Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients. An ST-T interval is identified from the plurality of subwaveforms and discontinuity points based on the comparison, the ST-T interval is divided into N number of equally spaced sections, and an average data value of detection is calculated for each section. A table is displayed that includes an average data value of detection for each section of the ST-T interval.

A computer implemented method and system for detecting premature ventricular contractions (PVCs) are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a cycle length (CL) distribution metric that plots a series of cardiac beats into one of a set of transition types based on R-R interval (RRI) difference pairs associated with the cardiac beats. The CL distribution metric plots the cardiac beats based on a comparison between combinations of the RRI difference pairs for corresponding combinations of the cardiac beats. The method calculates a distribution characteristic for the cardiac beats, from the series of cardiac beats that exhibit a first transition type from the set of transition types and calculates a discrimination score based on the distribution characteristic of the cardiac beats across the CL distribution metric. The method designates the CA signals to include a predetermined level of PVC burden based on the discrimination score.

A system is provided for isolating the value of a signal hidden within a composite electrical signal. The system comprises an input, a processor, and a memory configured to store instructions executable by the processor. The instructions cause the processor to estimate that portion of a received composite electrical signal that represents a hidden signal by subtracting a known first signal from the composite signal.

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.

The present specification discloses systems and methods of patient monitoring in which multiple sensors are used to detect physiological parameters and the data from those sensors are correlated to determine if an alarm should, or should not, be issued, thereby resulting in more precise alarms and fewer false alarms. Electrocardiogram readings can be combined with invasive blood pressure, non-invasive blood pressure, and/or pulse oximetry measurements to provide a more accurate picture of pulse activity and patient respiration. In addition, the monitoring system can also use an accelerometer or heart valve auscultation to further improve accuracy.

A method includes receiving an electrocardiogram (ECG) measured at a given location over a portion of a heart. Based on the measured ECG, a rhythmic pattern is identified over a given time-interval. The rhythmic pattern corresponds to a relation between a present cardiac cycle length and a preceding cardiac cycle length. Based on the identified rhythmic pattern, a classification of the location as either showing regular pattern or showing arrhythmia is determined. The location is graphically encoded according to the classification. The graphically encoded location is overlaid on an anatomical map of the portion of a heart.

A method and system for determining psychological acute stress are disclosed. In a first aspect, the method comprises detecting a physiological signal using a wireless sensor device, determining a stress feature using a normalized heart rate and a plurality of heart rate variability (HRV) features, wherein the normalized heart rate and the plurality of heart rate variability features are calculated using the detected physiological signal, and determining a stress level using the stress feature to determine the psychological acute stress. In a second aspect, the system comprises a wireless sensor device that includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the wireless sensor device to carry out the steps of the method.

Systems, apparatus, and methods for ablation therapy are described herein, with a processor for confirming pacing capture or detecting ectopic beats. An apparatus includes a processor for receiving cardiac signal data captured by a set of electrodes, extracting a sliding window of the cardiac signal data, identifying a peak frequency over a subrange of frequencies associated with the extracted sliding window, detecting ectopic activity based at least on a measure of the peak frequency over the subrange of frequencies, in response to detecting ectopic activity, sending an indication of ectopic activity to a signal generator configured to generate pulsed waveforms for cardiac ablation such that the signal generator is deactivated or switched off from generating the pulsed waveforms. An apparatus can further include a processor for confirming pacing capture of the set of pacing pulses based on cardiac signal data.

Described herein are methods, software, systems and devices for detecting the presence of an abnormality in an organ, tissue, body, or portion thereof of a subject by analysis of the electromagnetic fields generated by the organ, tissue, body, or portion thereof.