Abstract: Described herein are techniques for analyzing at least one electrocardiogram (ECG) signal. In some embodiments, the techniques include: receiving at least one ECG signal; encoding the at least one ECG signal using the encoder to obtain a numeric encoding of the at least one ECG signal; and processing the numeric encoding of the at least one ECG signal using at least one trained machine learning model to obtain: (i) at least one denoised ECG signal corresponding to the at least one ECG signal, and/or (ii) characteristics of the at least one ECG signal, the characteristics comprising: (i) rhythm types including a respective rhythm type for each of at least some segments of the at least one ECG signal, and/or (ii) sample-level ECG labels including a respective sample-level ECG label for each of at least some of the plurality of samples.

Abstract: Described herein are techniques for analyzing at least one electrocardiogram (ECG) signal. In some embodiments, the techniques include: receiving at least one ECG signal; encoding the at least one ECG signal using the encoder to obtain a numeric encoding of the at least one ECG signal; and processing the numeric encoding of the at least one ECG signal using at least one trained machine learning model to obtain: (i) at least one denoised ECG signal corresponding to the at least one ECG signal, and/or (ii) characteristics of the at least one ECG signal, the characteristics comprising: (i) rhythm types including a respective rhythm type for each of at least some segments of the at least one ECG signal, and/or (ii) sample-level ECG labels including a respective sample-level ECG label for each of at least some of the plurality of samples.

Abstract: Methods and systems for automated electrocardiogram (ECG) analysis using neural networks, enhancing the accuracy of beat-by-beat cardiac monitoring. The system utilizes a Generative Adversarial Network (GAN) and beat classifiers to analyze ECG data and detect conditions various beast properties of an ECG at a discrete level. Additional neural networks may be trained to detect beat based conditions such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). The GAN generates realistic ECG beats, while classifiers detect abnormalities. Additional transformers may be trained to detect rhythm based conditions such as AFib and Aflutter. Methods and Systems support real-time cardiac health insights and integrates with ECG devices for continuous monitoring, offering a robust solution for improving diagnostic accuracy.

Abstract: Described herein are techniques for analyzing at least one electrocardiogram (ECG) signal. In some embodiments, the techniques include: receiving at least one ECG signal; encoding the at least one ECG signal using the encoder to obtain a numeric encoding of the at least one ECG signal; and processing the numeric encoding of the at least one ECG signal using at least one trained machine learning model to obtain: (i) at least one denoised ECG signal corresponding to the at least one ECG signal, and/or (ii) characteristics of the at least one ECG signal, the characteristics comprising: (i) rhythm types including a respective rhythm type for each of at least some segments of the at least one ECG signal, and/or (ii) sample-level ECG labels including a respective sample-level ECG label for each of at least some of the plurality of samples.

Abstract: Described herein are techniques for analyzing at least one electrocardiogram (ECG) signal. In some embodiments, the techniques include: receiving at least one ECG signal; encoding the at least one ECG signal using the encoder to obtain a numeric encoding of the at least one ECG signal; and processing the numeric encoding of the at least one ECG signal using at least one trained machine learning model to obtain: (i) at least one denoised ECG signal corresponding to the at least one ECG signal, and/or (ii) characteristics of the at least one ECG signal, the characteristics comprising: (i) rhythm types including a respective rhythm type for each of at least some segments of the at least one ECG signal, and/or (ii) sample-level ECG labels including a respective sample-level ECG label for each of at least some of the plurality of samples.

Abstract: Methods and systems for automated electrocardiogram (ECG) analysis using neural networks, enhancing the accuracy of beat-by-beat cardiac monitoring. The system utilizes a Generative Adversarial Network (GAN) and beat classifiers to analyze ECG data and detect conditions various beast properties of an ECG at a discrete level. Additional neural networks may be trained to detect beat based conditions such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). The GAN generates realistic ECG beats, while classifiers detect abnormalities. Additional transformers may be trained to detect rhythm based conditions such as AFib and Aflutter. Methods and Systems support real-time cardiac health insights and integrates with ECG devices for continuous monitoring, offering a robust solution for improving diagnostic accuracy.