Merge pull request #1 from icarosadero/dev

ECG

Gemfile

@@ -8,4 +8,5 @@ group :jekyll_plugins do
   gem 'jekyll-timeago', '~> 0.13.1'
   gem 'jekyll-scholar', '~> 7.0.0'
   gem 'jekyll-feed', '~> 0.17.0'
+  gem 'jekyll-figure', '~> 0.2.0'
 end

_bibliography/references.bib

@@ -15,3 +15,111 @@ @BOOK{book:Nahin1998-wi
   address   = "Princeton, NJ",
   language  = "en"
 }
+@article{Venkatachalam2011,
+  title = {Signals and Signal Processing for the Electrophysiologist: Part I: Electrogram Acquisition},
+  volume = {4},
+  ISSN = {1941-3084},
+  url = {http://dx.doi.org/10.1161/CIRCEP.111.964304},
+  DOI = {10.1161/circep.111.964304},
+  number = {6},
+  journal = {Circulation: Arrhythmia and Electrophysiology},
+  publisher = {Ovid Technologies (Wolters Kluwer Health)},
+  author = {Venkatachalam,  K.L. and Herbrandson,  Joel E. and Asirvatham,  Samuel J.},
+  year = {2011},
+  month = dec,
+  pages = {965–973}
+},
+@inbook{inbook,
+  author = {Malmivuo, Jaakko and Plonsey, Robert},
+  year = {1975},
+  month = {01},
+  pages = {277-289},
+  title = {Bioelectromagnetism. 15. 12-Lead ECG System},
+  isbn = {978-0195058239},
+  url = {https://www.bem.fi/book/15/15.htm}
+},
+@instamp{instamp,
+  title = {The Differential Amplifier},
+  url = {https://www.electronics-tutorials.ws/opamp/opamp_5.html}
+},
+@bandfilter{bandfilter,
+  title = {Active Band Pass Filter},
+  url = {https://www.electronics-tutorials.ws/filter/filter_7.html}
+},
+@noise{noise,
+  title = {Operational Amplifiers: Noise Calculations of Instrumentation Amplifier Circuits},
+  url = {https://www.renesas.com/us/en/document/apn/r13an0011-noise-calculations-instrumentation-amplifier-circuits-rev100}
+},
+@texas_noise{texas_noise,
+  title = {Noise Analysis in Operational Amplifier Circuits},
+  url = {https://www.ti.com/lit/an/slva043b/slva043b.pdf?ts=1706030563174&ref_url=https%253A%252F%252Fsearch.brave.com%252F}
+},
+@article{YDU2017,
+  title = {Design of an ECG Sensor Circuitry for Cardiovascular Disease Diagnosis},
+  volume = {2},
+  ISSN = {2573-2838},
+  url = {http://dx.doi.org/10.15406/ijbsbe.2017.02.00032},
+  DOI = {10.15406/ijbsbe.2017.02.00032},
+  number = {4},
+  journal = {International Journal of Biosensors & Bioelectronics},
+  publisher = {MedCrave Group,  LLC},
+  author = {Y DU,  Winncy },
+  year = {2017},
+  month = may 
+},
+@ARTICLE{air_pollution,
+
+AUTHOR={Zhang, Shugang and Lu, Weigang and Wei, Zhiqiang and Zhang, Henggui},   
+
+TITLE={Air Pollution and Cardiac Arrhythmias: From Epidemiological and Clinical Evidences to Cellular Electrophysiological Mechanisms},      
+
+JOURNAL={Frontiers in Cardiovascular Medicine},      
+
+VOLUME={8},           
+
+YEAR={2021},      
+
+URL={https://www.frontiersin.org/articles/10.3389/fcvm.2021.736151},       
+
+DOI={10.3389/fcvm.2021.736151},      
+
+ISSN={2297-055X},   
+
+ABSTRACT={Cardiovascular disease is the leading cause of death worldwide and kills over 17 million people per year. In the recent decade, growing epidemiological evidence links air pollution and cardiac arrhythmias, suggesting a detrimental influence of air pollution on cardiac electrophysiological functionality. However, the proarrhythmic mechanisms underlying the air pollution-induced cardiac arrhythmias are not fully understood. The purpose of this work is to provide recent advances in air pollution-induced arrhythmias with a comprehensive review of the literature on the common air pollutants and arrhythmias. Six common air pollutants of widespread concern are discussed, namely particulate matter, carbon monoxide, hydrogen sulfide, sulfur dioxide, nitrogen dioxide, and ozone. The epidemiological and clinical reports in recent years are reviewed by pollutant type, and the recently identified mechanisms including both the general pathways and the direct influences of air pollutants on the cellular electrophysiology are summarized. Particularly, this review focuses on the impaired ion channel functionality underlying the air pollution-induced arrhythmias. Alterations of ionic currents directly by the air pollutants, as well as the alterations mediated by intracellular signaling or other more general pathways are reviewed in this work. Finally, areas for future research are suggested to address several remaining scientific questions.}
+},
+@ARTICLE{pharmacokinetic,
+
+AUTHOR={Zou, Huixi and Banerjee, Parikshit and Leung, Sharon Shui Yee and Yan, Xiaoyu},   
+
+TITLE={Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges},      
+
+JOURNAL={Frontiers in Pharmacology},      
+
+VOLUME={11},           
+
+YEAR={2020},      
+
+URL={https://www.frontiersin.org/articles/10.3389/fphar.2020.00997},       
+
+DOI={10.3389/fphar.2020.00997},      
+
+ISSN={1663-9812},   
+
+ABSTRACT={With the advancement of technology, drug delivery systems and molecules with more complex architecture are developed. As a result, the drug absorption and disposition processes after administration of these drug delivery systems and engineered molecules become exceedingly complex. As the pharmacokinetic and pharmacodynamic (PK-PD) modeling allows for the separation of the drug-, carrier- and pharmacological system-specific parameters, it has been widely used to improve understanding of the in vivo behavior of these complex delivery systems and help their development. In this review, we summarized the basic PK-PD modeling theory in drug delivery and demonstrated how it had been applied to help the development of new delivery systems and modified large molecules. The linkage between PK and PD was highlighted. In particular, we exemplified the application of PK-PD modeling in the development of extended-release formulations, liposomal drugs, modified proteins, and antibody-drug conjugates. Furthermore, the model-based simulation using primary PD models for direct and indirect PD responses was conducted to explain the assertion of hypothetical minimal effective concentration or threshold in the exposure-response relationship of many drugs and its misconception. The limitations and challenges of the mechanism-based PK-PD model were also discussed.}
+},
+
+
+@Article{ideal_ecg,
+AUTHOR = {Awal, Md. Abdul and Mostafa, Sheikh Shanawaz and Ahmad, Mohiuddin and Alahe, Mohammad Ashik and Rashid, Mohd Abdur and Kouzani, Abbas Z. and Mahmud, M. A. Parvez},
+TITLE = {Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias},
+JOURNAL = {Sensors},
+VOLUME = {21},
+YEAR = {2021},
+NUMBER = {5},
+ARTICLE-NUMBER = {1638},
+URL = {https://www.mdpi.com/1424-8220/21/5/1638},
+PubMedID = {33652721},
+ISSN = {1424-8220},
+ABSTRACT = {The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.},
+DOI = {10.3390/s21051638}
+}

_config.yml

@@ -38,6 +38,7 @@ plugins:
   - jekyll-mermaid
   - jekyll-feed
   - jekyll/scholar
+  - jekyll-figure
 
 mermaid:
   src: https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js
@@ -46,6 +47,12 @@ mermaid:
 sass:
   style: compressed
 
+scholar:
+  style: ieee
+
+jekyll-figure:
+  paragraphs: false
+
 exclude:
 - "*.gemspec"
 - LICENSE.md

_includes/head.html

@@ -6,13 +6,14 @@
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+	<script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
 	<script type="text/x-mathjax-config">
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+			inlineMath: [['$','$']],
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+		  TeX: { equationNumbers: { autoNumber: "ALL" } }
 		});
-	  </script>
-	  <script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
+	</script>
 </head>

_posts/2023-08-17-nernst-potential.md

@@ -113,4 +113,6 @@ $$\Delta V = \frac{1}{\beta q}\ln\left(\frac{n_1}{n_2}\right)$$
 
 Which is the Nernst Potential with $\beta = \frac{1}{k_B T}$.
 
-{% bibliography %}
+---
+
+{% bibliography --cited %}