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Data-driven search for causal paths 
in cardiorespiratory parameters

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Presentation during Kasprowisko 2019 (XXV Konferencja Szkoleniowa i XXI Konferencja Wspólna Sekcji Elektrokardiologii Nieinwazyjnej i Telemedycyny oraz ISHNE

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Data-driven search for causal paths 
in cardiorespiratory parameters

  1. 1. Data-driven search for causal paths 
 in cardiorespiratory parameters Marcel Młyńczak, Ph.D. Zakopane, 8th of March, 2019
  2. 2. Introduction 2 Tachogram Breathing phases Respiration ECG ECG-based analyses are still rarely combined with respiratory activity
  3. 3. 3 Causality analysis may be applied for sports medicine Hypothesis Training Prediction Parameterization How could it happen in the opposite case?
 (counterfactuals) Time-domain Frequency-domain Information-domain Causal-domain Determining the training program (by interventions) based on the knowledge of causal structures
  4. 4. Background 4 Level Symbol Typical 
 ”activity” Typical 
 questions Examples Association Seeing What is? How would seeing X change my belief in Y? What does 
 a symptom tell me about the disease? Intervention Doing
 Intervening What if? What if I do X? What if I take aspirin, will my headache be cured? Counterfactuals Imagining Retrospection Why? Was it X that caused Y? What if I had acted differently? Was it the aspirin 
 that stopped my headache? J. Pearl ”The Seven Tools of Causal Inference with Reflections on Machine Learning”
  5. 5. Cardiorespiratory context 5 From general to detail, in data-driven manner Parameters globally Signals globally Signals locally Taking into account temporal relationships Assessment of the causal connections stability in narrow time intervals Without considering the impact of time Causal structure needs to be primarily established
  6. 6. Cardiorespiratory context 6 Parameters globally Signals globally Signals locally Taking into account temporal relationships Assessment of the causal connections stability in narrow time intervals Without considering the impact of time Latest papers Short-term bivariate temporal orders exploration for causality analysis - a conceptual study Marcel Mły´nczak Warsaw University of Technology Faculty of Mechatronics Warsaw, Poland marcel.mlynczak@pw.edu.pl Abstract—Causality analysis of cardiorespiratory signals may be carried out on different levels of detail, e.g., global time- independent or global temporal. To analyze particular parts of the data one also needs a local approach. Therefore, the concept of short-term bivariate temporal orders (SBTOs) exploration was introduced. Two different kernels, based on linear modeling and transfer entropy, were proposed. The most important applications of the method are the stability assessment of the temporal orders across time and the detection of similar signals’ parts or changes in signals’ dynamics. The algorithm was preliminarily tested and the results appear to confirm theoretical predictions. SBTOs based on linear modeling kernel is informative enough for regular curves; more sophisticated transfer entropy approach is more relevant when the complexity is higher. Index Terms—Causality analysis, temporal succession, tidal volume, RR intervals, stability I. INTRODUCTION The network physiology concept is widely accepted [1]. It lies in the assumption that several systems are combined in a network of non-linear dependencies with various loops, feedbacks, and delays in transmitting information. The car- diovascular or respiratory systems may be also influenced by many environmental, psychological or demographic factors [2]. One of the possible and commonly analyzed combinations is that of respiration and heart activity. Several effects have been introduced and accepted. For instance, sinus respiratory arrhythmia is a phenomenon evident in resting ECG as the effect of successive inspirations and expirations [3]. The baroreflex effect is based on adjusting neural responses and thus affecting both heart and respiratory activity in a specific way [4]. Finally, the so-called cardiorespiratory coupling is that heartbeats coincide with the respiratory phases particularly because of increased sympathetic nervous activity [5]. Many mathematical approaches were proposed to analyze cardiorespiratory relationships. We also hypothesized that the parameterization of the cause-and-effect relationships would be promising. However, to evaluate different levels of con- nections we stated the analysis should move from general to detail; from the look at the global parameters without considering the impact of time; then through the insight into temporal relationships and causalities; up to the analysis in narrow time intervals enabling the assessment of the stability of causal connections within different time segments, e.g. during successive states after an orthostatic maneuver. The first two approaches have been already utilized [6], [7]. In the first, the discovery of time-independent causal paths sug- gested different results depending on the body position. During lying supine the values of tidal volume seemed to cause heart activity variation, which affected average heart activity, which finally influenced respiratory timing. Alternately for standing, the relation went from normalized respiratory activity variation to average heart activity. In the second approach, temporal relationships examined by Granger causality frameworks (with extensions that consider zero-lag effects [8]) or Time Series using Restricted Structural Equation Models (TiMINo) [9], suggested that the most promi- nent combination appeared between tachogram (RR intervals curve) and tidal volume signal. However, when analyzing signals, instead of beat-by-beat sequences, the results were weak and not stable, which is an effect of considering relatively long segments of data. On the other side, temporal causality analyses require long enough data to work correctly, e.g., based on the definition and due to the stationarity criterion. Therefore, there is a need for a framework to causally examine local short segments of data and to explore the temporal orders of cardiorespiratory signals. The presentation of the concept of such a technique is hence the main aim of the study. II. MATERIALS AND METHODS A. Description of the algorithm The block diagram of the short-term bivariate temporal orders (SBTOs) exploration is presented in Fig. 1. First, the bivariate dataset should be loaded. One needs to decide, which signal will not be moved, and which will be shifted in time (backward, forward, in the given range). There are several inputs parameters: • the length of the signal part being used in the particular step (the same length for both signals); • the maximum shift in time (the same for backward and forward direction); • time resolution of the signal parts’ centers (by default compatible with the sampling frequency); ORIGINAL RESEARCH published: 30 October 2018 doi: 10.3389/fphys.2018.01455 Frontiers in Physiology | www.frontiersin.org 1 October 2018 | Volume 9 | Article 1455 Edited by: Toby Mündel, College of Health, Massey University, New Zealand Reviewed by: Jui-Lin Fan, University of Otago, New Zealand Cristina Blasco-Lafarga, Universitat de Valencia, Spain Anabel Forte Deltell, Universitat de Valencia, Spain *Correspondence: Marcel Mły ´nczak marcel.mlynczak@pw.edu.pl Specialty section: This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology Received: 09 July 2018 Accepted: 25 September 2018 Published: 30 October 2018 Citation: Mły ´nczak M and Krysztofiak H (2018) Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes. Front. Physiol. 9:1455. doi: 10.3389/fphys.2018.01455 Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes Marcel Mły ´nczak1 * and Hubert Krysztofiak2 1 Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland, 2 Department of Applied Physiology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland Training of elite athletes requires regular physiological and medical monitoring to plan the schedule, intensity and volume of training, and subsequent recovery. In sports medicine, ECG-based analyses are well-established. However, they rarely consider the correspondence of respiratory and cardiac activity. Given such mutual influence, we hypothesize that athlete monitoring might be developed with causal inference and that detailed, time-related techniques should be preceded by a more general, time-independent approach that considers the whole group of participants and parameters describing whole signals. The aim of this study was to discover general causal paths among cardiac and respiratory variables in elite athletes in two body positions (supine and standing), at rest. ECG and impedance pneumography signals were obtained from 100 elite athletes. The mean heart rate, the root-mean-square difference of successive RR intervals (RMSSD), its natural logarithm (lnRMSSD), the mean respiratory rate (RR), the breathing activity coefficients, and the resulting breathing regularity (BR) were estimated. Several causal discovery frameworks were applied, comprising Generalized Correlations (GC), Causal Additive Modeling (CAM), Fast Greedy Equivalence Search (FGES), Greedy Fast Causal Inference (GFCI), and two score-based Bayesian network learning algorithms: Hill-Climbing (HC) and Tabu Search. The discovery of cardiorespiratory paths appears ambiguous. The main, still mild, rules best supported by data are: for supine - tidal volume causes heart activity variation, which causes average heart activity, which causes respiratory timing; and for standing - normalized respiratory activity variation causes average heart activity. The presented approach allows data-driven and time-independent analysis of elite athletes as a particular population, without considering prior knowledge. However, the results seem to be consistent with the medical background. Causality inference is an interesting mathematical approach to the analysis of biological responses, which are complex. One can use it to profile athletes and plan appropriate training. In the next step, we plan to expand the study using time-related causality analyses. Keywords: athlete training adaptation biomarker, cardiac function, tidal volume, cardiorespiratory causality, elite athletes ORIGINAL RESEARCH published: 05 February 2019 doi: 10.3389/fphys.2019.00045 Frontiers in Physiology | www.frontiersin.org 1 February 2019 | Volume 10 | Article 45 Edited by: Toby Mündel, College of Health, Massey University, New Zealand Reviewed by: Luca Faes, Università degli Studi di Palermo, Italy Luke Charles Wilson, University of Otago, New Zealand *Correspondence: Marcel Mły ´nczak marcel.mlynczak@pw.edu.pl Specialty section: This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology Received: 21 October 2018 Accepted: 16 January 2019 Published: 05 February 2019 Citation: Mły ´nczak M and Krysztofiak H (2019) Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof. Front. Physiol. 10:45. doi: 10.3389/fphys.2019.00045 Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof Marcel Mły ´nczak1 * and Hubert Krysztofiak2 1 Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, Warsaw, Poland, 2 Department of Applied Physiology, Mossakowski Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland Fitness level, fatigue and adaptation are important factors for determining the optimal training schedule and predicting future performance. We think that adding analysis of the mutual relationships between cardiac and respiratory activity enables better athlete profiling and feedback for improving training. Therefore, the main objectives were (1) to apply several methods for temporal causality analysis to cardiorespiratory data; (2) to establish causal links between the signals; and (3) to determine how parameterized connections differed across various subgroups. One hundred elite athletes (31 female) and a control group of 20 healthy students (6 female) took part in the study. All were asked to follow a protocol comprising two 5-min sessions of free breathing - once supine, once standing. The data were collected using Pneumonitor 2. Respiratory-related curves were obtained through impedance pneumography, along with a single-lead ECG. Several signals (e.g., tidal volume, instantaneous respiratory rate, and instantaneous heart rate) were derived and stored as: (1) raw data down-sampled to 25 Hz; (2) further down-sampled to 2.5 Hz; and (3) beat-by-beat sequences. Granger causality frameworks (pairwise-conditional, spectral or extended), along with Time Series Models with Independent Noise (TiMINo), were studied. The connections enabling the best distinctions were found using recursive feature elimination with a random forest kernel. Temporal causal links are the most evident between tidal volume and instantaneous heart rate signals. Predictions of the “effect” variable were improved by adding preceding “cause” samples, by medians of 20.3% for supine and 14.2% for standing body positions. Parameterized causal link structures and directions distinguish athletes from non-athletes with 83.3% accuracy on average. They may also be used to supplement standard analysis and enable classification into groups exhibiting different static and dynamic components during performance. Physiological markers of training may be extended to include cardiorespiratory data, and causality analysis may improve the resolution of training profiling and the precision of outcome prediction. Keywords: granger causality framework, athlete training adaptation biomarker, cardiac function, tidal volume, elite athletes In review
  7. 7. Cardiorespiratory context 7 Parameters globally Without considering the impact of time Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes Marcel Młyńczak, Hubert Krysztofiak Published on 30th of October, 2018
  8. 8. Methodology 8 Device • ECG signal to estimate heart rate and tachogram • Impedance signal relating to 
 main breathing activity • Portable • Recording on SD card • Rechargeable battery • Motion signal from 3-axis accelerometer to indicate subject’s activity and body position • 14.2cm x 6.9cm x 2.3cm; 160g • Sinusoidal application current amplitude adjustable up to 1mA, with a single, adjustable frequency (100kHz by default) • Impedance range: 0-250 Ohms • 250Hz sampling frequency, 100Hz pass frequency, 10-bit resolution • ECG amplifier has a gain of 100V/V, 
 10nV/sqrt(Hz) noise • InvenSense’s MPU-6050 (accelerometer and gyroscope unit, available commercially)
  9. 9. Methodology 9 Electrode configuration IP electrodes ECG electrodes
  10. 10. 5-minute
 measurement Free breathing 
 activity Supine, then standing 10 Study performed on Polish elite athletes, 
 before Olympic Games in Rio de Janeiro 2016: 32 females and 68 males Ethics Committee approval 
 AKBE/74/17 (WUM) Methodology Study protocol
  11. 11. 11 • Heart rate (HR) • Root-mean-square difference of successive RR intervals (RMSSD) • Natural logarithm of RMSSD (lnRMSSD) • Respiratory rate (RR) • Breathing regularity (BR) ➡ iRR - instantaneous respiratory rate ➡ InsT - duration of inspiration phase ➡ ExpT - duration of expiration phase ➡ InsV - tidal volume during inspiration phase ➡ ExpV - tidal volume during expiration phase Methodology Parameters
  12. 12. 12 • Generalized Correlations • Causal Additive Modeling • Fast Greedy Equivalence Search • Greedy Fast Causal Inference • Hill-Climbing • Tabu Search Methodology Causal Search Techniques • Russell SJ, Norvig P (2009). Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition. • Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition. • Margaritis D (2003). Learning Bayesian Network Model Structure from Data. Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. • Daly R, Shen Q (2007). "Methods to Accelerate the Learning of Bayesian Network Structures". Proceedings of the 2007 UK Workshop on Computational Intelligence.
  13. 13. Results 13 HR RMSSD lnRMSSD ciRR cInsT cInsVcExpT cExpV ciRR cInsT cInsVcExpT cExpV Cardiac parameters Respiratory parameters SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD RR BR RR BR ciRR cInsT cInsV cExpTcExpV ciRR cInsT cInsVcExpT cExpV Cardiac parameters Respiratory parameters SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD HR RMSSD lnRMSSD RR BR RR BR Cardiac parameters Respiratory parameters SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD HR RMSSD lnRMSSD RR BR BR ciRR cInsT cInsV cExpT cExpV ciRR cInsT cInsV cExpT cExpV RR ciRR cInsT cInsV cExpT cExpV ciRR cInsT cInsV cExpT cExpV Cardiac parameters Respiratory parameters SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD HR RMSSD lnRMSSD RR BR RR BR Cardiac parameters Respiratory parameters SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD RMSSD BR RR BR ciRR cInsT cInsV cExpT cExpV RR ciRR cInsT cInsV cExpT cExpV HR lnRMSSD ciRR cInsT cInsV cExpT cExpV Cardiac parameters Respiratory parameters RR BR ciRR cInsT cInsV cExpT cExpV SUPINE STANDING Cardiac parameters Respiratory parameters HR RMSSD lnRMSSD HR RMSSD lnRMSSD RR BR GC CAM FGES GFCI HC Tabu Overview
  14. 14. 14 • The inspiration phase duration appears to cause the 
 expiration phase duration. • The tidal volume during inspiration phase appears to cause 
 the tidal volume during expiration phase. Results Supine body position Tidal Volume Heart Activity Variation Average Heart Activity Respiratory Timing Respiratory Sinus Arrythmia Cardiorespiratory Coupling The lack of an arrow coming out to the breath parameter is in accordance with the literature results. Respiratory frequency is the most commonly measured respiratory parameter, and is at the end of the chain.
  15. 15. 15 • Definitely fewer connections found. • The respiratory frequency does not appear to affect any 
 of the considered parameters. Results Standing body position Normalized Respiratory Activity Variation Average Heart Activity Respiratory Sinus Arrythmia?
  16. 16. Next paper 16 Signals globally Taking into account temporal relationships Cardiorespiratory 
 Temporal Causal Links and the Differences by Sport 
 or Lack Thereof Marcel Młyńczak, Hubert Krysztofiak Published on 5th of February, 2019
  17. 17. Next steps 17 • Benchmark of causal search techniques • Causal analysis of IP + ECG + ABP signals • Causal analysis of IP + ECG + REO signals ➡ Data from control group are already stored. ➡ All in ”pair-wise” and ”multivariate” approaches. ➡ All in time-independent, temporal and stability contexts.
  18. 18. www.marcelmlynczak.com Zakopane, 8th of March, 2019

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