A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes

2019 ◽  
Vol 19 (12) ◽  
pp. 4509-4517 ◽  
Author(s):  
Rajesh Kumar Tripathy ◽  
Abhijit Bhattacharyya ◽  
Ram Bilas Pachori
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aline dos Santos Silva ◽  
Hugo Almeida ◽  
Hugo Plácido da Silva ◽  
António Oliveira

AbstractMultiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.


Marine Drugs ◽  
2019 ◽  
Vol 17 (7) ◽  
pp. 402 ◽  
Author(s):  
Nancy S. Younis ◽  
Esam M. Bakir ◽  
Maged E. Mohamed ◽  
Nermin A. El Semary

Cyanothece sp., a coccoid, unicellular, nitrogen-fixing and hydrogen-producing cyanobacterium, has been used in this study to biosynthesize customized gold nanoparticles under certain chemical conditions. The produced gold nanoparticles had a characteristic absorption band at 525–535 nm. Two types of gold nanoparticle, the purple and blue, were formed according to the chemical environment in which the cyanobacterium was grown. Dynamic light scattering was implemented to estimate the size of the purple and blue nanoparticles, which ranged from 80 ± 30 nm and 129 ± 40 nm in diameter, respectively. The highest scattering of laser light was recorded for the blue gold nanoparticles, which was possibly due to their larger size and higher concentration. The appearance of anodic and cathodic peaks in cyclic voltammetric scans of the blue gold nanoparticles reflected the oxidation into gold oxide, followed by the subsequent reduction into the nano metal state. The two produced forms of gold nanoparticles were used to treat isoproterenol-induced myocardial infarction in experimental rats. Both forms of nanoparticles ameliorated myocardial infarction injury, with a slight difference in their curative activity with the purple being more effective. Mechanisms that might explain the curative effect of these nanoparticles on the myocardial infarction were proposed. The morphological, physiological, and biochemical attributes of the Cyanothece sp. cyanobacterium were fundamental for the successful production of “tailored” nanoparticles, and complemented the chemical conditions for the differential biosynthesis process. The present research represents a novel approach to manipulate cyanobacterial cells towards the production of different-sized gold nanoparticles whose curative impacts vary accordingly. This is the first report on that type of manipulated gold nanoparticles biosynthesis which will hopefully open doors for further investigations and biotechnological applications.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kwanghyun Sohn ◽  
Steven P. Dalvin ◽  
Faisal M. Merchant ◽  
Kanchan Kulkarni ◽  
Furrukh Sana ◽  
...  

Abstract Repolarization alternans (RA) has been implicated in the pathogenesis of ventricular arrhythmias and sudden cardiac death. We developed a 12-lead, blue-tooth/Smart-Phone (Android) based electrocardiogram (ECG) acquisition and monitoring system (cvrPhone), and an application to estimate RA, in real-time. In in-vivo swine studies (N = 17), 12-lead ECG signals were recorded at baseline and following coronary artery occlusion. RA was estimated using the Fast Fourier Transform (FFT) method using a custom developed algorithm in JAVA. Underlying ischemia was detected using a custom developed ischemic index. RA from each lead showed a significant (p < 0.05) increase within 1 min of occlusion compared to baseline (n = 29). Following myocardial infarction, spontaneous ventricular tachycardia episodes (n = 4) were preceded by significant (p < 0.05) increase of RA prior to the onset of the tachy-arrhythmias. Similarly, the ischemic index exhibited a significant increase following myocardial infarction (p < 0.05) and preceding a tachy-arrhythmic event. In conclusion, RA can be effectively estimated using surface lead electrocardiograms by analyzing beat-to-beat variability in ECG morphology using a smartphone based platform. cvrPhone can be used to detect myocardial ischemia and arrhythmia susceptibility using a user-friendly, clinically acceptable, mobile platform.


2020 ◽  
Vol 10 (6) ◽  
pp. 1265-1273
Author(s):  
Lili Chen ◽  
Huoyao Xu

Sleep apnea (SA) is a common sleep disorders affecting the sleep quality. Therefore the automatic SA detection has far-reaching implications for patients and physicians. In this paper, a novel approach is developed based on deep neural network (DNN) for automatic diagnosis SA. To this end, five features are extracted from electrocardiogram (ECG) signals through wavelet decomposition and sample entropy. The deep neural network is constructed by two-layer stacked sparse autoencoder (SSAE) network and one softmax layer. The softmax layer is added at the top of the SSAE network for diagnosing SA. Afterwards, the SSAE network can get more effective high-level features from raw features. The experimental results reveal that the performance of deep neural network can accomplish an accuracy of 96.66%, a sensitivity of 96.25%, and a specificity of 97%. In addition, the performance of deep neural network outperforms the comparison models including support vector machine (SVM), random forest (RF), and extreme learning machine (ELM). Finally, the experimental results reveal that the proposed method can be valid applied to automatic SA event detection.


2017 ◽  
Vol 415-416 ◽  
pp. 190-198 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Hamido Fujita ◽  
Shu Lih Oh ◽  
Yuki Hagiwara ◽  
Jen Hong Tan ◽  
...  

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