200 research outputs found
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
Comparison Of Two Methods For Demodulation Of Pulse Signals - Application In Case Of Central Sleep Apnea
In the field of 24/7 human health monitoring, pervasive computing makes possible the continuous analysis of physiological parameters from an ambulatory device with a great acceptability. This paper presents two methods for obtaining cardiac and respiratory rates from a single arterial pressure signal: AM-FM demodulation and Singular Spectrum Analysis (SSA). With the aim to monitor sleep apnea, two simulated central sleep apnea were performed and recorded with Biopac reference system. The results showed a good evaluation of the cardiac rate with Singular Spectrum Analysis and bad results with AM-FM demodulation. For the respiration rate, some other signals were tested with average results for both methods. Further experiments will deal with real sleep apnea cases and algorithm improvements
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A New Framework to Estimate Breathing Rate from Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals
Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical settings. However, it is still widely measured manually. In this paper, a novel framework is proposed to estimate the BR from an electrocardiogram (ECG), a photoplethysmogram (PPG), or a blood pressure (BP) signal. The framework uses Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods to extract respiratory signals, taking advantage of both time and frequency domain
information. An Extended Kalman Filter (EKF), incorporating a Signal Quality Index (SQI), enabled our method to achieve acceptable performance even for significantly distorted periods of the signals. Using
state vector fusion, the output signals are combined and finally the BR is estimated. The framework was tested on two publicly available clinical databases: the MIT-BIH Polysomnographic and BIDMC databases.
Performance was evaluated using the mean absolute percentage error (MAPE). The results indicated high accuracy: MAPEs on the two databases of 3.9% and 3.6% for ECG signals, 6.0% for PPG, and 5.0% for BP signals. The results also indicated high robustness to noise down to 0dB. Therefore, this framework may have utility for BR monitoring in high noise settings
Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography
Respiratory rate (RR) is a useful vital sign that can not only provide auxiliary information on physiological changes within the human body, but also indicate early symptoms of various diseases. Recently, methods for the estimation of RR from photoplethysmography (PPG) have attracted increased interest, because PPG can be readily recorded using wearable sensors such as smart watches and smart bands. In the present study, we propose a new method for the fast and robust real-time estimation of RR using an adaptive infinite impulse response (IIR) notch filter, which has not yet been applied to the PPG-based estimation of RR. In our offline simulation study, the performance of the proposed method was compared to that of recently developed RR estimation methods called an adaptive lattice-type RR estimator and a Smart Fusion. The results of the simulation study show that the proposed method could not only estimate RR more quickly and more accurately than the conventional methods, but also is most suitable for online RR monitoring systems, as it does not use any overlapping moving windows that require increased computational costs. In order to demonstrate the practical applicability of the proposed method, an online RR estimation system was implemented.This research was supported by the National Research Foundation of Korea (NRF) grants funded by the Ministry of Science, ICT & Future Planning (MSIP) (NRF-2015M3C7A1065052 and 2015R1A2A1A15054662)
Heart Rate Monitoring During Different Lung Volume Phases Using Seismocardiography
Seismocardiography (SCG) is a non-invasive method that can be used for
cardiac activity monitoring. This paper presents a new electrocardiogram (ECG)
independent approach for estimating heart rate (HR) during low and high lung
volume (LLV and HLV, respectively) phases using SCG signals. In this study,
SCG, ECG, and respiratory flow rate (RFR) signals were measured simultaneously
in 7 healthy subjects. The lung volume information was calculated from the RFR
and was used to group the SCG events into low and high lung-volume groups. LLV
and HLV SCG events were then used to estimate the subjects HR as well as the HR
during LLV and HLV in 3 different postural positions, namely supine, 45 degree
heads-up, and sitting. The performance of the proposed algorithm was tested
against the standard ECG measurements. Results showed that the HR estimations
from the SCG and ECG signals were in a good agreement (bias of 0.08 bpm). All
subjects were found to have a higher HR during HLV (HR) compared
to LLV (HR) at all postural positions. The
HR/HR ratio was 1.110.07, 1.080.05,
1.090.04, and 1.090.04 (meanSD) for supine, 45 degree-first
trial, 45 degree-second trial, and sitting positions, respectively. This heart
rate variability may be due, at least in part, to the well-known respiratory
sinus arrhythmia. HR monitoring from SCG signals might be used in different
clinical applications including wearable cardiac monitoring systems
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
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