System and method for distinguishing a cardiac event from noise in an electrocardiogram (ECG) signal
First Claim
1. A cardiac monitoring device comprising:
- at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient;
a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and
at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to;
obtain the ECG signal from the at least one sensing electrode;
determine a transformed ECG signal based on the ECG signal;
extract at least one value representing at least one feature of the transformed ECG signal;
provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score;
compare the ECG-derived score to a predetermined threshold score determined by machine learning; and
provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score,wherein the machine learning is one of a multivariate adaptive regression splines classifier and a neural network classifier.
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Abstract
A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning.
42 Citations
22 Claims
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1. A cardiac monitoring device comprising:
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at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to; obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score, wherein the machine learning is one of a multivariate adaptive regression splines classifier and a neural network classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A cardiac monitoring device comprising:
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at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to; obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score, wherein the machine learning is based on a training data set comprising a collection of ECG signals associated with treatments performed by a plurality of defibrillators. - View Dependent Claims (19)
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20. A cardiac monitoring device comprising:
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at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to; obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score, wherein the machine learning is based on a training data set comprising a collection of ECG signals stored in a memory of the cardiac monitoring device.
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21. A cardiac monitoring device comprising:
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at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to; obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score, wherein the transformed ECG signal comprises a power spectral density (PSD) of the ECG signal, the PSD being determined by calculating a fast Fourier transform (FFT) of the ECG signal and at least four features of the PSD are extracted and provided to the machine learning.
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22. A cardiac monitoring device comprising:
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at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to; obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event based on the comparison of the ECG-derived score with the predetermined threshold score, wherein the transformed ECG signal comprises a power spectral density (PSD) of the ECG signal, the PSD being determined by calculating a fast Fourier transform (FFT) of the ECG signal and determining the PSD comprises calculating the fast Fourier transform (FFT) of the ECG signal and performing a square of a modulus of the FFT to transform the FFT into a real number.
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Specification