Abstract: Techniques for determining reliability of electrocardiogram (ECG) data are disclosed. ECG data, including waveform data for a patient, is received. An amplitude associated with an R-wave in the waveform data is determined. First and second baseline regions in the waveform data, each relating to the R-Wave, are identified. A signal-to-noise ratio (SNR) is determined, based on the amplitude, the first baseline region, and the second baseline region. The SNR facilitates medical treatment for the patient.

Abstract: Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.

Abstract: Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.

Abstract: Techniques for determining reliability of electrocardiogram (ECG) data are disclosed. ECG data, including waveform data for a patient, is received. An amplitude associated with an R-wave in the waveform data is determined. First and second baseline regions in the waveform data, each relating to the R-Wave, are identified. A signal-to-noise ratio (SNR) is determined, based on the amplitude, the first baseline region, and the second baseline region. The SNR facilitates medical treatment for the patient.

Abstract: Techniques for classifying cardiac events in electrocardiogram (ECG) data. A feature set is generated by analyzing ECG data for a patient using a first phase in a machine learning architecture. A first cardiac event in the ECG data is classified based on the feature set, using the first phase in the machine learning architecture. A second cardiac event in the ECG data is classified based on the classified first cardiac event and the feature set, using a second phase in the machine learning architecture. The second cardiac event overlaps at least partially in time with the first cardiac event. Further, a plurality of feature sets, corresponding to a plurality channels of ECG data, are generated using paths in a machine learning architecture. A cardiac event in the ECG data is classified using the machine learning architecture based on the plurality of feature sets.

Abstract: Techniques for determining reliability of electrocardiogram (ECG) data are disclosed. ECG data, including waveform data relating to a detected heartbeat for a patient, is received. A peak amplitude associated with an R-wave in the waveform data is determined. First and second baseline regions in the waveform data are identified. The first baseline region precedes the R-wave and the second baseline region follows the R-wave. A signal-to-noise ratio (SNR) is determined, based on the peak amplitude, the first baseline region, and the second baseline region. A confidence metric relating to the waveform data is determined, based on the determined SNR. The confidence metric is used in medical treatment related to the patient.