Covariance Intersection to Improve the Robustness of the Photoplethysmogram Derived Respiratory Rate

Respiratory rate (RR) can be estimated from the photoplethysmogram (PPG) recorded by optical sensors in wearable devices. The fusion of estimates from different PPG features has lead to an increase in accuracy, but also reduced the numbers of available final estimates due to discarding of unreliable data. We propose a novel, tunable fusion algorithm using covariance intersection to estimate the RR from PPG (CIF). The algorithm is adaptive to the number of available feature estimates and takes each estimates' trustworthiness into account. In a benchmarking experiment using the CapnoBase dataset with reference RR from capnography, we compared the CIF against the state-of-the-art Smart Fusion (SF) algorithm. The median root mean square error was 1.4 breaths/min for the CIF and 1.8 breaths/min for the SF. The CIF significantly increased the retention rate distribution of all recordings from 0.46 to 0.90 (p $<$ 0.001). The agreement with the reference RR was high with a Pearson's correlation coefficient of 0.94, a bias of 0.3 breaths/min, and limits of agreement of -4.6 and 5.2 breaths/min. In addition, the algorithm was computationally efficient. Therefore, CIF could contribute to a more robust RR estimation from wearable PPG recordings.Comment: accepted to EMBC 202

Data Fusion for QRS Complex Detection in Multi-Lead Electrocardiogram Recordings

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps

Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.Municipal Science & Technology Commission. Beijing Natural Science Foundation (Grants 3102028 and 3122034)General Logistics Science Foundation (Grant CWS11C108)National Institutes of Health (U.S.) (National Institute of General Medical Sciences (U.S.). Grant R01- EB001659)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Cooperative Agreement U01- EB-008577

Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals

Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method

Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters.

GOAL: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. METHODS: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. RESULTS: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25[Formula: see text]-75[Formula: see text] percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). CONCLUSION: Increased robustness of RR estimation by the proposed method was demonstrated. SIGNIFICANCE: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available

An Advanced Method Fusion and an Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals

Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data

Effectiveness of Music-Based Respiratory Biofeedback in Reducing Stress during Visually Demanding Tasks

Biofeedback techniques have shown to be effective to manage stress and improve task performance. Biofeedback generally can be divided into two steps (i) measuring physiological functions (e.g. respiration, heart rate) via sensors and (ii) conveying the physiological signals to the user to improve self-awareness. Current systems require costly and invasive sensors to measure physiology, which are not comfortable and are not readily accessible to the general population. Additionally, current feedback mechanisms may be physically unpleasant or may hinder multitasking, especially in visually-demanding environments. To overcome these problems, we developed two tools: a music-based biofeedback tool that uses music as the medium of feedback, and a tool to measure breathing rate using a smartphone camera. The music biofeedback tool encourages slow breathing by adjusting the quality of the music in response to the user’s breathing rate. This intervention combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). We evaluate the intervention on a 2×2 design with music and auditory biofeedback as independent variables. Our results indicate that music-biofeedback leads to lower arousal (as measured by electrodermal activity and heart rate variability) than music alone, auditory biofeedback alone, and a control condition. Music biofeedback also reduces driving errors when compared to the other three conditions. While our results suggest that the music-based biofeedback tool is useful and enjoyable, it still requires expensive physiological sensors which are intrusive in nature. Hence, we present a second tool to measure breathing rate in real-time via smartphone camera, which makes it easily accessible given the pervasiveness of smartphones. Our algorithm measures breathing rate by obtaining the photoplethysmographic signal and performing spectral analysis using Goertzel algorithm. We validated the method under a range of controlled breathing rate conditions, and our results show a high degree of agreement between our estimates and ground truth measurements obtained via standard respiratory sensors. These results show that it is possible to accurately compute breathing rate in real-time using a smartphone