Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions

Abstract

Photoplethysmogram (PPG) monitoring is not only essential for critically ill patients in hospitals or at home, but also for those undergoing exercise testing. However, processing PPG signals measured after exercise is challenging, especially if the environment is hot and humid. In this paper, we propose a novel algorithm that can detect systolic peaks under challenging conditions, as in the case of emergency responders in tropical conditions. Accurate systolic-peak detection is an important first step for the analysis of heart rate variability. Algorithms based on local maxima-minima, first-derivative, and slope sum are evaluated, and a new algorithm is introduced to improve the detection rate. With 40 healthy subjects, the new algorithm demonstrates the highest overall detection accuracy (99.84% sensitivity, 99.89% positive predictivity). Existing algorithms, such as Billauer's, Li's and Zong's, have comparable although lower accuracy. However, the proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination. For best performance, we show that a combination of two event-related moving averages with an offset threshold has an advantage in detecting systolic peaks, even in heat-stressed PPG signals.

Figure 1. Fingertip photoplethysmogram signal measurement.

The photoplethysmogram waveform consists of one systolic wave and one diastolic wave.

Figure 2. Measurement Protocol.

The duration of the whole experiment was approximately 4

Figure 3. Flowchart for Method I.

This systolic peak time-domain detection algorithm consists of two stages: bandpass filter and thresholding.

Figure 4. Filtering output.

(a) Original signal PPG from record I-31 measured at rest (b) low-pass filter in Method II (c) low-pass filter in Method III (d) band-pass filter in Method VI.

Figure 5. Features output.

(a) Original signal PPG from record I-31 measured at rest (b) derivative calculation in Method II (c) slope-sum in Method III (d) squaring in Method VI.

Figure 6. Flowchart for Method II.

This systolic peak time-domain detection algorithm consists of three main stages: lowpass filter, slope sum function, and thresholding.

Figure 7. Flowchart for Method III.

This systolic peak time-domain detection algorithm consists of three main stages: lowpass filter, slope sum function, and thresholding.

Figure 8. Flowchart for Method VI.

This systolic peak time-domain detection algorithm consists of three main stages: pre-processing (bandpass filter), feature extraction (two moving averages), and classification (threshold).

Figure 9. Generating blocks of interest using two moving averages.

(a) Measured at rest I-31 (b) measured II-51 after exercise 1. The dotted signal (in black) is the first moving average while the dashed signal is the second moving average . The squares are the blocks of interest.

Figure 10. Detected systolic peaks in PPG signals at rest (before exercise).

Here, ‘O’ represents the annotated systolic peak, ‘+’ represents detected peak by the algorithm, while ‘*’ represents the false positive.

Figure 11. Detected systolic peaks in PPG signals after exercise.

Here, ‘O’ represents the annotated systolic peak, ‘+’ represents detected peak by the algorithm; thus, when the circle has red-cross inside, it means the annotated peak has been correctly detected. If the circle is empty it means false negative; while ‘*’ represents the false positive.