St. Joseph Engineering College

About: St. Joseph Engineering College is a based out in . It is known for research contribution in the topics: WS-Policy & Web service. The organization has 238 authors who have published 261 publications receiving 2456 citations.

Automated detection and localization of myocardial infarction using electrocardiogram

Abstract: Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy ( E a x ), signal energy (?x), fuzzy entropy ( E f x ), Kolmogorov-Sinai entropy ( E k s x ), permutation entropy ( E p x ), Renyi entropy ( E r x ), Shannon entropy ( E s h x ), Tsallis entropy ( E t s x ), wavelet entropy ( E w x ), fractal dimension ( F D x ), Kolmogorov complexity ( C k x ), and largest Lyapunov exponent ( E L L E x ) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k-nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V5). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V3). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V3) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision.

Application of Lean Six Sigma methodology in the registration process of a hospital

Abstract: Purpose – The purpose of this paper is to study the scope of Lean Six Sigma (LSS) strategy and to explore its successful deployment in the Indian healthcare sector. Design/methodology/approach – The research reported in this paper is based on a case study carried out using the LSS DMAIC (Define-Measure-Analyze-Control) approach and its application in improving the registration process in the Health Information Department (HID) of a Medical College hospital in India. Findings – The study describes improvements in the HID through the power of LSS. Cycle time of the process was reduced from three to 1.5 minutes and the SD was reduced to 21.2 from 61 seconds. The project has shown a 94 per cent reduction in patients’ average waiting time. The study also reported a 91 per cent reduction in queue length and 48 per cent reduction in percentage of scheduled utilization of staff for the process. Research limitations/implications – The paper is based on a single case study executed in a hospital and hence there is ...

Structural, optical, mechanical and dielectric properties of titanium dioxide doped PVA/PVP nanocomposite

Abstract: We report preparation of Titanium dioxide (TiO2) nanoparticles doped PVA/PVP polymer nanocomposites by solvent casting technique. FTIR spectra and XRD studies signify the structural modifications taking place in the nanocomposites. The optical energy gap decreases by the addition of TiO2 nanoparticles. The mechanical properties such as tensile strength and Young’s modulus are enhanced by the addition of Titanium dioxide (TiO2) nanoparticles. The Fluorescence studies indicate that photoluminescence intensity is maximum for 4 wt% doping concentration of TiO2. The AFM analysis provides information about the surface roughness of the polymer film. The dielectric plot shows that the dielectric constant increases up to 12 wt% doping of TiO2, a further increase in the doping concentration results in the reduction of dielectric constant. The dielectric constant decreases with an increase in the frequency for all the films. The above properties show TiO2 doped PVA/PVP nanocomposite films are a prominent material for potential applications.

6G Ecosystem: Current Status and Future Perspective

Abstract: Next-generation of the cellular network will attempt to overcome the limitations of the current Fifth Generation (5G) networks and equip itself to address the challenges which become obvious in the future. Currently, academia and industry have focused their attention on the Sixth Generation (6G) network, which is anticipated to be the next big game-changer in the telecom industry. The outbreak of COVID’19 has made the whole world to opt for virtual meetings, live video interactions ranging from healthcare, business to education. However, we miss an immersive experience due to the lack of supporting technology. Experts have anticipated that starting from the post-pandemic age, the performance requirements of technology for virtual and real-time communication, the rise of several verticals such as industrial automation, robotics, and autonomous driving will increase tremendously, and will skyrocket during the next decade. In this manuscript, we study the latest perspectives and future megatrends that are most likely to drive 6G. Initially, we describe the instances that lead us to the vision of 6G. Later, we narrate some of the use cases and the KPIs essential to meet their performance requirement. Further, we highlight the key requirements of 6G based on contemporary research such as UN sustainability goals, business model, edge intelligence, digital divide, and the trends in machine learning for 6G.