Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

<div>Objective: Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation performed by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, depending on the selected data-model, the adopted estimation scheme (least-squares error, maximum likelihood, or Bayesian), and the prior assumptions on the model parameters and noise distributions, any estimation algorithm used in this context has an upper performance bound, which is not exceedable (for the same model and assumptions).</div><div><br></div><div>Method: In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature; namely bases expansions (including polynomials) and sum of Gaussian functions.</div><div><br></div><div>Results: The developed framework is evaluated over real and synthetic data, for three popular applications: T/R ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context, and compared with the derived theoretical CRLBs.</div><div>Conclusion and Significance: The proposed framework and the derived CRLBs provide fact-based guidelines for the selection of data-models, sampling frequency (beyond the Nyquist requirements), modeling segment length, the number of beats required for average ECG beat extraction, and other factors that influence the accuracy of ECG-based clinical parameter estimation.</div>

Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

<div>Objective: Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation performed by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, depending on the selected data-model, the adopted estimation scheme (least-squares error, maximum likelihood, or Bayesian), and the prior assumptions on the model parameters and noise distributions, any estimation algorithm used in this context has an upper performance bound, which is not exceedable (for the same model and assumptions).</div><div><br></div><div>Method: In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature; namely bases expansions (including polynomials) and sum of Gaussian functions.</div><div><br></div><div>Results: The developed framework is evaluated over real and synthetic data, for three popular applications: T/R ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context, and compared with the derived theoretical CRLBs.</div><div>Conclusion and Significance: The proposed framework and the derived CRLBs provide fact-based guidelines for the selection of data-models, sampling frequency (beyond the Nyquist requirements), modeling segment length, the number of beats required for average ECG beat extraction, and other factors that influence the accuracy of ECG-based clinical parameter estimation.</div>

Theoretical Performance Bounds of Model-Based Electrocardiogram Parameter Estimation

<div>Objective: Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation performed by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, depending on the selected data-model, the adopted estimation scheme (least-squares error, maximum likelihood, or Bayesian), and the prior assumptions on the model parameters and noise distributions, any estimation algorithm used in this context has an upper performance bound, which is not exceedable (for the same model and assumptions).</div><div><br></div><div>Method: In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature; namely bases expansions (including polynomials) and sum of Gaussian functions.</div><div><br></div><div>Results: The developed framework is evaluated over real and synthetic data, for three popular applications: T/R ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context, and compared with the derived theoretical CRLBs.</div><div>Conclusion and Significance: The proposed framework and the derived CRLBs provide fact-based guidelines for the selection of data-models, sampling frequency (beyond the Nyquist requirements), modeling segment length, the number of beats required for average ECG beat extraction, and other factors that influence the accuracy of ECG-based clinical parameter estimation.</div>

Formal requirements modeling for cyber-physical systems engineering: an integrated solution based on FORM-L and Modelica

The increasing complexity of cyber-physical systems (CPSs) makes their design, development and operation extremely challenging. Due to the nature of CPS that involves many heterogeneous components, which are often designed and developed by organizations belonging to different engineering domains, it is difficult to manage, trace and verify their properties, requirements and constraints throughout their lifecycle by using classical techniques. In this context, the paper presents an integrated solution to formally define system requirements and automate their verification through simulation. The solution is based on the FOrmal Requirements Modeling Language and the Modelica language. The solution is exemplified through two case studies concerning a Trailing-Edge High-Lift system and a Heating, Ventilation and Air Conditioning system.

Leveraging SPARQL Queries for UML Consistency Checking

Context and motivation: Multiple-viewed requirements modeling method describes the system to-be from different perspectives. Some requirements models are then specified in various UML diagrams. Question/problem: Managing those models can be tedious and error-prone, since a lot of CASE tools provide poor support for reasoning and consistency checking. Principal ideas/results: Ontology is a formal notation for describing concepts and their relations in a domain. Since software requirements are a kind of knowledge, we propose to adopt a knowledge engineering approach for managing the consistency of requirements models. In this paper, an ontology for three most commonly used UML diagrams is developed in Web Ontology Language (OWL). The transformation of UML class, sequence and state diagrams to OWL knowledge base is presented. Owing to the underlying logical reasoning capability of OWL, a semantic query language, SPARQL (SPARQL Protocol and RDF Query Language), is used to query the knowledge base for consistency checking. Contribution: This paper introduces a semantic web-based knowledge engineering approach to represent and manage software requirements knowledge in OWL. By experimenting with a concrete software system, we demonstrate the feasibility and applicability of this knowledge approach.

Automated Linescan Analysis for CMP Modeling

Many of the process steps used in semiconductor chip manufacturing require planar (smooth) surfaces on the wafer to ensure correct pattern printing and generation of multilevel interconnections in the chips during manufacturing. Chemical-mechanical polishing/planarization (CMP) is the primary process used to achieve these surface planarity requirements. Modeling of CMP processes allows users to detect and fix large surface planarity variations (hotspots) in the layout prior to manufacturing. Fixing hotspots before tape-out may significantly reduce turnaround time and the cost of manufacturing. Creating an accurate CMP model that takes into account complicated chemical and mechanical polishing mechanisms is challenging. Measured data analysis and extraction of erosion and dishing data from profile linescans from test chips are important steps in CMP model building. Measured linescans are often tilted and noisy, which makes the extraction of erosion and dishing data more difficult. The development and implementation of algorithms used to perform automated linescan analysis may significantly reduce CMP model building time and improve the accuracy of the models. In this work, an automated linescan analysis (ALSA) tool is presented that performs automated linescan delineation, test pattern separation, and automatic extraction of erosion and dishing values from linescan data.

RationalGRL: A framework for argumentation and goal modeling

Goal-oriented requirements modeling approaches aim to capture the intentions of the stakeholders involved in the development of an information system as goals and tasks. The process of constructing such goal models usually involves discussions between a requirements engineer and a group of stakeholders. Not all the arguments in such discussions can be captured as goals or tasks: e.g., the discussion whether to accept or reject a certain goal and the rationale for acceptance or rejection cannot be captured in goal models. In this paper, we apply techniques from computational argumentation to a goal modeling approach by using a coding analysis in which stakeholders discuss requirements for a Traffic Simulator. We combine a simplified version of a traditional goal model, the Goal-oriented Requirements Language (GRL), with ideas from argumentation on schemes for practical reasoning into a new framework (RationalGRL). RationalGRL provides a formal semantics and tool support to capture the discussions and outcomes of the argumentation process that leads to a goal model. We also define the RationalGRL development process to create a RationalGRL model.