Robust heart rate estimation from multimodal physiological signals using beat signal quality index based majority voting fusion method

2017 ◽  
Vol 33 ◽  
pp. 201-212 ◽  
Author(s):  
Shalini A. Rankawat ◽  
Rahul Dubey
Keyword(s):  
Heart Rate ◽  
Quality Index ◽  
Majority Voting ◽  
Beat Signal ◽  
Physiological Signals ◽  
Signal Quality ◽  
Fusion Method ◽  
Rate Estimation ◽  
Signal Quality Index ◽  
Heart Rate Estimation

scholarly journals Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7564
Author(s):  
Seunghyeok Hong ◽  
Jeong Heo ◽  
Kwang Suk Park
Keyword(s):  
Quality Index ◽  
Screening Method ◽  
Reference Signal ◽  
Real Life ◽  
Motion Artifacts ◽  
Physiological Signals ◽  
Signal Quality ◽  
Body Parts ◽  
Smart Healthcare ◽  
Signal Quality Index

We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.

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.

Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation

2017 ◽  
Vol 64 (12) ◽  
pp. 2793-2802 ◽  
Author(s):  
Fernando Andreotti ◽  
Felix Grasser ◽  
Hagen Malberg ◽  
Sebastian Zaunseder
Keyword(s):  
Heart Rate ◽  
Quality Assessment ◽  
Signal Quality ◽  
Ecg Signal ◽  
Rate Estimation ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Heart Rate Estimation

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 A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data

2017 ◽  
Vol 11 (3) ◽  
pp. 487-496 ◽  
Author(s):  
Venkata Rajesh Pamula ◽  
Jose Manuel Valero-Sarmiento ◽  
Long Yan ◽  
Alper Bozkurt ◽  
Chris Van Hoof ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sampled Data ◽  
Rate Estimation ◽  
Heart Rate Estimation
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