An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data

Abstract

Electrocardiogram, electrodermal activity, electromyogram, continuous blood pressure, and impedance cardiography are among the most commonly used peripheral physiological signals (biosignals) in psychological studies and healthcare applications, including health tracking, sleep quality assessment, disease early-detection/diagnosis, and understanding human emotional and affective phenomena. This paper presents the development of a biosignal-specific processing toolbox (Bio-SP tool) for preprocessing and feature extraction of these physiological signals according to the state-of-the-art studies reported in the scientific literature and feedback received from the field experts. Our open-source Bio-SP tool is intended to assist researchers in affective computing, digital and mobile health, and telemedicine to extract relevant physiological patterns (i.e., features) from these biosignals semi-automatically and reliably. In this paper, we describe the successful algorithms used for signal-specific quality checking, artifact/noise filtering, and segmentation along with introducing features shown to be highly relevant to category discrimination in several healthcare applications (e.g., discriminating patterns associated with disease versus non-disease). Further, the Bio-SP tool is a publicly-available software written in MATLAB with a user-friendly graphical user interface (GUI), enabling future crowd-sourced modification to these tools. The GUI is compatible with MathWorks Classification Learner app for inference model development, such as model training, cross-validation scheme farming, and classification result computation.

Context-aware experience sampling reveals the scale of variation in affective experience

“…The ECG signal was processed following previous work 94 . First, the raw signal was passed through an elliptic bandpass filter to remove baseline and high frequency noise without affecting the waveform.…”

“…First, the raw signal was passed through an elliptic bandpass filter to remove baseline and high frequency noise without affecting the waveform. Initial quality checks were then performed for each beat, checking for overall waveform shape, and acceptable minimum, maximum, and minimum-to-maximum values 94 . The default parameters, originally selected based on data collected in laboratory settings, were found to be too aggressive for this ambulatory data set, so we relaxed these thresholds based on manual review of the results by experts (J.B.W., K.S.Q.)…”

“…for a random subset of the data. The parameters we implemented are reported in supplementary Table S2 , along with the original recommendations 94 .…”

“…ICG feature detection was performed using an in-house implementation based on methods described in previous work 94 , 98 , and adjusted for ambulatory data using thresholds determined by comparing performance of the original algorithms with hand-scored impedance cardiographic data on a subset of participants. The first derivative of the basal impedance (Z 0 ) signal, dZ/dt, was used as the basis for all analyses.…”

“…The first derivative of the basal impedance (Z 0 ) signal, dZ/dt, was used as the basis for all analyses. The signal was segmented into time windows corresponding to 250 ms before the ECG R-peak to 500 ms after; eight such segments (i.e., 8 beats) were averaged together to form overlapping ensembles 94 . B points were detected in each ensemble by taking the first and second derivatives of the dZ/dt signal and comparing them with thresholds based on signal frequency (supplementary Table S2 ) 98 .…”