Frequency domain analysis of HR series

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Table of contents

Introduction
1 Heart rate analysis through mean change point detection: Case study of marathoners
1.1 Introduction
1.2 HR analysis and change detection approach
1.2.1 Frequency domain analysis of HR series
1.2.2 Frequency and time domain analysis of HR series through change detection approach
1.2.2.1 Change detection on the mean
1.2.2.2 Change detection on the spectral density
1.3 Experiment and data
1.3.1 Subjects
1.3.2 Data acquisition and pre-processing
1.3.2.1 Case of marathon runners
1.3.2.2 Case of shift workers
1.3.3 Pre-processing HR data
1.4 Results
1.4.1 Results for marathon runners
1.4.2 Results for shift workers
1.4.3 The two cohorts study
1.4.3.1 Statistical study of the cohorts
1.4.3.2 Results
1.5 Discussion
1.6 Conclusion
2 Change point detection by Filtered Derivative on the mean with p-Value method (FDpV)
2.1 Introduction
2.2 FDpV for change detection: a two step procedure
2.3 Some FDpV’s applications: examples of academic and real cases
2.3.1 Case of change detection on the mean
2.3.2 Case of change detection on the EDA signal
2.4 Discussion
2.5 Conclusion
3 Experimental protocol dedicated for marathoners state change assessment based on Electrodermal activity (EDA) measurement
3.1 In vivo pre-test experimentation with embedded sensors: Competition of Foulees du Lac
3.1.1 Population
3.1.2 Sensors for HR, EDA and respiration measurement
3.1.3 Faced issues
3.1.4 Towards a dedicated protocol for EDA measurement
3.2 EDA for state change detection
3.2.1 EDA versus HR
3.2.2 EDA indicators of state changes
3.2.3 EDA measurement
3.2.4 The Q sensor
3.3 Some possible EDA features and artefacts measurement
3.3.1 Motion artefact
3.3.2 Artefact due to mis-use of the sensor
3.3.2.1 Artefacts related to moved or detached electrodes
3.3.2.2 Missed data and zero values
3.3.3 Particular EDA feature: the storm
3.4 Comar Marathon with a well established protocol
3.4.1 Principle of the Comar experiment
3.4.2 Execution of the experience
3.4.3 Presentation of the protocol
3.4.4 Evaluation of the Comar protocol: data validation
3.5 Conclusion
4 Temporal signatures of electrodermal activity (EDA) for the evaluation of runners’performance: start and nish phases
4.1 Introduction
4.2 Protocol and data collection
4.2.1 Frame of the experiment
4.2.2 Population and classes of participants
4.2.3 Materials
4.3 Pre-processing
4.3.1 EDA artefacts identication
4.3.2 EDA special feature: The storm
4.4 Preliminary EDA analysis on a reference runner (P6)
4.5 Athletes EDA signature: the start and the nish phases
4.5.1 Temporal signature and EDA level of the start phase of the competition
4.5.1.1 Temporal signature of the start phase
4.5.1.2 EDA level at the start phase
4.5.2 Temporal signature and EDA level at the nish phase of a competition
4.5.2.1 Temporal signature of the nish phase
4.5.2.2 EDA level at the nish phase
4.6 Electrodermal reactions at the start and the nish phases
4.7 Conclusion
4.8 References
5 Tonic level and phasic activity extraction and motion artefact and special events detection
5.1 Introduction
5.2 EMD for EDA tonic level extraction
5.2.1 EDA tonic level extraction
5.2.2 EMD approach
5.2.3 IMF aggregation strategy for estimating EDA tonic component[73, 74]
5.3 EDA signal analysis: Case study of a marathon runner
5.3.1 EDA tonic level: extraction via EMD components aggregation
5.3.2 Pseudo periodic artefact detection via EMD components
5.4 Change point detection on IMF components of the EDA signal
5.5 Conclusion
Appendices
A Conception of an experimental protocol
B Participant form
C Sensor form
Bibliography