This disclosure is directed towards detecting artifacts in an ECG signal. An ECG system may include multiple sensors which can sense an ECG signal when attached to a patient. Bipolar leads connect the sensors, and provide the ECG signal from the sensors to a computing device. The computing device receives respective signals from the bipolar leads, where the respective signals are indicative of the ECG signal. The computing device identifies, based on the respective signals, a potential artifact corresponding to a subset of the plurality of bipolar leads. The computing device determines that each lead of the subset of the plurality of bipolar leads is connected to a common sensor. The computing device may use signals originating from a remainder of the bipolar leads (e.g., the bipolar leads that are not connected to the sensor(s) where the artifact is detected) to detect a condition of the patient.

The present invention relates to an electrocardiogram measurement apparatus (measurement sensor) which can be used in combination with a smartphone by an individual. The electrocardiogram measurement apparatus according to the present invention comprises: two amplifiers for receiving electrocardiogram signals from a first electrode and a second electrode; one electrode driving unit; a third electrode for receiving an output of the electrode driving unit; an A/D converter connected to an output terminal of each of the two amplifiers and converting analog signals into digital signals; a microcontroller for receiving the digital signals from the A/D converter; and a communication means for transmitting the digital signal, wherein: the microcontroller is supplied with power from a battery; the microcontroller controls the A/D converter and the communication means; and each of the two amplifiers amplifies one electrocardiogram signal so as to simultaneously measure two electrocardiogram signals.

The present invention relates to an electrocardiogram measurement apparatus (measurement sensor) which can be used in combination with a smartphone by an individual. The electrocardiogram measurement apparatus according to the present invention comprises: two amplifiers for receiving electrocardiogram signals from a first electrode and a second electrode; one electrode driving unit; a third electrode for receiving an output of the electrode driving unit; an A/D converter connected to an output terminal of each of the two amplifiers and converting analog signals into digital signals; a microcontroller for receiving the digital signals from the A/D converter; and a communication means for transmitting the digital signal, wherein: the microcontroller is supplied with power from a battery; the microcontroller controls the A/D converter and the communication means; and each of the two amplifiers amplifies one electrocardiogram signal so as to simultaneously measure two electrocardiogram signals.

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

Methods and systems are provided for automatically determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a deep neural network may be trained to map an ECG beat to a phase shift insensitive and noise insensitive feature space embedding using a training data triad, wherein the training data triad may be produced by a method comprising: selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad.

Methods and systems are provided for automatically determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a deep neural network may be trained to map an ECG beat to a phase shift insensitive and noise insensitive feature space embedding using a training data triad, wherein the training data triad may be produced by a method comprising: selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad.

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.

A method is described for extracting aperiodic components from a time-series wave data set for diagnosis purposes. The method may include collecting time-series wave data within a controlled environment were a plurality of contrasting conditions can be used in collecting the time-series wave data set. Aperiodic components can be extracted from the time-series wave data set and the aperiodic components can then be fitted to the plurality of contrasting conditions of the controlled environment to product regressed aperiodic components from which diagnostic determination can be made.

A defibrillator and method for using a defibrillator which adopts an ECG analysis algorithm that can detect a cardiac arrhythmia in the presence of noise artifact induced by cardio pulmonary resuscitation (CPR) compressions. The apparatus and method offers guidance throughout a cardiac rescue protocol involving both defibrillation shocks and CPR that improves the effectiveness of the rescue, resulting in more CPR “hands-on” time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.