Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

Abstract

The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.

Waveform Analysis to Estimate Respiratory Rate

“…This is in keeping with the literature, where it is well reported that physiologic signals can be expected to contain periods of artifact in the Critical Care setting [16]. Each 10 s segment of ECG and PPG data was categorised as either high or low quality using the signal quality indicator (SQI) reported in [17]. This SQI determines the quality of the signal in two steps.…”

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

“…This SQI uses a normal beat template and compares it to each individual detected beat (Orphanidou et al 2015). Because asystole, bradycardia and tachycardia alarms will generally have consistent morphology between beats, the correlation between beats in the record will be high.…”

“…These features are referred to as Signal Quality Indices (SQIs). SQIs are often developed and tested using ECG waveform datasets annotated as clinically usable if a human expert can derive a reliable heart rate and unusable otherwise (Clifford et al 2012, Orphanidou et al 2015). While such datasets provide a basis for motion and other artefacts in ECG waveforms, they often lack an important subset of ECG waveforms present in the clinical environment: pathologically different arrhythmic ECG waveforms which may be mistaken as noise.…”

Assessing Effect of Beat Detector on Detection Dependent Signal Quality Indices

“…Selected SQIs were calculated using beats detected by each of these detectors and reference beat annotations. The F1 score to detect beats for two 10 s periods with respect to reference beat annotations was calculated as [12] Mean heart rate for 10 s maxrri [12] Maximum RR interval for 10 s maxrr2minrr [12] Ratio of maximum RR interval to minimum RR interval for 10 s avecorr [12] Average template matching correlation coefficient: Average of the correlation coefficients of each QRS complex with mean QRS complex qrsa [14] Median value of the peak-to-nadir amplitude difference of the QRS complexes in 10 s sdrr2meanrr [13] Ratio of the standard deviation of RR interval to mean RR interval rangeqrs [13] Range of signal amplitude around QRS detection: Maximum minus the minimum signal amplitude within a QRS complex bw [11] Baseline wander estimation using cubic spline pln [11] Power line noise estimation using regression-subtraction residual [11] Residual noise by subtracting the estimated signal (median over 10 s) after subtracting baseline wander and power line noise (The Mathworks, Natick, MA).…”