PSR-based research of feature extraction from one-second EEG signals: a neural network study

Dawid, A.

doi:10.1007/s42452-019-1579-9

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…There has been a wealth of work in the classification of various Cardiovascular Diseases (CVDs) from ECG data [30,29,27,26,31,25,14,8,33]. Other applications of machine learning in ECG analysis include detecting seizures and heart attacks [18,19], predicting patients' blood pressure [24], detecting a patients facial expressions [6] and analysis of ECG of the brain has been used for creating brain computer interfaces (BCI) capable of detecting which body Figure 2. S-ICD screening tool.…”

Section: Related Workmentioning

confidence: 99%

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Deep learning methods for screening patients' S-ICD implantation eligibility

Dunn¹,

ElRefai²,

Roberts³

et al. 2021

Preprint

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Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently patients' Electrocardiograms (ECGs) are screened over 10 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behaviour of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.

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Section: Related Workmentioning

confidence: 99%

“…Box counting as well as column and row statistics are features o en extracted from the PSR matrix of ECG data. These methods have been used in the prediction of CVD [30,29,27,26], creating BCIs [5,7], and detecting facial expressions [6]. These approaches all centre around manually selecting features to extract from the PSR matrix.…”

Section: Related Workmentioning

confidence: 99%

Deep learning methods for screening patients' S-ICD implantation eligibility

Dunn¹,

ElRefai²,

Roberts³

et al. 2021

Preprint

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“…The system of headlights operation recognition using the digital twin method Aleksander Dawid, and Paweł Buchwald have come to the forefront [5][6][7]. Among these, convolutional neural networks (CNNs) have made substantial strides in advancing vehicle detection devices.…”

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confidence: 99%

The system of headlights operation recognition using the digital twin method

Dawid,

Buchwald

2024

International Journal of Electronics and Telecommunications

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Virtual digital representation of a physical object or system, created with precision through computer simulations, data analysis, and various digital technologies can be used as training set for real life situations. The principal aim behind creating a virtual representation is to furnish a dynamic, data-fueled, and digital doppelgänger of the physical asset. This digital counterpart serves multifaceted purposes, including the optimization of performance, the continuous monitoring of its well-being, and the augmentation of informed decision-making processes. Main advantage of employing a digital twin is its capacity to facilitate experimentation and assessment of diverse scenarios and conditions, all without impinging upon the actual physical entity. This capability translates into substantial cost savings and superior outcomes, as it allows for the early identification and mitigation of issues before they escalate into significant problems in the tangible world. Within our research endeavors, we've meticulously constructed a digital twin utilizing the Unity3D software. This digital replica faithfully mimics vehicles, complete with functioning headlamp toggles. Our lighting system employs polygons and normal vectors, strategically harnessed to generate an array of dispersed and reflected light effects. To ensure realism, we've meticulously prepared the scene to emulate authentic road conditions. For validation and testing, we integrated our model with the YOLO (You Only Look Once) neural network. A specifically trained compact YOLO model demonstrated impressive capabilities by accurately discerning the status of real vehicle headlamps. On average, it achieved an impressive recognition probability of 80%, affirming the robustness of our digital twin.

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GPU Implementation of the Parallel Ising Model Algorithm Using Object-Oriented Programming

Dawid

2020

Theory and Applications of Dependable Computer Systems

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PSR-based research of feature extraction from one-second EEG signals: a neural network study

Cited by 7 publications

References 36 publications

Deep learning methods for screening patients' S-ICD implantation eligibility

Deep learning methods for screening patients' S-ICD implantation eligibility

The system of headlights operation recognition using the digital twin method

GPU Implementation of the Parallel Ising Model Algorithm Using Object-Oriented Programming

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