Electrocardiogram diagnosis using wavelet-based artificial neural network

Chen, Kun-Chih Jimmy; Ni, Yu-Shu; Wang, Jhao-Yi

doi:10.1109/gcce.2016.7800547

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…In the preceding studies, quantifying respiratory data is generally limited to analyzing the pronounced features of the signal such as inhales, exhales, and breathing rate-discarding the many other features present in these waveforms. Relative to the electrocardiogram (EKG), where research has precisely defined each component of the waveform (Ponikowski et al 2016), established standard methods to detect them (Addison 2005), and used this information to predict cardiovascular disease (Václavík et al 2014;Chen et al 2016), the tools and methodology available for interpreting respiratory signals have been relatively limited. Several manuscripts mention a need for more sophisticated respiratory signal processing methods to advance our understanding of respiration and its clinical applications (Boiten et al 1994;Folke et al 2003;Van Duinen et al 2010;Vlemincx et al 2011;Meredith et al 2012;Grassmann et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox

et al. 2018

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Nasal inhalation is the basis of olfactory perception and drives neural activity in olfactory and limbic brain regions. Therefore, our ability to investigate the neural underpinnings of olfaction and respiration can only be as good as our ability to characterize features of respiratory behavior. However, recordings of natural breathing are inherently nonstationary, nonsinusoidal, and idiosyncratic making feature extraction difficult to automate. The absence of a freely available computational tool for characterizing respiratory behavior is a hindrance to many facets of olfactory and respiratory neuroscience. To solve this problem, we developed BreathMetrics, an open-source tool that automatically extracts the full set of features embedded in human nasal airflow recordings. Here, we rigorously validate BreathMetrics' feature estimation accuracy on multiple nasal airflow datasets, intracranial electrophysiological recordings of human olfactory cortex, and computational simulations of breathing signals. We hope this tool will allow researchers to ask new questions about how respiration relates to body, brain, and behavior.

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Section: Introductionmentioning

confidence: 99%

Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox

et al. 2018

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“…LSTM neural networks [83] Add fault identification methods. ART neural networks [84] It enhanced signal extraction capabilities. BAM neural networks [85] Remove redundant failure data.…”

Section: Table ⅲ Ait Diagnostic Methods Statistics Diagnostic Methodsmentioning

confidence: 99%

“…I A PVA fault diagnosis model based on an LSTM neural network is proposed in [83], which studies the influence of different fault states on the output characteristics of PVS through simulation and then obtains fault characteristic parameters. Reference [84] develops a PV fault diagnosis method based on an improved adaptive resonance theory (ART) neural network, which characterizes different fault characteristics using a matrix, allowing for the accurate diagnosis of PVS faults. In addition, reference [85] investigates a fault diagnosis method that integrates the theory of the fault tree (FT) and bidirectional associative memory (BAM) neural network.…”

Section: ) Neural Network Methodsmentioning

confidence: 99%

Recent Advances in Fault Diagnosis Techniques for Photovoltaic Systems: A Critical Review

Yang,

Zheng,

Han

et al. 2024

Prot. Control Mod. Power Syst.

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If a failure in the components of a photovoltaic (PV) system, such as PV module, controller, inverter, load, cable, etc. goes undetected and uncorrected, it can seriously affect the efficiency, safety, and reliability of the entire PV power plant. In addition, fires can occur if specific faults, such as arc, ground, and line-to-line faults remain unresolved. Therefore, PV system (PVS) fault diagnoses (FD) are crucial for PV power plant reliability, efficiency, and safety. Many fault diagnosis methods and techniques for PVS components have been developed. In addition, with the development of PV devices, more advanced and intelligent diagnostic technologies are continuously being researched and developed. However, a systematic and thorough analysis, summary, and conclusion are still urgently required. Thus, this paper introduces the types, causes, and impacts of PVS faults, and reviews and discusses the methods proposed in the literature for PVS fault diagnosis, and in particular, failures in PV arrays (PVA). Special attention is paid to the optimization direction of various fault diagnosis methods under different priorities, and their limitations, feasibility, complexity, and cost-effectiveness. Finally, challenges and suggestions are put forward for future research.

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“…The result illustrated that the W-ANN can provide lower computing time such that reduction time was 49% and cleaner ECG input signal. The method was implemented on the data MIT-BIH arrhythmia database and real ECG signal measurement [39]. Boussaa et al (2016) presented the design of a cardiac pathologies detection system with high precision of calculation and decision, which consists of the mel-frequency coefficient cepstrum algorithms such as fingerprint extractor (or features) of the cardiac signal and the algorithms of ANN multilayer perceptron (MLP) type MLP classifier as fingerprints extracted into two classes: Normal or abnormal.…”

Section: Annmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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Electrocardiogram diagnosis using wavelet-based artificial neural network

Cited by 9 publications

References 7 publications

Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox

Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox

Recent Advances in Fault Diagnosis Techniques for Photovoltaic Systems: A Critical Review

A Review Study for Electrocardiogram Signal Classification

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