Brain Mapping using a Blockchain Approach

Adochiei, Felix-Constantin; Ciucu, Radu; Adochiei, Ioana Raluca; Argatu, Florin-Ciprian; Enache, Bogdan–Adrian; Miron, C; Serițan, George

doi:10.1109/ehb47216.2019.8970089

Cited by 9 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HRV detection is a complex procedure which requires a series of actions, in order to accurately measure the rate of change associated with the R-R interval obtained from the QRS complex, the raw ECG signal first needs to be filtered, processed and reconstructed. Raw ECG signals need to be filtered in order to remove baseline wander, powerline interference and muscle noise (53,54). After filtering, the ECG signal is a lot smoother and cleaner, which makes it easier to detect the QRS complex.…”

Section: Hrv and Signal Processing Methodsmentioning

confidence: 99%

Trends in Heart-Rate Variability Signal Analysis

Ishaque¹,

Khan²,

Krishnan³

2023

Preprint

View full text Add to dashboard Cite

<p>Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.</p>

show abstract

Section: Hrv and Signal Processing Methodsmentioning

confidence: 99%

Trends in Heart-Rate Variability Signal Analysis

Ishaque¹,

Khan²,

Krishnan³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Digital filtering is commonly used to attenuate low-frequency baseline wandering and high-frequency noises [ 14 ]. Advanced methods using multi-resolution wavelet analysis [ 15 , 16 ] and kalman filtering [ 17 , 18 ] are also proposed to enhance ECG waveforms for QRS detection and extract ECG features.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

Chan

Chang

Hsu

et al. 2023

Sensors

View full text Add to dashboard Cite

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.

show abstract