Lightweight convolution transformer for cross-patient seizure detection in multi-channel EEG signals.

BACKGROUND: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizures frequency and severity to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies. METHODS AND MATERIALS: This study proposes a novel deep learning architecture-based Lightweight Convolution Transformer (LCT). The Transformer model is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed work, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the Transformer encoder is extracted by sequence pooling instead of the learnable class token. RESULTS: Extensive experimental results demonstrate that the proposed model on cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition, the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of Transformer encoder layers, which significantly reduces the computational complexity. In this research, we provide a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.

Computer-aided diagnostic system for hypertensive retinopathy: A review.

Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early microcirculation abnormalities in chronic diseases can be diagnosed through retinal examination prior to the onset of major clinical consequences. Computer-aided diagnosis (CAD) plays a vital role in the early identification of HR with improved diagnostic accuracy, which is time-efficient and demands fewer resources. Recently, numerous studies have been reported on the automatic identification of HR. This paper provides a comprehensive review of the automated tasks of Artery-Vein (A/V) classification, Arteriovenous ratio (AVR) computation, HR detection (Binary classification), and HR severity grading. The review is conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The paper discusses the clinical features of HR, the availability of datasets, existing methods used for A/V classification, AVR computation, HR detection, and severity grading, and performance evaluation metrics. The reviewed articles are summarized with classifiers details, adoption of different kinds of methodologies, performance comparisons, datasets details, their pros and cons, and computational platform. For each task, a summary and critical in-depth analysis are provided, as well as common research issues and challenges in the existing studies. Finally, the paper proposes future research directions to overcome challenges associated with data set availability, HR detection, and severity grading.

A review of the evolution of scientific literature on technology-assisted approaches using RGB-D sensors for musculoskeletal health monitoring.

The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention. The evolution in motion tracking technology enables us to capture the fine details of body movements. The research community has proposed various approaches to help clinicians in diagnosis and monitor treatment sessions. This paper succinctly reviews the evolution of technology-assisted approaches for musculoskeletal health monitoring, using motion capture sensors. To streamline the search through the literature database, the PICOS framework and PRISMA method have been incorporated. The present study reviews methods to transform motion capture data into kinematics variables and factors that affect the tracking performance of RGB-D sensors. Furthermore, widely utilized time-series filters for skeletal data denoising and smoothing for kinematics analysis, stochastic models for movement modeling, rule-based and template-based approaches for rehabilitation exercises assessment, and telerehabilitation sessions for remote health monitoring are explored. This article analyzes skeletal tracking methods by providing advantages and drawbacks of the state of the art rehabilitation sessions assessment, skeletal joint kinematics analysis, and MSK Telerehabilitation approaches. It also discusses the possible future research avenues to improve musculoskeletal disorder diagnosis and treatment monitoring. Our review signifies that RGB-D sensor-based approaches are inexpensive and portable for disorder diagnosis and treatment monitoring. It can also be a viable option for clinicians to provide contactless healthcare access to patients in the current scenario of the COVID-19 pandemic.

Kinect v2 tracked Body Joint Smoothing for Kinematic Analysis in Musculoskeletal Disorders.

Body joint monitoring is essential for disorder diagnosis and assessment of treatment effectiveness. Microsoft Kinect v2 is a low-cost and markerless human motion-tracking RGB-D sensor that provides spatial locations of tracked skeletal joints in the form of 3D coordinates. Sometimes, body tracking of kinect v2 produces erratic 3D coordinates, which affects the real-time tracking performance of the sensor. A careful study of the literature suggests that skeletal tracking of kinect v2 needs further exploration. This work proposes a filter combined with the concept of body kinematics to remove noise and enhances the quality of 3D coordinates in body frame data. Also, it generates "Motion Signature" of the tracked joint, which shows movement pattern for kinematic analysis, and helpful in joint monitoring of Musculoskeletal Disorders (MSD). The clinically relevant anatomical movement was executed, to evaluate the performance of the proposed filter. We compared Range of Motion (RoM) values obtained from the proposed filter with the gold standard goniometry. Results indicate that RoM values from the proposed filter are in high correlation with the goniometry with an Intraclass Correlation Coefficient values ranging between 0.95 to 0.98 authenticating that it improves the skeletal joint tracking of kinect v2.

Isolation and purification of antibacterial compound from Streptomyces levis collected from soil sample of north India.

During the screening programme for microbial cultures producing antimicrobial agents, an active microbial strain of Streptomyces was isolated from the agricultural soil of Narnaul, Haryana India. Physiological, biochemical characteristics and 16S ribosomal RNA sequence homology studies revealed that it was similar to Streptomyces levis (sequence similarity 100%). The microbial strain was submitted to Genomebio Technologies Pvt. Ltd., Pune, Maharashtra, India under Accession No. EU124569. The isolated strain was found to produce extracellular active compound showing strong antimicrobial activity against Klebsiella pneumoniae MTCC 109, Pseudomonas aeruginosa MTCC 741 and Staphylococcus aureus MTCC 96. The antibacterial compound was successfully isolated and purified. Structure elucidation of antibacterial metabolite with EI-MS/ HRMS showed molecular ion peak at m/z 686 [M+H]+. Whereas, elemental analysis of the said compound showed C = 61.31, H = 8.61, N = 2.04 and O = 28.02, and indicated a molecular formula of C35H59NO12. The presence of 'chromone' nucleus in the compound's chemical structure was confirmed by using 1HNMR studies. The present study reports the purification of potential antibacterial compound from Streptomyces levis isolated from the unexplored soil of north India and warrants for further characterization of this potential compound for optimum utilization for antimicrobial purposes.

Cancer medicine: a direction.

OBJECTIVE: The medicine of cancer is directed in this paper. The pie theory is applied for the proposed medicine. The improbability and un-constancy are the major theories, which are used to design this anti-cancer medicine.

Effect of Agonist and Antagonist on the In Vitro Contractility of Inflamed Vermiform Appendix.

INTRODUCTION: Appendicitis poses a great health problem worldwide. Previous studies demonstrated structural damage to neuronal network and interstitial cell of Cajal in appendicitis. Above observations suggest for the alterations in appendicular motility/contractility in appendicitis. But the mechanisms involved in mediating the contractility in inflamed vermiform appendix is not known till date. AIM: The present in vitro study was performed to find out the mechanisms responsible for contractility in the inflamed human vermiform appendix. MATERIALS AND METHODS: Contractions of the longitudinal muscle strips of inflamed appendix were recorded in vitro at 37±0.5°C. Control contractions were recorded for 30 min after an initial tension of 0.5 gram. Initially dose-response experiments of agonists (acetylcholine, serotonin and histamine) were performed separately and the dose that produced maximum contraction was determined with each agonist. This maximal dose of agonist was used to elicit contractions in next series of experiments before and after pre-treatment with appropriate antagonists like atropine, ondansetron (5-HT3 antagonist) and chlorpheniramine maleate respectively. RESULTS: Acetylcholine (ACh) and serotonin (5-HT) elicited maximum amplitude of contraction at 10 µM and 1 µM concentration respectively. These contractions were significantly blocked by prior exposure of muscle strips with atropine (100 µM) and ondansetron (10 µM). Histamine produced very low amplitude of contractions in comparison to ACh or 5-HT and did not exhibit dose-response relations. The histamine induced contractions were blocked by H1 antagonist chlorpheniramine maleate (100 µM). CONCLUSION: The observations suggested that the contractility of longitudinal muscle strips of inflamed vermiform appendix in human beings was predominantly mediated by muscarinic and serotonergic (5-HT3) mechanisms, whereas, histaminergic mechanisms played a minor role in mediating the contractility.

Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios.

This paper presents a system based on Seismocardiography (SCG) to monitor the heart sound signal for the long-term. It uses an accelerometer, which is of small size and low weight and, thus, convenient to wear. Such a system should also be robust to various noises which occur in real life scenarios. Therefore, a detailed analysis is provided of the proposed system and its performance is compared to the performance of the Phoncardiography (PCG) system. For this purpose, both signals of five subjects were simultaneously recorded in clinical and different real life noisy scenarios. For the quantitative analysis, the detection rate of fundamental heart sound components, S1 and S2, is obtained. Furthermore, a quality index based on the energy of fundamental components is also proposed and obtained for the same. Results show that both the techniques are able to acquire the S1 and S2, in clinical set-up. However, in real life scenarios, we observed many favourable features in the proposed system as compared to PCG, for its use for long-term monitoring.

Heart monitoring systems--a review.

To diagnose health status of the heart, heart monitoring systems use heart signals produced during each cardiac cycle. Many types of signals are acquired to analyze heart functionality and hence several heart monitoring systems such as phonocardiography, electrocardiography, photoplethysmography and seismocardiography are used in practice. Recently, focus on the at-home monitoring of the heart is increasing for long term monitoring, which minimizes risks associated with the patients diagnosed with cardiovascular diseases. It leads to increasing research interest in portable systems having features such as signal transmission capability, unobtrusiveness, and low power consumption. In this paper we intend to provide a detailed review of recent advancements of such heart monitoring systems. We introduce the heart monitoring system in five modules: (1) body sensors, (2) signal conditioning, (3) analog to digital converter (ADC) and compression, (4) wireless transmission, and (5) analysis and classification. In each module, we provide a brief introduction about the function of the module, recent developments, and their limitation and challenges.

Design methodology of a new wavelet basis function for fetal phonocardiographic signals.

Fetal phonocardiography (fPCG) based antenatal care system is economical and has a potential to use for long-term monitoring due to noninvasive nature of the system. The main limitation of this technique is that noise gets superimposed on the useful signal during its acquisition and transmission. Conventional filtering may result into loss of valuable diagnostic information from these signals. This calls for a robust, versatile, and adaptable denoising method applicable in different operative circumstances. In this work, a novel algorithm based on wavelet transform has been developed for denoising of fPCG signals. Successful implementation of wavelet theory in denoising is heavily dependent on selection of suitable wavelet basis function. This work introduces a new mother wavelet basis function for denoising of fPCG signals. The performance of newly developed wavelet is found to be better when compared with the existing wavelets. For this purpose, a two-channel filter bank, based on characteristics of fPCG signal, is designed. The resultant denoised fPCG signals retain the important diagnostic information contained in the original fPCG signal.

Implementation of foetal e-health monitoring system through biotelemetry.

Continuous foetal monitoring of physiological signals is of particular importance for early detection of complexities related to the foetus or the mother's health. The available conventional methods of monitoring mostly perform off-line analysis and restrict the mobility of subjects within a hospital or a room. Hence, the aim of this paper is to develop a foetal e-health monitoring system using mobile phones and wireless sensors for providing advanced healthcare services in the home environment. The system is tested by recording the real-time Foetal Phonocardiography (fPCG) signals from 15 subjects with different gestational periods. The performance of the developed system is compared with the existing ultrasound based Doppler shift technique, ensuring an overall accuracy of 98% of the developed system. The developed framework is non-invasive, cost-effective and simple enough to be used in home care application. It offers advanced healthcare facilities even to the pregnant women living in rural areas and avoids their unnecessary visits at the healthcare centres.

A review and comparative analysis of recent advancements in fetal monitoring techniques.

Over the past few years, various devices and techniques have been developed for electronic fetal monitoring (EFM), which is performed during pregnancy or continuously during labor to ensure normal delivery of a healthy baby. We reviewed and analyzed the performance of currently used EFM techniques with the goal of determining a noninvasive, cost-effective alternative for use in the home environment. This review includes research papers, publications, web sources, product manuals, interviews, formal discussions, and other available literature with the goal of providing a comprehensive comparative analysis of all available EFM techniques. We relate some of the insights gained from reviewing a large number of resources.