Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice

Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach

Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to leverage this fact, by employing a novel deep learning framework which is a based on a repurposed convolutional autoencoder. Our model aims to encode all of the relevant respiratory information contained within photoplethysmography waveform, and decode it into a waveform that is similar to a gold standard respiratory reference. The model is employed on two photoplethysmography data sets, namely Capnobase and BIDMC. We show that the model is capable of producing respiratory waveforms that approach the gold standard, while in turn producing state of the art respiratory rate estimates. We also show that when it comes to capturing more advanced respiratory waveform characteristics such as duty cycle, our model is for the most part unsuccessful. A suggested reason for this, in light of a previous study on in-ear PPG, is that the respiratory variations in finger-PPG are far weaker compared with other recording locations. Importantly, our model can perform these waveform estimates in a fraction of a millisecond, giving it the capacity to produce over 6 hours of respiratory waveforms in a single second. Moreover, we attempt to interpret the behaviour of the kernel weights within the model, showing that in part our model intuitively selects different breathing frequencies. The model proposed in this work could help to improve the usefulness of consumer PPG-based wearables for medical applications, where detailed respiratory information is required

Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning

Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG signals provide insight into the body's circulatory system and can be employed to extract various bio-features, such as heart rate and vascular ageing. Although several algorithms have been proposed for this purpose, many exhibit limitations, including heavy reliance on human calibration, high signal quality requirements, and a lack of generalisation. In this paper, we introduce a PPG signal processing framework that integrates graph theory and computer vision algorithms, to provide an analysis framework which is amplitude-independent and invariant to affine transformations. It also requires minimal preprocessing, fuses information through RGB channels and exhibits robust generalisation across tasks and datasets. The proposed VGTL-net achieves state-of-the-art performance in the prediction of vascular ageing and demonstrates robust estimation of continuous blood pressure waveforms

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces

An algorithm for heart rate extraction from acoustic recordings at the neck

Heart rate is an important physiological parameter to assess the cardiac condition of an individual and is traditionally determined by attaching multiple electrodes on the chest of a subject to record the electrical activity of the heart. The installation and handling complexities of such systems does not prove feasible for a user to undergo a long-term monitoring in the home settings. A small-sized, battery-operated wearable monitoring device is placed on the suprasternal notch at neck to record acoustic signals containing information about breathing and cardiac sounds. The heart sounds obtained are heavily corrupted by the respiratory cycles and other external artifacts. This paper presents a novel algorithm for reliably extracting the heart rate from such acoustic recordings, keeping in mind the constraints posed by the wearable technology. The methodology constructs the Hilbert energy envelope of the signal by calculating its instantaneous characteristics to segment and classify a cardiac cycle into S1 and S2 sounds using their timing characteristics. The algorithm is tested on a dataset consisting of 13 subjects with an approximate data length of 75 hours and achieves an accuracy of 94.34%, an RMS error of 3.96 bpm and a correlation coefficient of 0.93 with reference to a commercial device in use

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