E4 TimeStamper helps researchers to automatically add timestamps to physiological data derived from Empatica's E4 wristbands.
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Updated
Jun 18, 2024
E4 TimeStamper helps researchers to automatically add timestamps to physiological data derived from Empatica's E4 wristbands.
Deep learning model for sepsis prediction using high-frequency data
Supporting code for the GX Dataset
Fever prediction model using high-frequency real-time sensor data
Physiological signals recorded with a Microsoft Band 2 are processed and modeled with a basic bayesian model to infer exaltation and relaxation states of mind
Web application for sepsis clinical assessment scores using batch ICU patient data
Integrating genomics and physiologic data (high-frequency) for sepsis detection
NeuroImaging-oriented Physiological Log Extraction for Modeling
Custom client for the E4 wristband from Empatica
An implementation of the WESAD processing and training pipeline in Python.
Phymdos - the ONTOX Physiological maps documentation software
Investigating the privacy inherent to publicly available, anonymized datasets of wearable data (mainly time-series and daily/hourly physiological data, such as activity, EKG, etc.). Exploration of differentially privacy solutions.
CNN models used in the paper "Biometric recognition using wearable devices in real-life settings" by Emanuela Piciucco, Elena Di Lascio, Emanuela Maiorana, Silvia Santini, Patrizio Campisi
Python implementation of RETROICOR - physiology regressors for fMRI preprocessing.
🔨 [UNDER DEVELOPMENT] A broad intro into scientific and mathematical computations in Python for data analysis, with recipes and short tutorials. Specific interests in pathophsyiology, comp bio, expression analysis, ethnography, clinical data, and GIS (for disparities research). Curated by @milodubois.
Experimentation with Wearables Data for Perioperative Medicine
Config files for my GitHub profile.
Scripts for the extraction of statistical descriptors from bio signals of the X-ITE Pain Database (preprocessing included).
Apache spark streaming analytic engine for predicting patient deterioration using physiological data
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