Name
Title | ||
|---|---|---|
Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone. |
Abstract | ||
|---|---|---|
Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, Incremental-Merge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0% and a positive predictive value of 98.8% were obtained, which were 7.7% higher than the IMS algorithm in sensitivity, and 8.3% higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944306 | EMBC |
Keywords | Field | DocType |
photoplethysmography,ppg signals,inverted triangular area,heart rate,adaptive threshold,medical signal detection,waveform detection,smartphone,photoplethysmogram,incremental-merge segmentation,real-time detection,adaptive thresholding,computational complexity,baseline drift,adt,dedicated pulse oximeters,ims,linear time complexity,ppg,smart phones,smartphone cameras,adaptive waveform detection,real time,photoplethysmogram traces,peak detection,android,haemodynamics,thresholding | Signal processing,Computer vision,Computer science,Photoplethysmogram,Segmentation,Waveform,Electronic engineering,Pulse oximeters,Sampling (statistics),Artificial intelligence,Thresholding,Time complexity | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjun Jiang | 1 | 356 | 24.25 |
| Peter Wittek | 2 | 0 | 1.01 |
| Li Zhao | 3 | 41 | 4.25 |
| Shi Chao Gao | 4 | 0 | 0.34 |