Artificial intelligence classification methods of atrial fibrillation with implementation technology

Lim, Huey Woan; Hau, Yuan Wen; Lim, C.W.; Othman, Mohamed

doi:10.1080/24699322.2016.1240303

Cited by 16 publications

(14 citation statements)

References 35 publications

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“…The pulse-like signals shown in the lower section of each figure are the trigger signals to activate the VADs as opening commands. Though there are different disturbances in the original ECG signals as shown in the upper section of each figure, such as low frequency drift, amplitude inconsistency, disorder period, atrial fibrillation, which is one of the most common types of arrhythmia[27], the proposed method can always accurately and stably identify the interest signals, These simulation results based on the ECG signals from authoritative database confirmed that this method including the key algorithm and the program based on it is of high adaptability.…”

mentioning

confidence: 67%

Improved identification of the electrocardio-signal for pulsatile ventricular assist devices

Luo

Zhu

et al. 2017

Computer Assisted Surgery

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We introduce and investigate a method to identify the feature point of an electrocardiogram (ECG) to provide real-time and accurate trigger signals for pulsatile ventricular assist devices (PVADs). An important part of this method is an improved data processing algorithm, in which a differential calculator and another a low-pass filtering were added to avoid drift in the original signal and systematically delay caused by physical devices. The method was systematically illustrated in this article and a test-setup was built based on the LabVIEW program development environment. Both simulations and experiments were carried out to demonstrate the merits of the method. Simulated results based on four typical pathological ECG signals confirm the robustness and adaptability of this method. Experimental results verified the benefits of this method with regard to increased accuracy that can pick up the interested signal accurately in a degree of larger than 99%. This improved technology proposed in this research will be greatly beneficial to simultaneous triggering in VADs and eventually to the heart failure recovery.

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mentioning

confidence: 67%

Improved identification of the electrocardio-signal for pulsatile ventricular assist devices

Luo

Zhu

et al. 2017

Computer Assisted Surgery

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“…Additionally, commonly known classes include statistical or distribution function features. For the features extraction study with conventional domains please consult [84]. But here in this study, classification of feature extraction is driven by the source activities that generate the data or features.…”

Section: Features Extractionmentioning

confidence: 99%

“…RR Interval is the most popular feature for the detection of AF and many other types of arrhythmia. Some commonly used RR interval based time, frequency, spectral and distribution domain features are listed in Table VI [19], [84], [100]- [102].…”

Section: Features Extractionmentioning

confidence: 99%

“…Besides the popular methods mentioned above, some other advance techniques used for ECG feature extraction include power spectral density(PSD) [96], and Structural Co-Occurrence Matrix (SCM) [103], [104], Burg method, Short Time Fourier Transforms (STFT), Higher Order Statistics (HOS) [103], [104], Continuous Wavelet Transform (CWT). Some non-linear methods of feature extraction include Recurrence Plots, Sample Entropy (SampEn), Fractal Dimension, Approximate Entropy, Largest Lyapunov Exponent, Detrended Fluctuation Analysis, Correlation Dimension Analysis [84]. Methods of finding similarity between two waveform like wavelet coherence (WTC) method is also very effective method used for ECG features extraction [105].…”

Section: Features Extractionmentioning

confidence: 99%

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A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. n this paper, state-ofthe-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

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“…AI is an English standard for intelligence. Theories, systems, technologies, and systems used in research and simulation are new science; human wisdom is constantly expanding [22,23]. The roles of AI technology in visual media communication design are the following:…”

Section: Ai Technologymentioning

confidence: 99%

Using Design and Graphic Design with Color Research in AI Visual Media to Convey

2021

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With the development of science and technology and social progress, people can reveal design information in different forms on a daily basis. The more common visual design information, the more important its development status. In visual communication design, the two most important factors are color and image. These two factors can play a great role and can attract the public’s attention. This article is based on AI technology to study the design of color matching and image application in visual media communication design, aiming to innovate the method of visual media communication design, breaking through the traditional color matching and image application, so as to better use color art in visual communication design and graphic art and design excellent works. First, this article elaborates on the concept, main components, and influencing factors of visual communication design and also introduces the application of AI technology, then designs a visual media communication design model based on AI technology, and finally analyzes the current status of visual media communication design, usage, and satisfaction. From the analytical analysis, it appears that the optical media interface is commonly used for packaging design, responding to 45.6%; it is used more in the film and television media industry, reaching 68%; 49% of the public are very satisfied with AI visual media communication design. The above data fully shows that the application of AI visual media communication design is very wide, and the color matching and image use are very prominent, and the public is willing to accept AI visual media communication design works.

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Artificial intelligence classification methods of atrial fibrillation with implementation technology

Cited by 16 publications

References 35 publications

Improved identification of the electrocardio-signal for pulsatile ventricular assist devices

Improved identification of the electrocardio-signal for pulsatile ventricular assist devices

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Using Design and Graphic Design with Color Research in AI Visual Media to Convey

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