scholarly journals Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation

2016 ◽  
Vol 2 (1) ◽  
pp. 241-245 ◽  
Author(s):  
Fabian Schrumpf ◽  
Matthias Sturm ◽  
Gerold Bausch ◽  
Mirco Fuchs
Keyword(s):  
Respiratory Rate ◽  
Autocorrelation Function ◽  
Real World ◽  
Fourier Transformation ◽  
Breathing Frequency ◽  
Zero Crossing ◽  
Ecg Signals ◽  
Respiration Rates ◽  
Respiratory Signal ◽  
Electrocardiogram Ecg

AbstractThe estimation of respiratory rates from contineous respiratory signals is commonly done using either fourier transformation or the zero-crossing method. This paper introduces another method which is based on the autocorrelation function of the respiratory signal. The respiratory signals can be measured either directly using a flow sensor or chest strap or indirectly on the basis of the electrocardiogram (ECG). We compare our method against other established methods on the basis of real-world ECG signals and use a respiration-based breathing frequency as a reference. Our method achieved the best agreement between respiration rates derived from directly and indirectly measured respiratory signals.

scholarly journals Respiratory Rate Estimation Based on Spectrum Decomposition

2018 ◽  
Author(s):  
SeungJae Lee ◽  
Soo-Yong Kim
Keyword(s):  
Respiratory Rate ◽  
Mean Squared Error ◽  
Physiological State ◽  
Singular Spectrum Analysis ◽  
Estimation Methods ◽  
Ecg Signals ◽  
Squared Error ◽  
Transthoracic Impedance ◽  
Electrocardiogram Ecg ◽  
Spectrum Decomposition

We propose an electrocardiogram (ECG) signal-based algorithm to estimate the respiratory rate is a significant informative indicator of physiological state of a patient. The consecutive ECG signals reflect the information about the respiration because inhalation and exhalation make transthoracic impedance vary. The proposed algorithm extracts the respiration-related signal by finding out the commonality between the frequency and amplitude features in the ECG pulse train. The respiration rate can be calculated from the principle components after the procedure of the singular spectrum analysis. We achieved 1.7569 breaths per min of root-mean-squared error and 1.7517 of standard deviation with a 32-seconds signal window of the Capnobase dataset, which gives notable improvement compared with the conventional Autoregressive model based estimation methods.

scholarly journals Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments

2015 ◽  
Vol 1 (1) ◽  
pp. 61-64 ◽  
Author(s):  
Marcus Schmidt ◽  
Johannes W Krug ◽  
Andy Schumann ◽  
Karl-Jürgen Bär ◽  
Georg Rose
Keyword(s):  
Respiratory Activity ◽  
Vital Signs ◽  
Daily Routine ◽  
Impedance Change ◽  
Resonance Imaging ◽  
Central Moments ◽  
Ecg Signals ◽  
Respiratory Signal ◽  
Magnetic Resonance Imaging Mri ◽  
Electrocardiogram Ecg

AbstractFor a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.

scholarly journals Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters

2021 ◽  
Vol 3 (1) ◽  
pp. 34-38
Author(s):  
Gede Aditya Mahendra Oka ◽  
Andjar Pudji
Keyword(s):  
Respiration Rate ◽  
Vital Sign ◽  
Critical Period ◽  
Heart Activity ◽  
Electrical Behavior ◽  
Ecg Signals ◽  
Physiological Signal ◽  
Respiration Rates ◽  
Pulse Form ◽  
Electrocardiogram Ecg

Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.ECG, Respiration Rate, Filter

scholarly journals Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249843
Author(s):  
Stephanie Baker ◽  
Wei Xiang ◽  
Ian Atkinson
Keyword(s):  
Respiratory Rate ◽  
Quality Index ◽  
Absolute Error ◽  
Healthcare Applications ◽  
Non Invasive ◽  
Electrocardiogram Signals ◽  
Respiratory Signal ◽  
Clinical Environments ◽  
Electrocardiogram Ecg

Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the use of respiratory signal quality quantification and several neural network (NN) structures for improved RR estimation. We extract respiratory modulation signals from the electrocardiogram (ECG) and photoplethysmogram (PPG) signals, and calculate a possible RR from each extracted signal. We develop a straightforward and efficient respiratory quality index (RQI) scheme that determines the quality of each moonddulation-extracted respiration signal. We then develop NNs for the estimation of RR, using estimated RRs and their corresponding quality index as input features. We determine that calculating RQIs for modulation-extracted RRs decreased the mean absolute error (MAE) of our NNs by up to 38.17%. When trained and tested using 60-sec waveform segments, the proposed scheme achieved an MAE of 0.638 breaths per minute. Based on these results, our scheme could be readily implemented into non-invasive wearable devices for continuous RR measurement in many healthcare applications.

EXTRACTING RESPIRATION RATE FROM RAW ECG SIGNALS

2010 ◽  
Vol 22 (04) ◽  
pp. 307-314 ◽  
Author(s):  
Richard Singhathip ◽  
Si-Hui Yang ◽  
Maysam Abbod ◽  
Rong-Guan Yeh ◽  
Jiann-Shing Shieh
Keyword(s):  
Respiration Rate ◽  
Strain Gauge ◽  
Simple Algorithm ◽  
Normal Subjects ◽  
Impedance Plethysmography ◽  
Special Device ◽  
Ecg Signals ◽  
Respiration Rates ◽  
Electrocardiogram Ecg ◽  
Additional Hardware

There exists several techniques for monitoring respiration (spirometers, nasal thermocouples, transthoracic inductance, impedance plethysmography, strain gauge etc.), but each of these techniques requires a special device. The method proposed in this paper calculates the intervals of respiration rates extracted from ordinary electrocardiogram (ECG) signals. The result obtained shows clearly that each respiration cycle can be represented by a maxima. The simple algorithm proposed in this study is used to count the respiration times. The method has been tested on 26 voluntary subjects in three different conditions (normal, controlled breath to simulate apnea, and during sleep). Moreover, the respiration times obtained have been compared with the respiration rate acquired by Philips Physiological Monitor (MP60). The method proposed in this paper for normal subjects have an accuracy that can reach over 96%. However, for apnea subject, the proposed methods can reach over 93% accuracy. In conclusion, this technique is applicable to any type of automated ECG analysis, in real time and without the need of additional hardware.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanfei Yang ◽  
Mingzhu Xu ◽  
Aimin Liang ◽  
Yan Yin ◽  
Xin Ma ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Data Acquisition ◽  
Current Distribution ◽  
Spin Exchange ◽  
Healthy Human ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Magnetometer Array ◽  
Data Acquisition And Processing ◽  
Electrocardiogram Ecg

AbstractIn this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

scholarly journals Novel Stable Capacitive Electrocardiogram Measurement System

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3668
Author(s):  
Chi-Chun Chen ◽  
Shu-Yu Lin ◽  
Wen-Ying Chang
Keyword(s):  
Measurement System ◽  
Input Resistance ◽  
Voltage Divider ◽  
Motion Artifact ◽  
Guard Ring ◽  
Capacitive Sensing ◽  
Ecg Signals ◽  
Optimal Input ◽  
Electrocardiogram Ecg ◽  
Mode Interference

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

scholarly journals Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

2021 ◽  
Vol 11 (3) ◽  
pp. 1125
Author(s):  
Htet Myet Lynn ◽  
Pankoo Kim ◽  
Sung Bum Pan
Keyword(s):  
Short Term Memory ◽  
Feature Learning ◽  
Biometric Authentication ◽  
Qrs Complex ◽  
Data Types ◽  
Ecg Signals ◽  
Data Independent Acquisition ◽  
Deep Networks ◽  
Classification Tasks ◽  
Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

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