際際滷

際際滷Share a Scribd company logo

CBMS_Presentation (5).pdf

A
Abhinandan panda

This document presents a policy-based diabetes detection system using formal runtime verification monitors of electrocardiogram (ECG) signals. ECG intervals are extracted from patient data and used to infer policies for detecting diabetes. These policies are represented as timed automata and monitored in real-time to detect violations or satisfactions. The system was tested on a dataset of ECG recordings from diabetic and healthy patients, achieving accurate diabetes detection through non-invasive ECG monitoring and formal verification of inferred health policies.

1 of 31
Download to read offline
IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022) Policy-Based Diabetes Detection using Formal Runtime Verification Monitors Abhinandan Panda 1 , Srinivas Pinisetty1 , Partha Roop 2 1 Indian Institute of Technology, Bhubaneswar, India 2 University of Auckland, Auckland, New Zealand December 5, 2022
Outline 1 Introduction 2 ECG 3 Proposed monitoring system 4 Policies Mining 5 RV Monitor 6 Experimental results & Comparison 7 Conclusion & Future Works 8 References
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Introduction: Diabetes A metabolic disorder Lack of control or balance in blood glucose (BG) Type 1 diabetes (deficiency of insulin) / Type 2 diabetes (excess of insulin) Hyperglycemia (very high blood glucose levels) / hypoglycemia (very low blood glucose levels) events (normal blood glucose level 140 mg/dL). CBMS 2022 Abhinandan Panda December 5, 2022 1/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Health Complications in Diabetes Figure: Health complications in diabetes [1] CBMS 2022 Abhinandan Panda December 5, 2022 2/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Global Diabetic Growth Figure: Global diabetic growth [1] CBMS 2022 Abhinandan Panda December 5, 2022 3/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Invasive Blood Glucose Monitoring Figure: Invasive blood glucose monitoring [1] CBMS 2022 Abhinandan Panda December 5, 2022 4/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Need of non-invasive continuous monitoring of diabetes Severe health complications [2]. Around 9.3% of people are affected by diabetes globally [3] . Tedious initial screening process About 45.8% of diabetes cases with cardiac complications are untreated [4]. Continuous diabetes monitoring technique should be adopted [5] CBMS 2022 Abhinandan Panda December 5, 2022 5/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Non-invasive approach: monitoring physiological signal ECG Time Amplitude S R Q P Q R S QRS interval P PR interval P-wave interval QT interval RT interval TpTe interval Te RR interval Tp Tp Figure: A typical ECG Signal CBMS 2022 Abhinandan Panda December 5, 2022 6/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG in diabetes Figure: ECG in diabetes [6] CBMS 2022 Abhinandan Panda December 5, 2022 7/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG and Diabetes: Analysis hypoglycemia (low blood glucose level) results in prolongation of QT interval [christensen2010]. Hypoglycemia (low blood glucose level) associated with increased heart rate (HR) [heger1996]. Hyperglycemia (high blood glucose level) related with reduced heart rate variability (HRV) [singh2000]. Corrected QT dispersion and PR interval have a significant change in hyperglycemia condition [marfella2000]. According to [nguyen2012], ECG parameters such as corrected QT interval, PR interval, corrected RT interval, corrected TpTe interval and heart rate (HR) can be used for identification of hypoglycemia and hyperglycemia detection. CBMS 2022 Abhinandan Panda December 5, 2022 8/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Related Works Authors Methods Accuracy Acharya et al. [7] Nonlinear 86.0 Jian et al. [8] Higher order spectrum 79.93 Acharya et al. [9] Discrete wavelet transform 92.02 Pachori et al. [10] Empirical mode decomposition 95.63 Swapna et al. [11] Deep learning (CNN-LSTM) 95.1 CBMS 2022 Abhinandan Panda December 5, 2022 9/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Motivation Deep learning-based models such as CNN, RNN, LSTM, BI-LSTM, GRU, etc. provide a good prediction accuracy, however, these models are "black-box". Monitor should not only be able to classify the input signals (ECG, PPG) accurately to access the condition of a patient but also the cause of the outcome should be explainable. To understand further the effect of physiological signal features on the outcome. There is an urge for explainable monitoring models in healthcare [reyes2020, gastounioti2020]. We propose a formal method-based framework that is correct by construction. We develop a formal runtime monitoring (RV) framework based on ECG sensing for diabetes monitoring. CBMS 2022 Abhinandan Panda December 5, 2022 10/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Runtime Verification : Overview Figure: Overview of the monitor based verification process CBMS 2022 Abhinandan Panda December 5, 2022 11/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor Definition Consider a given property tw(裡) defining the property to monitor that is defined as TA A. Function M : tw(裡) D is a verification monitor for , where D = {T, F, CT, CF} and is defined as follows, with tw(裡) denoting the current observation (a finite timed word over the alphabet 裡): M() = 錚 錚 錚 錚 錚 錚 錚 T if tw(裡) : 揃 F if tw(裡) : 揃 霧 CT if tw(裡) : 揃 霧 CF if 霧 tw(裡) : 揃 Correct by construction Satisfy impartiality and anticipation constraints (Bauer et al.[12]). CBMS 2022 Abhinandan Panda December 5, 2022 12/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Proposed Monitoring Approach Raw ECG ECG intervals ECG Policy inference ECG Processing Module Data Mining Model ECG Dataset (Diabetic, Healthy) Class label (diabetes/healthy) Figure: Policy learning framework ECG events True / False (Diabetes detection) Inferred ECG Policies ECG signal ECG Sensor ECG Processing Module RV monitor Figure: Proposed monitoring framework CBMS 2022 Abhinandan Panda December 5, 2022 13/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG and Diabetes Dataset Dataset We consider DICARDIA database [ledezma2014] of 65 subjects with: i) 51 diabetic subjects with cardiac complications of age 57.00 賊 10.00 years ii) 3 diabetic subjects without cardiac complications of age 49.00 賊 12.00 years iii) 11 healthy subjects as a control group of age 50.00 賊 6.00 years. iv) Approximately 30 min. long records. Signal Processing The ECG_Processing module is implemented in Python toolkit Neurokit2 [Makowski2021]. Apply a high pass Butterworth filter and a low pass filter to remove baseline drift and high-frequency noise from the ECG signal. The R-peaks in ECG are extracted using the Pan-Tompkins algorithm [13]. Wavelet analysis to detect the P-peaks, Q-peaks, R-peaks, T-peaks and T-ends of the ECG. CBMS 2022 Abhinandan Panda December 5, 2022 14/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG Feature extraction Figure: Processed ECG CBMS 2022 Abhinandan Panda December 5, 2022 15/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Policies Mining : Decision tree Features ECG intervals: PR, RR, QT, TpTe and RT Class: Healhty (H), Diabetic (diabetic, diabetic with cardiac complications) CBMS 2022 Abhinandan Panda December 5, 2022 16/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG Policies ECG1: If RR > 619 ms and PR > 127 ms and QT > 361 ms, then it is diabetes. ECG2: If RR > 619 ms and PR <= 127 ms, then it indicates diabetes. ECG3: When RR > 528 ms and RR <= 619 ms and RT > 297.5 ms, diabetes is present. ECG4: When RR > 619 ms and PR > 127 ms and PR <= 140 ms and QT <= 361 ms, it is diabetes. ECG5: If RR > 401 ms and RR <= 408 ms and RT <= 297.5 ms, then it indicates diabetes. CBMS 2022 Abhinandan Panda December 5, 2022 17/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Sub-policies Each ECG policy is a combination of multiple sub-policies. For example, to monitor policy ECG1, we monitor the intersection of the following sub-policies. PECG11: The RR interval of ECG should be less than or equal to 619 ms. PECG12: The PR interval of ECG should be less than or equal to 127 ms. PECG13: The QT interval of ECG should be less than or equal to 361 ms. CBMS 2022 Abhinandan Panda December 5, 2022 18/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG policy as Timed Automata l0 l1 l2 裡 R R, x := 0 裡R R, x > 619 R, x 619 裡 (a) Timed automata representing policy PECG11 l0 l1 l2 裡 P P, x := 0 裡R R, x > 127 R, x 127 裡 (b) Timed automata representing policy PECG12 l0 l1 l2 裡 Q Q, x := 0 裡Te T e, x > 361 T e, x 361 裡 (c) Timed automata representing policy PECG13 Figure: Timed automata representing ECG policies PECG11, PECG12 and PECG13 CBMS 2022 Abhinandan Panda December 5, 2022 19/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor Definition Consider a given property tw(裡) defining the property to monitor that is defined as TA A. Function M : tw(裡) D is a verification monitor for , where D = {T, F, CT, CF} and is defined as follows, with tw(裡) denoting the current observation (a finite timed word over the alphabet 裡): M() = 錚 錚 錚 錚 錚 錚 錚 T if tw(裡) : 揃 F if tw(裡) : 揃 霧 CT if tw(裡) : 揃 霧 CF if 霧 tw(裡) : 揃 Synthesis of RV monitor from policies formalized as timed automata following the approaches mentioned in [pinisetty2017, pinisetty2018, Bauer:2011]. CBMS 2022 Abhinandan Panda December 5, 2022 20/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Example: Input-output Behavior of a RV Monitor ECG trace : [ = (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) 揃 (R, 800)] Table: RV monitors behaviour for the policy ECG1 MECG1() (P, 30) CT (P, 30) 揃 (Q, 150) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) 揃 (R, 800) CF CBMS 2022 Abhinandan Panda December 5, 2022 21/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Sample excetion of ECG trace on RV monitor CBMS 2022 Abhinandan Panda December 5, 2022 22/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Experimental Results Accuracy(%) = TP + TN TP + TN + FP + FN 100 Sensitivity(%) = TP TP + FN 100 Specificity(%) = TN TN + FP 100 Accuracy Sensitivity Specificity RV framework 88.07% 89.36% 86.36% Table: RV framework Performance Table: Comparison with other works Authors Methods Accuracy Acharya et al. [7] Nonlinear 86.0 Jian et al. [8] Higher order spectrum 79.93 Acharya et al. [9] Discrete wavelet transform 92.02 Pachori et al. [10] Empirical mode decomposition 95.63 Swapna et al. [11] Deep learning (CNN-LSTM) 95.1 Our RV framework Policy based 88.07 CBMS 2022 Abhinandan Panda December 5, 2022 23/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor on Wearable Device Figure: RV monitor on wearable device [14] CBMS 2022 Abhinandan Panda December 5, 2022 24/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Conclusion & Future Works Conclusion Explainable health monitoring Correct by construction model Future work Testing with other datasets Analysing other ECG features Implementation on wearable device CBMS 2022 Abhinandan Panda December 5, 2022 25/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References I [1] Prateek Jain, Amit M Joshi, and Saraju Mohanty. Everything you wanted to know about noninvasive glucose measurement and control. In: arXiv preprint arXiv:2101.08996 (2021). [2] American Diabetes Association. Diagnosis and classification of diabetes mellitus. In: Diabetes care 37.Supplement_1 (2014), S81S90. [3] Pouya Saeedi et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. In: Diabetes research and clinical practice 157 (2019), p. 107843. [4] Jessica Beagley et al. Global estimates of undiagnosed diabetes in adults. In: Diabetes research and clinical practice 103.2 (2014), pp. 150160. [5] Melanie J Davies et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). In: Diabetes care 41.12 (2018), pp. 26692701. CBMS 2022 Abhinandan Panda December 5, 2022 26/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References II [6] Jukka Lipponen et al. Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects. In: Physiological measurement 32 (June 2011), pp. 64960. doi: 10.1088/0967-3334/32/6/003. [7] U Rajendra Acharya et al. An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. In: Computer methods in biomechanics and biomedical engineering 16.2 (2013), pp. 222234. [8] Lee Wei Jian and Teik-Cheng Lim. Automated detection of diabetes by means of higher order spectral features obtained from heart rate signals. In: Journal of medical imaging and health informatics 3.3 (2013), pp. 440447. [9] U Rajendra Acharya et al. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. In: Knowledge-based systems 81 (2015), pp. 5664. CBMS 2022 Abhinandan Panda December 5, 2022 27/28
Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References III [10] Ram Bilas Pachori et al. An improved online paradigm for screening of diabetic patients using RR-interval signals. In: Journal of Mechanics in Medicine and Biology 16.01 (2016), p. 1640003. [11] Goutham Swapna, Soman Kp, and Ravi Vinayakumar. Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. In: Procedia computer science 132 (2018), pp. 12531262. [12] Andreas Bauer, Martin Leucker, and Christian Schallhart. Runtime Verification for LTL and TLTL. In: ACM Trans. Softw. Eng. Methodol. 20.4 (Sept. 2011), 14:114:64. issn: 1049-331X. [13] Jiapu Pan and Willis J Tompkins. A real-time QRS detection algorithm. In: IEEE transactions on biomedical engineering 3 (1985), pp. 230236. [14] Srinivas Pinisetty et al. Runtime enforcement of cyber-physical systems. In: ACM Transactions on Embedded Computing Systems (TECS) 16.5s (2017), pp. 125. CBMS 2022 Abhinandan Panda December 5, 2022 28/28
Thank you!

More Related Content

CBMS_Presentation (5).pdf

  • 1. IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022) Policy-Based Diabetes Detection using Formal Runtime Verification Monitors Abhinandan Panda 1 , Srinivas Pinisetty1 , Partha Roop 2 1 Indian Institute of Technology, Bhubaneswar, India 2 University of Auckland, Auckland, New Zealand December 5, 2022
  • 2. Outline 1 Introduction 2 ECG 3 Proposed monitoring system 4 Policies Mining 5 RV Monitor 6 Experimental results & Comparison 7 Conclusion & Future Works 8 References
  • 3. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Introduction: Diabetes A metabolic disorder Lack of control or balance in blood glucose (BG) Type 1 diabetes (deficiency of insulin) / Type 2 diabetes (excess of insulin) Hyperglycemia (very high blood glucose levels) / hypoglycemia (very low blood glucose levels) events (normal blood glucose level 140 mg/dL). CBMS 2022 Abhinandan Panda December 5, 2022 1/28
  • 4. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Health Complications in Diabetes Figure: Health complications in diabetes [1] CBMS 2022 Abhinandan Panda December 5, 2022 2/28
  • 5. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Global Diabetic Growth Figure: Global diabetic growth [1] CBMS 2022 Abhinandan Panda December 5, 2022 3/28
  • 6. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Invasive Blood Glucose Monitoring Figure: Invasive blood glucose monitoring [1] CBMS 2022 Abhinandan Panda December 5, 2022 4/28
  • 7. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Need of non-invasive continuous monitoring of diabetes Severe health complications [2]. Around 9.3% of people are affected by diabetes globally [3] . Tedious initial screening process About 45.8% of diabetes cases with cardiac complications are untreated [4]. Continuous diabetes monitoring technique should be adopted [5] CBMS 2022 Abhinandan Panda December 5, 2022 5/28
  • 8. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Non-invasive approach: monitoring physiological signal ECG Time Amplitude S R Q P Q R S QRS interval P PR interval P-wave interval QT interval RT interval TpTe interval Te RR interval Tp Tp Figure: A typical ECG Signal CBMS 2022 Abhinandan Panda December 5, 2022 6/28
  • 9. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG in diabetes Figure: ECG in diabetes [6] CBMS 2022 Abhinandan Panda December 5, 2022 7/28
  • 10. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG and Diabetes: Analysis hypoglycemia (low blood glucose level) results in prolongation of QT interval [christensen2010]. Hypoglycemia (low blood glucose level) associated with increased heart rate (HR) [heger1996]. Hyperglycemia (high blood glucose level) related with reduced heart rate variability (HRV) [singh2000]. Corrected QT dispersion and PR interval have a significant change in hyperglycemia condition [marfella2000]. According to [nguyen2012], ECG parameters such as corrected QT interval, PR interval, corrected RT interval, corrected TpTe interval and heart rate (HR) can be used for identification of hypoglycemia and hyperglycemia detection. CBMS 2022 Abhinandan Panda December 5, 2022 8/28
  • 11. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Related Works Authors Methods Accuracy Acharya et al. [7] Nonlinear 86.0 Jian et al. [8] Higher order spectrum 79.93 Acharya et al. [9] Discrete wavelet transform 92.02 Pachori et al. [10] Empirical mode decomposition 95.63 Swapna et al. [11] Deep learning (CNN-LSTM) 95.1 CBMS 2022 Abhinandan Panda December 5, 2022 9/28
  • 12. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Motivation Deep learning-based models such as CNN, RNN, LSTM, BI-LSTM, GRU, etc. provide a good prediction accuracy, however, these models are "black-box". Monitor should not only be able to classify the input signals (ECG, PPG) accurately to access the condition of a patient but also the cause of the outcome should be explainable. To understand further the effect of physiological signal features on the outcome. There is an urge for explainable monitoring models in healthcare [reyes2020, gastounioti2020]. We propose a formal method-based framework that is correct by construction. We develop a formal runtime monitoring (RV) framework based on ECG sensing for diabetes monitoring. CBMS 2022 Abhinandan Panda December 5, 2022 10/28
  • 13. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Runtime Verification : Overview Figure: Overview of the monitor based verification process CBMS 2022 Abhinandan Panda December 5, 2022 11/28
  • 14. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor Definition Consider a given property tw(裡) defining the property to monitor that is defined as TA A. Function M : tw(裡) D is a verification monitor for , where D = {T, F, CT, CF} and is defined as follows, with tw(裡) denoting the current observation (a finite timed word over the alphabet 裡): M() = 錚 錚 錚 錚 錚 錚 錚 T if tw(裡) : 揃 F if tw(裡) : 揃 霧 CT if tw(裡) : 揃 霧 CF if 霧 tw(裡) : 揃 Correct by construction Satisfy impartiality and anticipation constraints (Bauer et al.[12]). CBMS 2022 Abhinandan Panda December 5, 2022 12/28
  • 15. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Proposed Monitoring Approach Raw ECG ECG intervals ECG Policy inference ECG Processing Module Data Mining Model ECG Dataset (Diabetic, Healthy) Class label (diabetes/healthy) Figure: Policy learning framework ECG events True / False (Diabetes detection) Inferred ECG Policies ECG signal ECG Sensor ECG Processing Module RV monitor Figure: Proposed monitoring framework CBMS 2022 Abhinandan Panda December 5, 2022 13/28
  • 16. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG and Diabetes Dataset Dataset We consider DICARDIA database [ledezma2014] of 65 subjects with: i) 51 diabetic subjects with cardiac complications of age 57.00 賊 10.00 years ii) 3 diabetic subjects without cardiac complications of age 49.00 賊 12.00 years iii) 11 healthy subjects as a control group of age 50.00 賊 6.00 years. iv) Approximately 30 min. long records. Signal Processing The ECG_Processing module is implemented in Python toolkit Neurokit2 [Makowski2021]. Apply a high pass Butterworth filter and a low pass filter to remove baseline drift and high-frequency noise from the ECG signal. The R-peaks in ECG are extracted using the Pan-Tompkins algorithm [13]. Wavelet analysis to detect the P-peaks, Q-peaks, R-peaks, T-peaks and T-ends of the ECG. CBMS 2022 Abhinandan Panda December 5, 2022 14/28
  • 17. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG Feature extraction Figure: Processed ECG CBMS 2022 Abhinandan Panda December 5, 2022 15/28
  • 18. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Policies Mining : Decision tree Features ECG intervals: PR, RR, QT, TpTe and RT Class: Healhty (H), Diabetic (diabetic, diabetic with cardiac complications) CBMS 2022 Abhinandan Panda December 5, 2022 16/28
  • 19. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG Policies ECG1: If RR > 619 ms and PR > 127 ms and QT > 361 ms, then it is diabetes. ECG2: If RR > 619 ms and PR <= 127 ms, then it indicates diabetes. ECG3: When RR > 528 ms and RR <= 619 ms and RT > 297.5 ms, diabetes is present. ECG4: When RR > 619 ms and PR > 127 ms and PR <= 140 ms and QT <= 361 ms, it is diabetes. ECG5: If RR > 401 ms and RR <= 408 ms and RT <= 297.5 ms, then it indicates diabetes. CBMS 2022 Abhinandan Panda December 5, 2022 17/28
  • 20. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Sub-policies Each ECG policy is a combination of multiple sub-policies. For example, to monitor policy ECG1, we monitor the intersection of the following sub-policies. PECG11: The RR interval of ECG should be less than or equal to 619 ms. PECG12: The PR interval of ECG should be less than or equal to 127 ms. PECG13: The QT interval of ECG should be less than or equal to 361 ms. CBMS 2022 Abhinandan Panda December 5, 2022 18/28
  • 21. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References ECG policy as Timed Automata l0 l1 l2 裡 R R, x := 0 裡R R, x > 619 R, x 619 裡 (a) Timed automata representing policy PECG11 l0 l1 l2 裡 P P, x := 0 裡R R, x > 127 R, x 127 裡 (b) Timed automata representing policy PECG12 l0 l1 l2 裡 Q Q, x := 0 裡Te T e, x > 361 T e, x 361 裡 (c) Timed automata representing policy PECG13 Figure: Timed automata representing ECG policies PECG11, PECG12 and PECG13 CBMS 2022 Abhinandan Panda December 5, 2022 19/28
  • 22. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor Definition Consider a given property tw(裡) defining the property to monitor that is defined as TA A. Function M : tw(裡) D is a verification monitor for , where D = {T, F, CT, CF} and is defined as follows, with tw(裡) denoting the current observation (a finite timed word over the alphabet 裡): M() = 錚 錚 錚 錚 錚 錚 錚 T if tw(裡) : 揃 F if tw(裡) : 揃 霧 CT if tw(裡) : 揃 霧 CF if 霧 tw(裡) : 揃 Synthesis of RV monitor from policies formalized as timed automata following the approaches mentioned in [pinisetty2017, pinisetty2018, Bauer:2011]. CBMS 2022 Abhinandan Panda December 5, 2022 20/28
  • 23. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Example: Input-output Behavior of a RV Monitor ECG trace : [ = (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) 揃 (R, 800)] Table: RV monitors behaviour for the policy ECG1 MECG1() (P, 30) CT (P, 30) 揃 (Q, 150) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) CT (P, 30) 揃 (Q, 150) 揃 (R, 155) 揃 (Te, 500) 揃 (R, 800) CF CBMS 2022 Abhinandan Panda December 5, 2022 21/28
  • 24. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Sample excetion of ECG trace on RV monitor CBMS 2022 Abhinandan Panda December 5, 2022 22/28
  • 25. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Experimental Results Accuracy(%) = TP + TN TP + TN + FP + FN 100 Sensitivity(%) = TP TP + FN 100 Specificity(%) = TN TN + FP 100 Accuracy Sensitivity Specificity RV framework 88.07% 89.36% 86.36% Table: RV framework Performance Table: Comparison with other works Authors Methods Accuracy Acharya et al. [7] Nonlinear 86.0 Jian et al. [8] Higher order spectrum 79.93 Acharya et al. [9] Discrete wavelet transform 92.02 Pachori et al. [10] Empirical mode decomposition 95.63 Swapna et al. [11] Deep learning (CNN-LSTM) 95.1 Our RV framework Policy based 88.07 CBMS 2022 Abhinandan Panda December 5, 2022 23/28
  • 26. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References RV Monitor on Wearable Device Figure: RV monitor on wearable device [14] CBMS 2022 Abhinandan Panda December 5, 2022 24/28
  • 27. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References Conclusion & Future Works Conclusion Explainable health monitoring Correct by construction model Future work Testing with other datasets Analysing other ECG features Implementation on wearable device CBMS 2022 Abhinandan Panda December 5, 2022 25/28
  • 28. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References I [1] Prateek Jain, Amit M Joshi, and Saraju Mohanty. Everything you wanted to know about noninvasive glucose measurement and control. In: arXiv preprint arXiv:2101.08996 (2021). [2] American Diabetes Association. Diagnosis and classification of diabetes mellitus. In: Diabetes care 37.Supplement_1 (2014), S81S90. [3] Pouya Saeedi et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. In: Diabetes research and clinical practice 157 (2019), p. 107843. [4] Jessica Beagley et al. Global estimates of undiagnosed diabetes in adults. In: Diabetes research and clinical practice 103.2 (2014), pp. 150160. [5] Melanie J Davies et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). In: Diabetes care 41.12 (2018), pp. 26692701. CBMS 2022 Abhinandan Panda December 5, 2022 26/28
  • 29. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References II [6] Jukka Lipponen et al. Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects. In: Physiological measurement 32 (June 2011), pp. 64960. doi: 10.1088/0967-3334/32/6/003. [7] U Rajendra Acharya et al. An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. In: Computer methods in biomechanics and biomedical engineering 16.2 (2013), pp. 222234. [8] Lee Wei Jian and Teik-Cheng Lim. Automated detection of diabetes by means of higher order spectral features obtained from heart rate signals. In: Journal of medical imaging and health informatics 3.3 (2013), pp. 440447. [9] U Rajendra Acharya et al. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. In: Knowledge-based systems 81 (2015), pp. 5664. CBMS 2022 Abhinandan Panda December 5, 2022 27/28
  • 30. Introduction ECG Proposed monitoring system Policies Mining RV Monitor Experimental results & Comparison Conclusion & Future Works References References References III [10] Ram Bilas Pachori et al. An improved online paradigm for screening of diabetic patients using RR-interval signals. In: Journal of Mechanics in Medicine and Biology 16.01 (2016), p. 1640003. [11] Goutham Swapna, Soman Kp, and Ravi Vinayakumar. Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. In: Procedia computer science 132 (2018), pp. 12531262. [12] Andreas Bauer, Martin Leucker, and Christian Schallhart. Runtime Verification for LTL and TLTL. In: ACM Trans. Softw. Eng. Methodol. 20.4 (Sept. 2011), 14:114:64. issn: 1049-331X. [13] Jiapu Pan and Willis J Tompkins. A real-time QRS detection algorithm. In: IEEE transactions on biomedical engineering 3 (1985), pp. 230236. [14] Srinivas Pinisetty et al. Runtime enforcement of cyber-physical systems. In: ACM Transactions on Embedded Computing Systems (TECS) 16.5s (2017), pp. 125. CBMS 2022 Abhinandan Panda December 5, 2022 28/28