Eligibility for subcutaneous implantable cardiac defibrillator utilising artificial intelligence and deep learning methods for prolonged screening: where is the cut-off?

Funding Acknowledgements:Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Main author is receiving an unrestricted grant by Boston ScientificBackground: S-ICD eligibility is determined by a single surface ECG analysis in which the suitability of an individual’s ECG vector morphology is assessed. A major predictor of eligibility is the T:R ratio. Current screening tools proposes T: R of 1:3 as a cut-off for eligibility. Inappropriate shocks due to T-wave oversensing (TWO) remains an issue despite screening. EFFORTLESS and PRAETORIAN trials reported inappropriate shock rates of 11.4% and 9.7% respectively, most frequently caused by cardiac oversensing.Purpose: The cut-off T: R of 1:3 currently used incorporates a safety margin to accommodate for ECG signal amplitudes fluctuations without affecting S-ICD sensing. Prolonged screening using our tool accurately measures the T: R fluctuations. However, utilising a T: R of 1:3 for prolonged screening can unnecessarily exclude appropriate S-ICD candidates. The purpose of our study is to provide groundwork for future trials to find the optimal ratio that identifies patients at risk of TWO and inappropriate shocks while not excluding true S-ICD candidates after prolonged screening.Methods: Patients were fitted with 24-hour Holter monitors with leads placed to correspond to the vectors of an S-ICD. We used our tool to assess T: R over the recordings utilising Phase Space Reconstruction matrices - to convert the ECG signal into compressed pixel images. A Convolutional Neural Network (CNN) model was trained to accurately predict the T: R from these images resulting in a T: R variation plot for each vector. We then applied multiple T:R ratio cut-offs on the recordings to identify patients at risk of inappropriate shocks due to TWO at each proposed value. A vector with a T: R above the cut-off for 20 consecutive seconds was deemed to have failed screening, the time determined by the current detection, charge, and redetection time of the current S-ICD system. A patient has to have at least one suitable vector to pass the screening at the selected threshold.Results: 37 patients (mean age 54.5 years,64.8% male) were included. 14 had Heart failure, 7 Hypertrophic cardiomyopathy, 7 normal hearts, 6 Adult congenital heart disease and 3 patients who received inappropriate S-ICD shocks due to TWO. Overall, 20 (54%) of patients passed prolonged screening using a 1:3 ratio. All of the patients passed screening with a T: R of 1:1. The only subgroup to wholly pass the screening for all the proposed ratios are the normal hearts group.Conclusion: We propose adopting prolonged screening to select S-ICD eligible patients with low probability of TWO and inappropriate shocks. However, utilising T: R of 1:3 can unnecessarily exclude otherwise S-ICD eligible patients. The appropriate ratio likely lies between 1:3 - 1:1. Further studies are needed to identify the optimal screening thresholds, particularly in patients that have had inappropriate shocks due to TW

Eligibility for subcutaneous implantable cardiac defibrillator utilising artificial intelligence and deep learning methods for prolonged screening: where is the cut-off?

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Main author is receiving an unrestricted grant by Boston Scientific Background S-ICD eligibility is determined by a single surface ECG analysis in which the suitability of an individual’s ECG vector morphology is assessed. A major predictor of eligibility is the T:R ratio. Current screening tools proposes T: R of 1:3 as a cut-off for eligibility. Inappropriate shocks due to T-wave oversensing (TWO) remains an issue despite screening. EFFORTLESS and PRAETORIAN trials reported inappropriate shock rates of 11.4% and 9.7% respectively, most frequently caused by cardiac oversensing. Purpose The cut-off T: R of 1:3 currently used incorporates a safety margin to accommodate for ECG signal amplitudes fluctuations without affecting S-ICD sensing. Prolonged screening using our tool accurately measures the T: R fluctuations. However, utilising a T: R of 1:3 for prolonged screening can unnecessarily exclude appropriate S-ICD candidates. The purpose of our study is to provide groundwork for future trials to find the optimal ratio that identifies patients at risk of TWO and inappropriate shocks while not excluding true S-ICD candidates after prolonged screening. Methods Patients were fitted with 24-hour Holter monitors with leads placed to correspond to the vectors of an S-ICD. We used our tool to assess T: R over the recordings utilising Phase Space Reconstruction matrices - to convert the ECG signal into compressed pixel images. A Convolutional Neural Network (CNN) model was trained to accurately predict the T: R from these images resulting in a T: R variation plot for each vector. We then applied multiple T:R ratio cut-offs on the recordings to identify patients at risk of inappropriate shocks due to TWO at each proposed value. A vector with a T: R above the cut-off for 20 consecutive seconds was deemed to have failed screening, the time determined by the current detection, charge, and redetection time of the current S-ICD system. A patient has to have at least one suitable vector to pass the screening at the selected threshold. Results 37 patients (mean age 54.5 years,64.8% male) were included. 14 had Heart failure, 7 Hypertrophic cardiomyopathy, 7 normal hearts, 6 Adult congenital heart disease and 3 patients who received inappropriate S-ICD shocks due to TWO. Overall, 20 (54%) of patients passed prolonged screening using a 1:3 ratio. All of the patients passed screening with a T: R of 1:1. The only subgroup to wholly pass the screening for all the proposed ratios are the normal hearts group. Conclusion We propose adopting prolonged screening to select S-ICD eligible patients with low probability of TWO and inappropriate shocks. However, utilising T: R of 1:3 can unnecessarily exclude otherwise S-ICD eligible patients. The appropriate ratio likely lies between 1:3 - 1:1. Further studies are needed to identify the optimal screening thresholds, particularly in patients that have had inappropriate shocks due to TWO. </jats:sec

The use of artificial intelligence and deep learning methods in subcutaneous implantable cardioverter defibrillator screening to optimise selection in special patient populations

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Dr.Mohamed ElRefai is receiving an unrestricted grant from Boston Scientific. Introduction Adult congenital heart disease (ACHD) and hypertrophic cardiomyopathy (HCM) patients who require defibrillator therapy are often relatively young and may require several generator replacements in their lifetime. The increased risk of complications associated with transvenous ICDs make the subcutaneous (S-ICD) a valuable alternative. However, higher S-ICD ineligibility rates (20-40% in ACHD and 7-38% in HCM) and higher inappropriate shock rates (10.5% in ACHD and 12.5% in HCM) are observed in these populations. Unfavourable T:R ratios and dynamic changes in the R and T wave amplitudes are the primarily factors behind ineligibility and inappropriate shocks, which are most commonly caused by T wave over-sensing. Purpose We report a novel application of deep learning methods used to autonomously screen patients for S-ICD eligibility over a longer period than conventional screening. We hypothesise that this screening approach might achieve better patient selection and optimise S-ICD vector selection in challenging patient cohorts. Methods Adult patients with ACHD or HCM and a control group of normal subjects were fitted with a 24-hour ambulatory ECG with the leads placed to record their S-ICD vectors. T: R ratio throughout the recordings was analysed utilising phase space reconstruction matrices to convert the ECG signal into compressed pixel images. Whilst a convolutional neural network model was trained to provide an in-depth description of the T: R variation plot for each vector T: R variation was compared statistically using a one-way ANOVA test. Results 20 patients (age 44.1 ±11.68, 60% male, 7 HCM, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the three groups (p&amp;lt;0.001). There was no difference observed in the standard deviation of T: R between the control subjects and HCM group. However, there was a statistically significant difference in the standard deviation of T: R between the control subjects and the ACHD group (p= 0.01). [see Figure]. Conclusions T:R ratio, a main determinant for S-ICD eligibility, is significantly higher in ACHD and HCM when compared to normal hearts and it also has more tendency to fluctuate in ACHD patients when compared to HCM and normal hearts populations. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio reducing the risk of T wave oversensing and inappropriate shocks particularly in the ACHD patients’ cohort. </jats:sec

Two-Layer Deception Model Based on Signaling Games Against Cyber Attacks on Cyber-Physical Systems

Cyber-physical systems (CPS) are increasingly vulnerable to sophisticated cyber-attacks that can target multiple layers within the system. To strengthen defenses against these complex threats, deception-based techniques have emerged as a promising solution. While previous research has primarily focused on single-layer deception strategies, the authors argue that a multi-layer approach is essential for effectively countering advanced attackers capable of perceiving information across both the application and network layers. In this work, we propose a two-layer deception model based on signaling games to enhance the defense of CPS. Our model captures the dynamic, non-cooperative interactions between the attacker and defender under conditions of incomplete information. Unlike existing approaches, our model expands the defender&#x2019;s action space to incorporate deception at both the application and network layers, while maintaining the attacker&#x2019;s uncertainty about the true system type. Through analytical and simulation results, we identify the Perfect Bayesian Nash Equilibrium (PBNE) strategies for both players. Our findings demonstrate that the two-layer deception model significantly outperforms single-layer deception in deceiving the attacker and improving system resilience, particularly against sophisticated adversaries capable of perceiving information across multiple layers

The use of artificial intelligence and deep learning methods in subcutaneous implantable cardioverter defibrillator screening to optimise selection in special patient populations

Funding Acknowledgements: Type of funding sources: Private company. Main funding source(s): Dr.Mohamed ElRefai is receiving an unrestricted grant from Boston Scientific.Introduction: Adult congenital heart disease (ACHD) and hypertrophic cardiomyopathy (HCM) patients who require defibrillator therapy are often relatively young and may require several generator replacements in their lifetime. The increased risk of complications associated with transvenous ICDs make the subcutaneous (S-ICD) a valuable alternative. However, higher S-ICD ineligibility rates (20-40% in ACHD and 7-38% in HCM) and higher inappropriate shock rates (10.5% in ACHD and 12.5% in HCM) are observed in these populations. Unfavourable T:R ratios and dynamic changes in the R and T wave amplitudes are the primarily factors behind ineligibility and inappropriate shocks, which are most commonly caused by T wave over-sensing.Purpose: We report a novel application of deep learning methods used to autonomously screen patients for S-ICD eligibility over a longer period than conventional screening. We hypothesise that this screening approach might achieve better patient selection and optimise S-ICD vector selection in challenging patient cohorts.Methods: Adult patients with ACHD or HCM and a control group of normal subjects were fitted with a 24-hour ambulatory ECG with the leads placed to record their S-ICD vectors. T: R ratio throughout the recordings was analysed utilising phase space reconstruction matrices to convert the ECG signal into compressed pixel images. Whilst a convolutional neural network model was trained to provide an in-depth description of the T: R variation plot for each vector T: R variation was compared statistically using a one-way ANOVA test.Results: 20 patients (age 44.1 ±11.68, 60% male, 7 HCM, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the three groups (p&lt;0.001). There was no difference observed in the standard deviation of T: R between the control subjects and HCM group. However, there was a statistically significant difference in the standard deviation of T: R between the control subjects and the ACHD group (p= 0.01). [see Figure].Conclusions: T:R ratio, a main determinant for S-ICD eligibility, is significantly higher in ACHD and HCM when compared to normal hearts and it also has more tendency to fluctuate in ACHD patients when compared to HCM and normal hearts populations. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio reducing the risk of T wave oversensing and inappropriate shocks particularly in the ACHD patients’ cohor