2015 · 15 citations · 8 references
EngineeringBiomedical EngineeringPhysiologyBioinstrumentationBiomedical ImagingBiomedical Signal ProcessingCardiac Signal ProcessingCardiovascular MonitoringDigital Signal ProcessingHeart Rate VariabilitySignal ProcessingVital SignsHeart Rate MonitoringHeart Rate Variability MeasurementHeart Rate Variability Signal PreprocessingPhotoplethysmogram Signal ProcessingPhotoplethysmographyPhysiological Signal ProcessingPulse Transit Time AnalysisSmartphone-based Diagnostics
This work compares several fiducial points to detect the arrival of a new pulse in a photoplethysmographic signal using the built-in camera of smartphones or a photoplethysmograph. Also, an optimization process for the signal preprocessing stage has been done. Finally we characterize the error produced when we use the best cutoff frequencies and fiducial point for smartphones and photopletysmograph and compare if the error of smartphones can be reasonably be explained by variations in pulse transit time. The results have revealed that the peak of the first derivative and the minimum of the second derivative of the pulse wave have the lowest error. Moreover, for these points, high pass filtering the signal between 0.1 to 0.8 Hz and low pass around 2.7 Hz or 3.5 Hz are the best cutoff frequencies. Finally, the error in smartphones is slightly higher than in a photoplethysmograph.
8
A Real-Time QRS Detection Algorithm
Jiapu Pan, W.J. Tompkins · IEEE Transactions on Biomedical Engineering · 1985 · 7.7K citations
Kenta Matsumura, P. Rolfe, Jihyoung Lee et al. · PLoS ONE · 2014 · 73 citations · Full text