scholarly journals Continuous signal quality estimation for robust heart rate extraction from photoplethysmographic signals

2020 ◽  
Vol 6 (3) ◽  
pp. 510-513
Author(s):  
Jonas Massmann ◽  
Timo Tigges ◽  
Reinhold Orglmeister
Keyword(s):  
Heart Rate ◽  
Pulse Rate ◽  
Quality Index ◽  
Reference Model ◽  
Feature Reduction ◽  
Signal Quality ◽  
Estimation Errors ◽  
Pulse Rate Variability ◽  
Continuous Signal ◽  
Signal Quality Index

AbstractThis study presents a novel method for estimating the signal quality of photoplethysmographic (PPG) signals. For this purpose a robust classifier is implemented and evaluated by using finger- and inear-PPG. A new procedure is proposed, which uses feature reduction to determine the Mahalanobis distance of the PPG-pulses to a statistical reference model and thus facilitates a robust heart rate extraction. The evaluation of the algorithm is based on a classical binary classification using a manually annotated gold standard. For the finger-PPG a sensitivity of 86 ± 15 % and a specificity of 94 ± 13 % was achieved. Additionally, a novel classification method which is based on a continuous signal quality index is used. Pulse rate estimation errors greater than 5 BPM can be detected with a sensitivity of 91 ± 13 % and a specificity of 91 ± 15 %. Also, a functional correlation between the signal quality index and the standard deviation of the pulse rate error is shown. The proposed classifier can be used for improving the heart rate extration in pulse rate variability analysis or in the area of mobile monitoring for battery saving.

scholarly journals Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1923 ◽  
Author(s):  
Ivan Liu ◽  
Shiguang Ni ◽  
Kaiping Peng
Keyword(s):  
Heart Rate ◽  
Health Monitoring ◽  
Quality Index ◽  
Signal Quality ◽  
Heart Attacks ◽  
Index Approach ◽  
Signal Quality Index ◽  
Potential Applications ◽  
Hrv Analysis ◽  
Sinusoidal Function

Heart rate variability (HRV) provides essential health information such as the risks of heart attacks and mental disorders. However, inconvenience related to the accurate detection of HRV limits its potential applications. The ubiquitous use of smartphones makes them an excellent choice for regular and portable health monitoring. Following this trend, smartphone photoplethysmography (PPG) has recently garnered prominence; however, the lack of robustness has prevented both researchers and practitioners from embracing this technology. This study aimed to bridge the gap in the literature by developing a novel smartphone PPG quality index (SPQI) that can filter corrupted data. A total of 226 participants joined the study, and results from 1343 samples were used to validate the proposed sinusoidal function-based model. In both the correlation coefficient and Bland–Altman analyses, the agreement between HRV measurements generated by both the smartphone PPG and the reference electrocardiogram improved when data were filtered through the SPQI. Our results support not only the proposed approach but also the general value of using smartphone PPG in HRV analysis.

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 Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaodong Ding ◽  
Yiqin Wang ◽  
Yiming Hao ◽  
Yi Lv ◽  
Rui Chen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Pulse Rate ◽  
Euclidean Distance ◽  
Operating Characteristic ◽  
Pulse Wave ◽  
Characteristic Curve ◽  
Signal Quality ◽  
Training Set ◽  
Pulse Rate Variability ◽  
Operating Characteristic Curve

Background. Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. Objective. The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. Methods. During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. Results. The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. Conclusion. This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

Comment on ‘pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability’

2021 ◽  
Vol 42 (9) ◽  
pp. 098001
Author(s):  
Jacquelin Peck ◽  
Michael J Wishon ◽  
Harrison Wittels ◽  
Hector Davila ◽  
S Howard Wittels ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Cardiovascular Health ◽  
Pulse Rate Variability

scholarly journals Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses

2020 ◽  
Vol 11 ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
Karthik Budidha ◽  
Tomas Ysehak Abay ◽  
James M. May ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Rate Variability ◽  
Autonomic Responses

scholarly journals Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability

2020 ◽  
Vol 41 (7) ◽  
pp. 07TR01 ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M May ◽  
Robinson Torres ◽  
Panayiotis A Kyriacou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Cardiovascular Health ◽  
Pulse Rate Variability

scholarly journals Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7052
Author(s):  
Pei-Chun Su ◽  
Elsayed Z. Soliman ◽  
Hau-Tieng Wu
Keyword(s):  
Quality Index ◽  
Signal To Noise Ratio ◽  
Clinical Applications ◽  
Noise Robustness ◽  
Signal Quality ◽  
New Techniques ◽  
Detection Algorithms ◽  
Signal Quality Index ◽  
The Mean ◽  
Electrocardiogram Ecg

An automatic accurate T-wave end (T-end) annotation for the electrocardiogram (ECG) has several important clinical applications. While there have been several algorithms proposed, their performance is usually deteriorated when the signal is noisy. Therefore, we need new techniques to support the noise robustness in T-end detection. We propose a new algorithm based on the signal quality index (SQI) for T-end, coined as tSQI, and the optimal shrinkage (OS). For segments with low tSQI, the OS is applied to enhance the signal-to-noise ratio (SNR). We validated the proposed method using eleven short-term ECG recordings from QT database available at Physionet, as well as four 14-day ECG recordings which were visually annotated at a central ECG core laboratory. We evaluated the correlation between the real-world signal quality for T-end and tSQI, and the robustness of proposed algorithm to various additive noises of different types and SNR’s. The performance of proposed algorithm on arrhythmic signals was also illustrated on MITDB arrhythmic database. The labeled signal quality is well captured by tSQI, and the proposed OS denoising help stabilize existing T-end detection algorithms under noisy situations by making the mean of detection errors decrease. Even when applied to ECGs with arrhythmia, the proposed algorithm still performed well if proper metric is applied. We proposed a new T-end annotation algorithm. The efficiency and accuracy of our algorithm makes it a good fit for clinical applications and large ECG databases. This study is limited by the small size of annotated datasets.

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