Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments.
The current world condition is dire due to epidemics and pandemics as a result of novel viruses, such as influenza and the coronavirus, causing acute respiratory syndrome. To overcome these critical situations, the current research seeks to generate a common surveillance system with the assistance of a controlled Internet of Things operated under a Gaussian noise channel. To create the model system, a study with an analysis of H1N1 influenza virus determination on an interdigitated electrode (IDE) sensor was validated by current-volt measurements. The preliminary data were generated using hemagglutinin as the target against gold-conjugated aptamer/antibody as the probe, with the transmission pattern showing consistency with the Gaussian noise channel algorithm. A good fit with the algorithmic values was found, displaying a similar pattern to that output from the IDE, indicating reliability. This study can be a model for the surveillance of varied pathogens, including the emergence and reemergence of novel strains.
Artificial intelligence of things (AIoT) has become a potential tool for use in a wide range of fields, and its use is expanding in interdisciplinary sciences. On the other hand, in a clinical scenario, human blood-clotting disease (Royal disease) detection has been considered an urgent issue that has to be solved. This study uses AIoT with deep long short-term memory networks for biosensing application and analyzes the potent clinical target, human blood clotting factor IX, by its aptamer/antibody as the probe on the microscaled fingers and gaps of the interdigitated electrode. The earlier results by the current-volt measurements have shown the changes in the surface modification. The limit of detection (LOD) was noticed as 1 pM with the antibody as the probe, whereas the aptamer behaved better with the LOD at 100 fM. The time-series predictions from the AIoT application supported the obtained results with the laboratory analyses using both probes. This application clearly supports the results obtained from the interdigitated electrode sensor as aptamer to be the better option for analyzing the blood clotting defects. The current study supports a great implementation of AIoT in sensing application and can be followed for other clinical biomarkers.
Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
Myocardial infarction (MI) is highly related to cardiac arrest leading to death and organ damage. Radiological techniques and electrocardiography have been used as preliminary tests to diagnose MI; however, these techniques are not sensitive enough for early-stage detection. A blood biomarker-based diagnosis is an immediate solution, and due to the high correlation of troponin with MI, it has been considered to be a gold-standard biomarker. In the present research, the cardiac biomarker troponin I (cTnI) was detected on an interdigitated electrode sensor with various surface interfaces. To detect cTnI, a capture aptamer-conjugated gold nanoparticle probe and detection antibody probe were utilized and compared through an alternating sandwich pattern. The surface metal oxide morphology of the developed sensor was proven by microscopic assessments. The limit of detection with the aptamer-gold-cTnI-antibody sandwich pattern was 100 aM, while it was 1 fM with antibody-gold-cTnI-aptamer, representing 10-fold differences. Further, the high performance of the sensor was confirmed by selective cTnI determination in serum, exhibiting superior nonfouling. These methods of determination provide options for generating novel assays for diagnosing MI.
In an aim of developing portable biosensor for SARS-CoV-2 pandemic, which facilitates the point-of-care aptasensing, a strategy using 10 μm gap-sized gold interdigitated electrode (AuIDE) is presented. The silane-modified AuIDE surface was deposited with ∼20 nm diamond and enhanced the detection of SARS-CoV-2 nucleocapsid protein (NCP). The characteristics of chemically modified diamond were evidenced by structural analyses, revealing the cubic crystalline nature at (220) and (111) planes as observed by XRD. XPS analysis denotes a strong interaction of carbon element, composed ∼95% as seen in EDS analysis. The C-C, CC, CO, CN functional groups were well-refuted from XPS spectra of carbon and oxygen elements in diamond. The interrelation between elements through FTIR analysis indicates major intrinsic bondings at 2687-2031 cm-1. The aptasensing was evaluated through electrochemical impedance spectroscopy measurements, using NCP spiked human serum. With a good selectivity the lower detection limit was evidenced as 0.389 fM, at a linear detection range from 1 fM to 100 pM. The stability, and reusability of the aptasensor were demonstrated, showing ∼30% and ∼33% loss of active state, respectively, after ∼11 days. The detection of NCP was evaluated by comparing anti-NCP aptamer and antibody as the bioprobes. The determination coefficients of R2 = 0.9759 and R2 = 0.9772 were obtained for aptamer- and antibody-based sensing, respectively. Moreover, the genuine interaction of NCP aptamer and protein was validated by enzyme linked apta-sorbent assay. The aptasensing strategy proposed with AuIDE/diamond enhanced sensing platform is highly recommended for early diagnosis of SARS-CoV-2 infection.