![]() ![]() ![]() The aim of this paper is to propose a scalable context-aware framework for early detection of several cardiovascular diseases by continuous monitoring using smart sensors and utilizing the strength of cloud computing. ![]() This study aims to propose the basis for developing Decision Support Software (DSS) based on machine learning models. For this research, two models based on LSTM and ResNet34 neural networks were developed, which showed high accuracy, 98.71% and 93.64%, respectively. Following the transformation of the one-dimensional signals to three-dimensional signals, they were analyzed in the same sense. In this way, the signals were taken in one-dimensional format and analyzed using neural networks. sECGs are three-dimensional images (two dimensions in space and one in time). On the other hand, surface electrocardiography (sECG) is a little-explored technique for this diagnosis. In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. Wide validation over five different databases proves the robustness of this method. The proposed method is based on dynamic thresholding and simple decision rules, which makes this method computationally efficient. The overall sensitivity rate of 99.70% and positive predictivity rate of 99.69% have been achieved. The proposed method was applied to Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database (MIT-BIH AD), Fantasia Database (FTD), European ST-T Database (ESTD), MIT-BIH Noise Stress Test Database (NSTD), and Direct Fetal ECG Database (FTD) for its evaluation and validation. Kurtosis coefficient computation is used for discarding prominent T-wave and further this technique located the QRS-complex accurately in the raw ECG signal. The threshold value was automatically updated using the previous threshold value, R-peak amplitude, RR-interval, and RR-intervals means. Then baseline and root mean square (RMS) value of first three seconds of the signal are used for initial thresholding, later dynamic thresholding process was utilized to update the threshold value after the detection of four R-peaks. Next, the ECG is enhanced to the power third after multiplication followed by normalization and moving average process to retain dynamic QRS-complex. In this paper, A window-based FIR filter is used to eliminate the high-frequency noise. Abnormal and varying peaks, baseline wander and other noise are the main challenges in accurate QRS-complex detection. QRS-complex detection is a primitive step in the detection of cardiac disorder using electrocardiogram (ECG). Numerical examples from synthetic data and natural phenomena are given to demonstrate the power of this new method. For complex signals that are a superposition of several MIMFs with well-differentiated phase functions $\phi(t)$, a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series. This paper proposes the \emph$ provide innovative features for adaptive time series analysis.
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