Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification

2020 ◽  
Vol 67 (3) ◽  
pp. 773-785 ◽  
Author(s):  
Kilin Shi ◽  
Sven Schellenberger ◽  
Fabian Michler ◽  
Tobias Steigleder ◽  
Anke Malessa ◽  
...  
Keyword(s):  
Quality Index ◽  
Heart Sound ◽  
Ensemble Classification ◽  
Signal Quality ◽  
Signal Quality Index

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 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

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.

A composite signal quality index for analysis of the large ECG datasets

2021 ◽  
Vol 69 ◽  
pp. 89
Author(s):  
Sara Mariani ◽  
Saeed Babaeizadeh ◽  
Reza Firoozabadi ◽  
Wei Zong
Keyword(s):  
Quality Index ◽  
Signal Quality ◽  
Signal Quality Index ◽  
Composite Signal
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