A Workflow to Process Data Collected with PULSE Systems
heartbeatr utility function
Process PULSE data file by file (STEPS 1-6)
Halves heart beat frequencies computed by pulse_heart
(STEP 4) Determine the heartbeat rate in all channels of a split PUL...
Increase the number of data points in PULSE data through interpolation
Normalize PULSE heartbeat rate estimates
(STEP 3) Optimize PULSE data through interpolation and smoothing
heartbeatr utility function
Plot raw PULSE data
Plot processed PULSE data
(STEP 2) Split pulse_data across sequential time windows
Summarise PULSE heartbeat rate estimates over new time windows
(STEP 1) Read data from all PULSE files in the target folder
Find peaks of waves in raw PULSE data
heartbeatr: A Workflow to Process Data Collected with PULSE Systems
(STEP 6) Choose the best heart beat frequency estimate from among tw...
(STEP 5) Fix heart rate frequencies double the real value
Get paths to pulse example files
Determine the heartbeat rate in all channels of a PULSE split window
Determine the heart beat frequency in one PULSE channel
Smooth PULSE data
Process PULSE data from a single experiment (STEPS 1-6)
Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system (<https://electricblue.eu/pulse>) from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting. Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) <doi:10.3390/a5040588>. Check original code at <https://github.com/ig248/pyampd>. ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (<https://electricblue.eu>).