Background: Manual analysis of ECG reports can be time-consuming and difficult to perform. Machine learning (ML) models have shown higher success when trained on raw time-series rather than images.

Nichols' execution is scheduled for Dec. 11. He is hoping to get records related to lethal injection in Tennessee. Harold Wayne Nichols, who has two weeks to decide how the state will kill him, is ...

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A digital innovation initiative about fault anomalies has been selected as one of the first projects for the new Microsoft AI Co-Innovation Lab. GREEN BAY, Wis. - June 27, 2025 - BW Converting is ...

Abstract: This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular ...

ECG-based machine learning offers a promising, interpretable approach for liver disease detection, particularly in resource-limited settings. By revealing clinically relevant biomarkers, this method ...

WaveformNet is a deep learning project for classifying electrocardiogram (ECG) signals into multiple arrhythmia types using convolutional neural networks. It features two separately trained models: a ...

Hello! I am Stanisalv Kotlyarov , an aspiring Data Analyst with a passion for data science, machine learning, and problem-solving. I am currently working on enhancing my data analysis skills and ...

Abstract: In a global health context outlined by the World Health Organization (WHO), the escalating prevalence of CVDs underscores a pressing need for advanced diagnostic methodologies. CVDs remain a ...