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

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.

Download Full-text

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

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2016.12.004 ◽

2017 ◽

Vol 33 ◽

pp. 201-212 ◽

Cited By ~ 8

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

Download Full-text

Twin photodetectors wearable PPG sensor for controlled measurement and improved signal quality index at lower sample rate

10.1063/5.0065870 ◽

2021 ◽

Author(s):

Muhideen Abbas Hasan ◽

Ahmed Kareem Khudhair ◽

Settar S. Keream

Keyword(s):

Quality Index ◽

Signal Quality ◽

Signal Quality Index ◽

Sample Rate

Download Full-text

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

Sensors ◽

10.3390/s20247052 ◽

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.

Download Full-text

Imaging Photoplethysmography: a Real-time Signal Quality Index

2017 Computing in Cardiology Conference (CinC) ◽

10.22489/cinc.2017.142-139 ◽

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

Download Full-text

A signal quality index for ballistocardiogram recordings based on electrocardiogram RR intervals and matched filtering

2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) ◽

10.1109/bhi.2018.8333390 ◽

2018 ◽

Cited By ~ 2

Author(s):

A. Ozan Bicen ◽

Omer T. Inan

Keyword(s):

Quality Index ◽

Matched Filtering ◽

Signal Quality ◽

Rr Intervals ◽

Signal Quality Index

Download Full-text

Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Sensors ◽

10.3390/s21165357 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5357

Author(s):

Gaël Vila ◽

Christelle Godin ◽

Sylvie Charbonnier ◽

Aurélie Campagne

Keyword(s):

Heart Rate ◽

Quality Index ◽

Real Life ◽

Motion Artifacts ◽

Cardiac Monitoring ◽

Data Loss ◽

Median Error ◽

Monitoring Applications ◽

The Impact ◽

Frequency Power

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

Download Full-text