Over the past few decades, nanostructured conducting polymers have received great attention in several application fields, including biosensors, microelectronics, polymer batteries, actuators, energy ...conversion, and biological applications due to their excellent conductivity, stability, and ease of preparation. In the bioengineering application field, the conducting polymers were reported as excellent matrixes for the functionalization of various biological molecules and thus enhanced their performances as biosensors. In addition, combinations of metals or metal oxides nanostructures with conducting polymers result in enhancing the stability and sensitivity as the biosensing platform. Therefore, several methods have been reported for developing homogeneous metal/metal oxide nanostructures thin layer on the conducting polymer surfaces. This review will introduce the fabrications of different conducting polymers nanostructures and their composites with different shapes. We will exhibit the different techniques that can be used to develop conducting polymers nanostructures and to investigate their chemical, physical and topographical effects. Among the various biosensors, we will focus on conducting polymer-integrated electrochemical biosensors for monitoring important biological targets such as DNA, proteins, peptides, and other biological biomarkers, in addition to their applications as cell-based chips. Furthermore, the fabrication and applications of the molecularly imprinted polymer-based biosensors will be addressed in this review.
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest ...among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.
Cardiovascular diseases, listed as the underlying cause of death, accounted for approximately 836,546 deaths in the United States in 2018. Statistics show that almost one of every three deaths in the ...US is a result of heart disease. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. This is while quick and immediate action at the onset of such heart conditions can save many lives. To this end, ample research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. However, more accurate and near real-time techniques are still under investigation. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier. The proposed method classifies the ECG signal as normal or abnormal by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 97.47%.
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