Towards Closed-Loop Neuromodulation: AI Prediction of Ventricular Tachyarrhythmias from Multimodal Biosignals in a Porcine Ischemia Model

Myocardial ischemia hyperactivates cardiac sensory neurons, disrupting autonomic balance through excessive sympathetic activity and predisposing the heart to fatal ventricular tachyarrhythmias (VTs). Neuromodulation therapies, including spinal cord stimulation, can suppress ischemia-induced sympathoexcitation. However, open-loop neuromodulation techniques lack adaptive control, leading to suboptimal precision, reduced long-term efficacy, and potential side effects. Closed-loop systems require reliable biofeedback to anticipate autonomic dysfunction and arrhythmic risk. In this study, we evaluate whether epicardial mapping, surface ECG, and arterial blood pressure can provide robust biofeedback signals for predicting VT incidence. Anesthetized Yorkshire pigs (n=12) were subjected to 1 hour of left anterior descending coronary artery ischemia, during which we recorded epicardial electrograms, surface ECG, and blood pressure. To confirm the increased sympathoexcitation and arrhythmogenicity during LAD ischemia, we measured the activation recovery interval, a surrogate for action potential duration, and the dispersion of repolarization. Additionally, we assessed the arrhythmia incidence by identifying the VT episodes. An AI method was then used to assess whether VT could be predicted using 20-second data segments leading to VT. During LAD ischemia, we observed shortened ARIs, increased DORs, and elevated arrhythmia scores, confirming the presence of sympathoexcitation and arrhythmogenicity. The AI model demonstrated a sensitivity of 0.774 and a specificity of 0.770, with a positive predictive value of 0.632 and a negative predictive value of 0.870. This study suggests that combining epicardial electrograms, surface ECG, and blood pressure may provide reliable inputs for an AI-assisted closed-loop system capable of predicting VT onset and triggering neuromodulation therapy before arrhythmia develops.

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Published
2025-09-26
DOI
https://doi.org/10.33774/coe-2025-fs2vw
Primary Topic
Cardiac electrophysiology and arrhythmias
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article
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article

Towards Closed-Loop Neuromodulation: AI Prediction of Ventricular Tachyarrhythmias from Multimodal Biosignals in a Porcine Ischemia Model

Ali Bahrami Rad, Siamak Salavatian, Yuki Kuwabara
Cardiac electrophysiology and arrhythmias
article

Towards Closed-Loop Neuromodulation: AI Prediction of Ventricular Tachyarrhythmias from Multimodal Biosignals in a Porcine Ischemia Model

Ali Bahrami Rad, Siamak Salavatian, Yuki Kuwabara
article en

Abstract

Myocardial ischemia hyperactivates cardiac sensory neurons, disrupting autonomic balance through excessive sympathetic activity and predisposing the heart to fatal ventricular tachyarrhythmias (VTs). Neuromodulation therapies, including spinal cord stimulation, can suppress ischemia-induced sympathoexcitation. However, open-loop neuromodulation techniques lack adaptive control, leading to suboptimal precision, reduced long-term efficacy, and potential side effects. Closed-loop systems require reliable biofeedback to anticipate autonomic dysfunction and arrhythmic risk. In this study, we evaluate whether epicardial mapping, surface ECG, and arterial blood pressure can provide robust biofeedback signals for predicting VT incidence. Anesthetized Yorkshire pigs (n=12) were subjected to 1 hour of left anterior descending coronary artery ischemia, during which we recorded epicardial electrograms, surface ECG, and blood pressure. To confirm the increased sympathoexcitation and arrhythmogenicity during LAD ischemia, we measured the activation recovery interval, a surrogate for action potential duration, and the dispersion of repolarization. Additionally, we assessed the arrhythmia incidence by identifying the VT episodes. An AI method was then used to assess whether VT could be predicted using 20-second data segments leading to VT. During LAD ischemia, we observed shortened ARIs, increased DORs, and elevated arrhythmia scores, confirming the presence of sympathoexcitation and arrhythmogenicity. The AI model demonstrated a sensitivity of 0.774 and a specificity of 0.770, with a positive predictive value of 0.632 and a negative predictive value of 0.870. This study suggests that combining epicardial electrograms, surface ECG, and blood pressure may provide reliable inputs for an AI-assisted closed-loop system capable of predicting VT onset and triggering neuromodulation therapy before arrhythmia develops.

Emory University (US), University of Pittsburgh (US)
Openalex Percentile: Top 60%
Cardiac electrophysiology and arrhythmias
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