The Artificial Pacemaker: A Historical Overview

Gomes, J. Anthony

doi:10.1007/978-3-030-45066-3_31

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Over sixty years of research and development in designing artificial cardiac pacemakers has resulted in remarkable robustness, convenience, and acceptance of these devices. As the number of patients using pacemakers continues to rise (Greenspon et al 2012), so are the expectations on the invisibility of its design resulting in ever-increasing demands on improved functionality, size shrinkage, power consumption, battery performance and adaptability to individual cardiac arrhythmias (Gomes 2020;Finkelmeier 1991). The medical device industry continues to meet these challenges by extending its design with more sensors (e.g., for rate augmentation, sleep apnea, and haemodynamic status) (Lau, Siu, and Tse 2017) resulting in ever-increasing complexity of the device software.…”

Section: Introductionmentioning

confidence: 99%

Correct-by-Construction Reinforcement Learning of Cardiac Pacemakers from Duration Calculus Requirements

Dole

Gupta

Komp

et al. 2023

AAAI

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As the complexity of pacemaker devices continues to grow, the importance of capturing its functional correctness requirement formally cannot be overestimated. The pacemaker system specification document by \emph{Boston Scientific} provides a widely accepted set of specifications for pacemakers. As these specifications are written in a natural language, they are not amenable for automated verification, synthesis, or reinforcement learning of pacemaker systems. This paper presents a formalization of these requirements for a dual-chamber pacemaker in \emph{duration calculus} (DC), a highly expressive real-time specification language. The proposed formalization allows us to automatically translate pacemaker requirements into executable specifications as stopwatch automata, which can be used to enable simulation, monitoring, validation, verification and automatic synthesis of pacemaker systems. The cyclic nature of the pacemaker-heart closed-loop system results in DC requirements that compile to a decidable subclass of stopwatch automata. We present shield reinforcement learning (shield RL), a shield synthesis based reinforcement learning algorithm, by automatically constructing safety envelopes from DC specifications.

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Section: Introductionmentioning

confidence: 99%