Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators

Bàrdossy, András; Blinowska, A.; Kuźmicz, Wiesław; Ollitrault, J.; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew

doi:10.1016/j.artmed.2013.12.004

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…The presented below the fuzzy logic-based heart rhythm algorithmic analysis is an example of an AI implementation in medicine due to its optimization and self-learning properties [46]. If this project is successful, its results will become a foundation for a citywide and countrywide rapid rescue system for people at risk of SCA.…”

Section: Artificial Intelligence In Medicine and Sca Managementmentioning

confidence: 99%

“…Smart implants (Sis) are medically implantable devices with sensors which assess the sensory input and decide on a response. An active Si is made up of biosensors, a wireless module enabling telemetry, and a miniaturized computer [46]. Examples of Sis are cardiac electronic implantable devices (pacemakers, ICDs, S-ICDs, cardiac resynchronization therapy devices) and ILRs which constantly trace the heart rhythm.…”

Section: Smart Implantsmentioning

confidence: 99%

“…The receiver then communicates via a wired network with the server, which saves long-term data. The possibilities for the communication between IMDs and/or wearable medical devices are as follows: WLAN (wireless local area network), WPAN (wireless personal area network), LR-WPAN (low-rate wireless personal area network) and WBAN (wireless body area network) [46]. The first three are more suitable for on-body communication, while the latter is better for in-body communication.…”

Section: Smart Implantsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

Lewandowski

2021

Micromachines

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Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project “PROTECTOR”, the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).

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Section: Artificial Intelligence In Medicine and Sca Managementmentioning

confidence: 99%

Section: Smart Implantsmentioning

confidence: 99%

Section: Smart Implantsmentioning

confidence: 99%

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A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

Lewandowski

2021

Micromachines

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“…Generally, an FLS consists of a rule base with rules associated with particular regions, where the information available is transparent and easily readable. This characteristic of fuzzy systems has been employed in many fields including medical (Bárdossy et al, 2014;Razak et al, 2013), engineering (Gad & Farooq, 2001), decision support (O. W. Samuel, Omisore, & Ojokoh, 2013), pattern recognition (Pedrycz, 1990) and others.…”

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Towards Designing A Hierarchical Fuzzy System for Early Diagnosis of Heart Disease

Razak

Mahadi

Halim

et al. 2019

JCRINN

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Heart disease may represent a range of conditions that affect our heart. Disease under heart diseases umbrella include coronary heart disease, heart attack, congestive heart failure, and congenital heart disease, is the leading cause of death. Mor eover, heart disease not only attacks the elderly. In the present day, lots of younger people might be getting affected by the number of heart diseases. In order to decrease the mortality rate caused by heart disease, it is necessary for the disease, to be diagnosed at an early stage. In this paper, we have proposed the use of hierarchical fuzzy systems (HFSs) for early diagnosis of heart disease. However, to design the HFSs is challenging, especially for the complex system. Therefore, in this paper, we foc us on designing a hierarchical fuzzy system to handle the complex medical application. The designed HFS consists of six key main steps implemented on heart disease. The input variables of heart disease includes shortness of breath, discomfort, pressure, he aviness, or pain in the chest, arm, or below the breastbone, fatigue, nausea, difficulties in climbing stairs, swelling in ankles, difficulty to sleep at night, irregular heartbeats, fullness, sweating, take frequent break during the day, dizzy and depress ed. Additionally, the output of heart disease is to classify whether the patient is healthy or suspecting with heart disease. The study contributes to providing insight into a way of designing the HFSs, particularly for the complex medical application.

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“…Fuzzy logic systems (FLSs) have been utilised successfully in a variety of disciplines, including research, industry, manufacturing, and business. They're also used in the medical industry for decision-making, particularly when dealing with ambiguity and inaccurate data [4]- [7]. The curse of dimensionality, on the other hand, is a fundamental drawback of traditional fuzzy systems: the number of needed rules rises exponentially with the number of input variables [8].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Fuzzy Systems: Interpretability and Complexity

Razak

Fauzi

Gining

et al. 2021

IJEEI

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Rule explosion related to an increase in the number of input variables has been recognised as a key issue in fuzzy logic systems (FLSs), and hierarchical fuzzy systems (HFSs) have been proposed as a viable solution. The typical FLS subsystem system is transformed into a low-dimensional FLS subsystem network in HFS. Furthermore, because the number of input variables in each subsystem is reduced, the rules in HFS generally contain antecedents with fewer variables than the rules in regular FLS with similar functions. As a result, HFSs can reduce rule explosion, reducing model complexity and improving model interpretability. Nonetheless, the concerns concerning the issue of "Does reducing the complexity of HFSs with various subsystems, layers, and diverse topologies actually increase their interpretability?" remain unclear. In this study, we compare two HFS topologies: parallel and serial, concentrating on interpretability and complexity. For both topologies, a full measurement of the interpretability and complexity with various configurations is presented. The goal of this comparative study is to see if there is a correlation between HFS interpretability and complexity.

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Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators

Cited by 20 publications

References 32 publications

A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

Towards Designing A Hierarchical Fuzzy System for Early Diagnosis of Heart Disease

Hierarchical Fuzzy Systems: Interpretability and Complexity

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