Abstract:
A wearable cardioverter defibrillator system includes a support structure that a patient can wear. The system also includes electrodes that contact the patient, and define two or more channels from which ECG signals are sensed. A processor may evaluate the channels by analyzing their respective ECG signals, to determine which contains less noise than the other(s). The analysis can be by extracting statistics from the ECG signals, optionally after first processing them, and then by comparing these statistics. These statistics may include tall peak counts, amplitudes of peaks compared to historical peak amplitudes, signal baseline shift, dwell time near a baseline, narrow peak counts, zero crossings counts, determined heart rates, and so on. Once the less noisy signal is identified, its channel can be followed preferentially or to the exclusion of other channels, for continuing monitoring and/or determining whether to shock the patient.
Abstract:
A wearable cardioverter defibrillator system includes a support structure that a patient can wear. The system also includes electrodes that contact the patient, and define two or more channels from which ECG signals are sensed. A processor may evaluate the channels by analyzing their respective ECG signals, to determine which contains less noise than the other(s). The analysis can be by extracting statistics from the ECG signals, optionally after first processing them, and then by comparing these statistics. These statistics may include tall peak counts, amplitudes of peaks compared to historical peak amplitudes, signal baseline shift, dwell time near a baseline, narrow peak counts, zero crossings counts, determined heart rates, and so on. Once the less noisy signal is identified, its channel can be followed preferentially or to the exclusion of other channels, for continuing monitoring and/or determining whether to shock the patient.
Abstract:
An external medical device can include a housing and a processor within the housing. The processor can be configured to receive an input signal for a patient receiving chest compressions from a mechanical chest compression device. The processor can also be configured to select at least one filter mechanism, the mechanical chest compression device having a chest compression frequency f. The processor can be further configured to apply the at least one filter mechanism to the signal to at least substantially remove chest compression artifacts from the signal, wherein the chest compression artifacts correspond to the chest compressions being delivered to the patient by the mechanical chest compression device, and wherein the at least one filter mechanism substantially rejects content in the frequency f plus content in at least one more frequency that is a higher harmonic to the frequency f.
Abstract:
Embodiments operate in contexts where field data have been generated from a field event, and annotations have been generated from the field data, which purport to identify events within the field data, such as CPR compressions and ventilations. Metrics are generated from the annotations, which are used in training. In such contexts, a grade may be assigned that reflects how well the annotations meet one or more accuracy criteria. The grade may be used in a number of ways. Reviewers may opt to disregard field data and metrics that have a low grade. Expert annotators may be guided as to precisely which annotations to revise, saving time. A low grade may decide that the results are not emailed to reviewers, but to annotators. A learning medical device can use the grade internally to adjust its own internal parameters so as to improve its annotating algorithms.
Abstract:
Embodiments operate in contexts where field data have been generated from a field event, and annotations have been generated from the field data, which purport to identify events within the field data, such as CPR compressions and ventilations. Metrics are generated from the annotations, which are used in training. In such contexts, a grade may be assigned that reflects how well the annotations meet one or more accuracy criteria. The grade may be used in a number of ways. Reviewers may opt to disregard field data and metrics that have a low grade. Expert annotators may be guided as to precisely which annotations to revise, saving time. A low grade may decide that the results are not emailed to reviewers, but to annotators. A learning medical device can use the grade internally to adjust its own internal parameters so as to improve its annotating algorithms.
Abstract:
An external medical device can include a housing and a processor within the housing. The processor can be configured to receive an input signal for a patient receiving chest compressions from a mechanical chest compression device. The processor can also be configured to select at least one filter mechanism, the mechanical chest compression device having a chest compression frequency f. The processor can be further configured to apply the at least one filter mechanism to the signal to at least substantially remove chest compression artifacts from the signal, wherein the chest compression artifacts correspond to the chest compressions being delivered to the patient by the mechanical chest compression device, and wherein the at least one filter mechanism substantially rejects content in the frequency f plus content in at least one more frequency that is a higher harmonic to the frequency f.