md. moniruzzaman

Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals. This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD.  The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

To examine the rain-fed Aman rice yield fluctuation due to climatic anomalies overtimes in Bangladesh, we used climate-induced yield index (CIYI), ensemble empirical mode decomposition (EEMD), step-wise multiple regression, isotopic signature, wavelet transform coherence (WTC) and random forest (RF) model. In this work, daily multiple source climatic data which were collected between 1980 and 2017, from 18 weather stations and five atmospheric circulation indices were used for this purpose. The key findings were as follows; by employing principal component analysis (PCA), six temporal variability modes were identified as six corresponding sub-regions with various Aman rice CIYI fluctuations. The Aman rice CIYI in different sub-regions represented a noteworthy 3–4-year quasi-oscillation using the EEMD. The key climate variables (KCVs) including the potential evapotranspiration and cloud cover in September, the minimum temperature in August, and precipitation in July, August, and October were the best rice yield prediction signals in these sub-regions. The results suggest that Aman rice yield could likely decline by 33.59%, and 3.37% in the southwestern and southeastern regions, respectively, if KCV increased by 1 °C or 1%. The RF model suggests that the Indian Ocean Dipole (IOD) significantly influenced the rice yield. Isotopic signatures were employed to confirm the fluctuation and anti-amount effect during the Aman rice-growing period in Bangladesh. The results obtained in this study could be used as a guideline for sustainable mitigation and adaptation measures in managing agro-meteorological hazards in Bangladesh.