Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Comparative Evaluation of C-Reactive Protein Levels among Peri-Implant Health and Disease Conditions.

Peri-implant disease pathogenesis results in production of pro-inflammatory mediators, among which C-reactive protein (CRP) is one of the acute phase reactants. The aim of the study was to comparative CRP levels among peri-implant health and disease conditions. The present study was carried out in the Department of Implantology, Saveetha Dental College and Hospitals, Chennai, India. A total of 40 patients with peri-implant health (n = 10), peri-mucositis (n = 10), early peri-implantitis (n = 10) and advanced peri-implantitis (n = 10) were enrolled. Unstimulated salivary samples were collected and subjected to latex agglutination assay for CRP analysis. CRP levels were then correlated with peri-implant health and diseases. CRP level in peri-implant health, peri-implant mucositis, early peri-implantitis and advanced peri-implantitis were 0.18 ± 0.04 mg/dL, 2.05 ± 0.61 mg/dL, 4.14 ± 1.82 mg/dL and 6.21 ± 1.35 mg/dL respectively. There was a statistically significant difference in CRP levels between all the tested groups (ANOVA, P = 0.03). Pearson correlation coefficient analysis revealed a strong positive correlation between CRP and peri-implant health status. CRP level was high among patients with peri-implantitis followed by peri-implant mucositis and peri-implant health. Also, CRP level increases with severity of peri-implant diseases and there exists a positive correlation between CRP level and peri-implant health status.

Randomized double-blind trial comparing effects of low-flow vs high-flow anesthesia on postoperative lung functions using respirometer.

BACKGROUND AND AIMS: Modern anesthetic practice utilizes low-flow anesthesia with evolving evidence on its pulmonary effects. Studies comparing measurement of vital capacity and inspiratory reserve volume using respirometer in both low-flow and high-flow anesthesia are sparse. We evaluated the effects of low-flow and high-flow anesthesia on postoperative pulmonary functions using respirometer. MATERIAL AND METHODS: This was a prospective randomized double blind study wherein One hundred and ten patients undergoing peripheral surgeries under general anesthesia were allocated into two groups Group I- Low-flow anesthesia with O2 + N2O + Sevoflurane (0.5L + 0.5L + 3.5%) and Group II- High-flow anesthesia with O2 + N2O + Sevoflurane (2L + 2L + 2%). The difference in vital capacity (VC), inspiratory reserve volume (IRV), and peak expiratory flow rates (PEFR) from the preoperative period were compared in both the groups postoperatively. RESULTS: The difference in VC, IRV, and PEFR measured in both the groups between the preoperative and postoperative period were found to be similar and statistically insignificant (P - 0.173, 1.00 and 0.213 respectively). The difference in single breath count (SBC), breath holding time (BHT), and respiratory rates (RR) were also similar in both the groups (P - 0.101, 0.698, and 0.467) respectively. CONCLUSIONS: The pulmonary effects of low-flow anesthesia are comparable with the high-flow ones in patients undergoing elective surgeries under general anesthesia.