Recognizing ECG cardiac arrhythmia automatically is an essential task for diagnosing the abnormalities of cardiac muscle. The proposal of few algorithms has been made for classifying the ECG cardiac arrhythmias, however the system of classification efficiency is determined on the basis of its prediction and diagnosis accuracy. Hence, in this study the proposal of an efficient system has been made for classifying the ECG cardiac arrhythmia as an expertise. Discrete Wavelet Transform (DWT) is being utilized for the preprocessing mechanism of ECG signal, Independent Component Analysis (ICA) is being utilized for dimensionality reduction and Feature Extraction process of ECG signal and Multi-Layer Perceptron (MLP) neural network is being utilized for performing the task of classification. As an outcome of classification, the results have been acquired on categorizing Normal Beats under the class of Non-Ectopic beat, Atrial Premature Beat under the class of Supra-Ventricular ectopic beat and Ventricular Escape beat under the class of Ventricular ectopic beat on the basis of standardization given by ANSI/AAMI EC57: 1998. For the acquisition of ECG signal, MIT-BIH physionet arrhythmia database is being utilized in this study added to that its being utilized for training process and testing process of the classifier on the basis of MLP-NN. The results obtained from the simulation has been inferred that the accuracy of classification of the proposed algorithm is 96.50% on utilizing 10 files inclusive of normal beats, Atrial Premature Beat and Ventricular Escape beat.

The primary emphasis of this research paper lies in advancing versatile quantum cascade laser-based spectroscopic methods tailored for highly sensitive gas detection. These techniques hold promise for a range of applications including atmospheric sensing, biomedical science, and high-resolution fundamental molecular spectroscopy. Quantum Cascade Lasers (QCLs) have revolutionized spectroscopic techniques and trace gas analysis due to their unique properties. In spectroscopy, QCLs offer widely tunable, high-powered, and narrow-linewidth radiation across the mid-infrared (MIR) region, enabling precise identification and quantification of molecular species. Utilizing a continuous wave cavity ring-down spectrometer (CW-CRDS) with high resolution, coupled with an external cavity continuous-wave quantum cascade laser (CW-EC-QCL) functioning within the wavenumber interval from 1250 cm⁻¹ to 1333 cm⁻¹ enables spectroscopic examination. This has found applications in various fields namely, Atmospheric Sensing, Biomedical Science, Security and Defense, Environmental Monitoring, Industrial Process Control, Astrophysics and Astronomy. In trace gas analysis, QCLs offer unmatched sensitivity and selectivity. They can detect minuscule concentrations of gases, down to parts per trillion or even lower, making them indispensable in fields where precise gas measurements are paramount. This includes environmental monitoring, medical diagnostics, industrial safety, and scientific research. The versatility and precision of QCL-based spectroscopic techniques continue to drive innovation and advancements in these diverse fields.