A cardiac monitoring device comprising: at least one sensing electrode for obtaining an electrocardiogram 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; characterized by the comparison comprising: analyzing the ECG signal via the machine learning to provide a score for sliding ten-second windows with one-second overlap of the ECG signal; integrating the scores for each of the sliding windows into a master score as the ECG-derived score for the ECG signal; comparing the master score to a noise classification threshold as the predetermined threshold score; and provide an indication of whether an event is at least one of a ventricular tachycardia event and a ventricular fibrillation event or noise based on the comparison. at least one sensing electrode for obtaining an electrocardiogram 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; 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; characterized by the comparison comprising: analyzing the ECG signal via the machine learning to provide a score for sliding ten-second windows with one-second overlap of the ECG signal; integrating the scores for each of the sliding windows into a master score as the ECG-derived score for the ECG signal; comparing the master score to a noise classification threshold as the predetermined threshold score; and analyzing the ECG signal via the machine learning to provide a score for sliding ten-second windows with one-second overlap of the ECG signal; integrating the scores for each of the sliding windows into a master score as the ECG-derived score for the ECG signal; comparing the master score to a noise classification threshold as the predetermined threshold score; and provide an indication of whether an event is at least one of a ventricular tachycardia event and a ventricular fibrillation event or noise based on the comparison.