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 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 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.

scholarly journals Imaging Photoplethysmography: a Real-time Signal Quality Index

2017 ◽  
Author(s):  
Sibylle Fallet ◽  
Yann Schoenenberger ◽  
Lionel Martin ◽  
Fabian Braun ◽  
Virginie Moser ◽  
...  
Keyword(s):  
Real Time ◽  
Quality Index ◽  
Time Signal ◽  
Signal Quality ◽  
Signal Quality Index

Dynamic signal quality index for electrocardiograms

2018 ◽  
Vol 39 (10) ◽  
pp. 105008 ◽  
Author(s):  
Negin Yaghmaie ◽  
Mohammad Ali Maddah-Ali ◽  
Herbert F Jelinek ◽  
Faezeh Mazrbanrad
Keyword(s):  
Quality Index ◽  
Signal Quality ◽  
Signal Quality Index ◽  
Dynamic Signal

scholarly journals Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3870 ◽  
Author(s):  
Keisuke Kamata ◽  
Koichi Kinoshita ◽  
Manabu Kano
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Least Squares ◽  
Partial Least Squares ◽  
Health Monitoring ◽  
Normal Sinus Rhythm ◽  
Local Model ◽  
Heart Rate Variability Analysis ◽  
Modeling Framework ◽  
Hrv Analysis

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

scholarly journals APPLYING WEIGHTED PULSE DECOMPOSITION ANALYSIS WITH SIGNAL QUALITY INDEX TO FINGER PHOTOPLETHYSMOGRAM FOR ESTIMATION OF BLOOD PRESSURES

2021 ◽  
Vol 39 (Supplement 1) ◽  
pp. e69-e70
Author(s):  
Tzung-Dau Wang ◽  
Jia-Wei Guo ◽  
Pei-Yun Tsai ◽  
Hung-Ju Lin ◽  
An-Yeu Wu
Keyword(s):  
Quality Index ◽  
Decomposition Analysis ◽  
Signal Quality ◽  
Signal Quality Index ◽  
Blood Pressures
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