activity does not hold, and draw a road-map to overcome this problem. In Part III, we use indexes and methodology developed in Parts I and II in two applications for mobile health monitoring, namely, emotion recognition and sleep apnea detection from cardiac and breathing biosignals. Results on challenging datasets show that the cardio-respiratory indexes introduced in the present thesis, especially those related to the phase lag between RSA and breathing, are successful for emotion recognition and sleep apnea detection. The novel indexes reveal to be complementary to previous ones, and bring additional insight into the physiological basis of emotions and apnea episodes. To summarize, the techniques proposed in this thesis help to bypass shortcomings of previous approaches in the understanding and the estimation of cardio-respiratory coupling in real-life mobile health monitoring

Respiratory Rate Estimation from Multi-Lead ECGs using an Adaptive Frequency Tracking Algorithm

Estimating the respiratory rate (RR) from the electrocardiogram (ECG) is of interest as the direct measurement of the respiration in clinical situations is often cumbersome. In this study, the RR was estimated from the multi-lead ECG R-peak amplitude (RPA) waveforms, which contain the modulation of the cardiac activity by the respiration. An adaptive oscillator-based frequency tracking algorithm was used to estimate the RR from the RPAs of two or three ECG leads. This automatic and instantaneous method tracks the common respiratory frequency which is present in its inputs as the RR estimate. On a subset of the Physionet MFH/MF dataset, it was shown that combining information from three leads yielded more accurate RR estimates than using two leads or each lead alone. It was also shown that the frequency tracking algorithm outperformed Fourier-based frequency estimation

Estimating the real-time respiratory rate from the ECG with a bank of notch filters

The respiratory rate is an important vital sign that needs to be monitored continuously in clinical and non-clinical health monitoring applications. It is commonly estimated from electrocardiogram (ECG)-derived respiratory waveforms such as the respiratory sinus arrhythmia~(RSA) and the ECG R peak amplitude~(RPA). Current methods combine respiratory information from these two waveforms but produce large delays in estimating the respiratory rate. In this work, the power of the outputs of a bank of order-3 FIR notch filters were used in an adaptive scheme to estimate in a real-time manner and with minimal delay the respiratory rate from the RSA and the RPA waveforms simultaneously. The algorithm was tested on the public Physionet Fantasia data set and compared to the state-of-the-art in terms of estimation accuracy and delay. It was shown that the proposed method provides more accurate estimates with smaller delays than those of the state-of-the-art

Cardio-Respiratory Characterization of the Autonomic Balance

The autonomic balance is often measured using the low frequency~(LF) and high frequency~(HF) powers of the heart beat-to-beat intervals. However, these indices do not adequately integrate the influence of respiration and have been widely criticized. We studied the autonomic balance with measures from the heart beat-to-beat intervals taking into account the respiratory activity. Using cardiac and respiratory data acquired simultaneously from healthy volunteers in supine and orthostatic positions, we found that the investigated measures convey changes in the autonomic balance in a physiologically meaningful manner in contrast to the classic LF and HF indices

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

Palm vein recognition with local texture patterns

Biometric recognition using the palm vein characteristics is emerging as a touchless and spoof-resistant hand-based means to identify individuals or to verify their identity. One of the open challenges in this field is the creation of fast and modality-dependent feature extractors for recognition. This article investigates features using local texture description methods. The local binary pattern (LBP) operator as well as the local derivative pattern (LDP) operator and the fusion of the two are studied in order to create efficient descriptors for palm vein recognition by systematically adapting their parameters to fit palm vein structures. Results of experiments are reported on the CASIA multi-spectral palm print image database V1.0 (CASIA database). It is found that the local texture patterns proposed in this study can be adapted to the vein description task for biometric recognition and that the LDP operator consistently outperforms the LBP operator in palm vein recognition

Respiratory rate estimation from the ECG using an instantaneous frequency tracking algorithm

Monitoring the respiratory rate (RR) is important in many clinical and non-clinical situations but it is difficult in practice, for existing devices are obtrusive, bulky and expensive. The extraction of the RR from the routinely acquired electrocardiogram (ECG) has been proposed lately. Two approaches exist, one exploiting the modulation of the heart rate by the respiration, known as the respiratory sinus arrhythmia (RSA) and the other using the modulation by the respiration of the R-peak amplitudes (RPA). In this study, the weighted multi-signal oscillator based band pass filtering (W-OSC) algorithm is applied to track the common frequency in the RSA and RPA waveforms simultaneously, as an estimate of the instantaneous RR. On the public PhysioNet Fantasia data set, it is shown that the presented method is automatic, instantaneous and comparable in accuracy to the state-of-the-art. (C) 2014 Elsevier Ltd. All rights reserved

Real-time multi-signal frequency tracking with a bank of notch filters to estimate the respiratory rate from the ECG

Measuring the instantaneous frequency of a signal rapidly and accurately is essential in many applications. However, the instantaneous frequency by definition is a parameter difficult to determine. Fourier-based methods introduce estimation delays as computations are performed in a time-window. Instantaneous methods based on the Hilbert transform lack robustness. State-of-the-art adaptive filters yield accurate estimates, however, with an adaptation delay. In this study we propose an algorithm based on short length-3 FIR notch filters to estimate the instantaneous frequency of a signal at each sample, in a real-time manner and with very low delay. The output powers of a bank of the above-mentioned filters are used in a recursive weighting scheme to estimate the dominant frequency of the input. This scheme has been extended to process multiple inputs containing a common frequency by introducing an additional weighting scheme upon the inputs. The algorithm was tested on synthetic data and then evaluated on real biomedical data, i.e. the estimation of the respiratory rate from the electrocardiogram. It was shown that the proposed method provided more accurate estimates with less delay than those of state-of-the-art methods. By virtue of its simplicity and good performance, the proposed method is a worthy candidate to be used in biomedical applications, for example in health monitoring developments based on portable and automatic devices