Computer implemented methods and systems for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a far field cardiac activity (CA) data set that includes far field CA signals for beats. The method applies a feature enhancement function to the CA signals to form an enhanced feature in the CA data set. The method calculates an adaptive sensitivity level and sensitivity limit based on the enhanced feature from one or more beats within the CA data set and automatically iteratively analyzes a beat segment of interest by comparing the beat segment of interest to the current sensitivity level to determine whether one or more R-waves are present within the beat segment of interest. The method repeats the iterative analyzing operation while progressively adjusting the current sensitivity level until i) the one or more R-waves are detected in the beat segment of interest and/or ii) the current sensitivity level reaches the sensitivity limit. The method detects an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves and records results of the detecting of the arrhythmia.

Computer implemented methods and systems for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a far field cardiac activity (CA) data set that includes far field CA signals for beats. The method applies a feature enhancement function to the CA signals to form an enhanced feature in the CA data set. The method calculates an adaptive sensitivity level and sensitivity limit based on the enhanced feature from one or more beats within the CA data set and automatically iteratively analyzes a beat segment of interest by comparing the beat segment of interest to the current sensitivity level to determine whether one or more R-waves are present within the beat segment of interest. The method repeats the iterative analyzing operation while progressively adjusting the current sensitivity level until i) the one or more R-waves are detected in the beat segment of interest and/or ii) the current sensitivity level reaches the sensitivity limit. The method detects an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves and records results of the detecting of the arrhythmia.

A method for analyzing arrhythmia in real time and an electrocardiogram monitoring device is disclosed. The method for analyzing arrhythmia in real time includes: acquiring a QRS template set currently used for arrhythmia analysis; determining whether each QRS template in the QRS template set is reliable; displaying information of a QRS template when the QRS template set contains an unreliable QRS template; determining the QRS template set according to an operation performed by a user on the information of the QRS template; and acquiring real-time electrocardiogram data, performing arrhythmia analysis on the real-time electrocardiogram data by using a determined QRS template set, and outputting an arrhythmia analysis result of the real-time electrocardiogram data in real time. The method for analyzing arrhythmia in real time may achieve the correction of a template of a special waveform by means of editing a currently created QRS template. When the same type of waveform is analyzed again, a correct arrhythmia analysis result may be provided, which may improve the accuracy of arrhythmia analysis and improve the quality of electrocardiogram monitoring.

A patient-worn ambulatory cardiac monitoring device for monitoring a patient during a patient activity includes at least one physiological sensor configured to detect signals indicative of cardiac activity, an activity sensor and associated circuitry configured to monitor patient movements, and a vibrational sensor configured to monitor a cardio-vibrational signal of the patient. The at least one physiological sensor can include one of an ECG sensor and a heart rate sensor. At least one processor in communication with the at least one physiological sensor, the activity sensor, and the vibrational sensor, is configured to measure, during the patient activity, at least one time interval between an ECG fiducial point in an ECG signal and a cardio-vibrational fiducial point in the cardio-vibrational signal during a cardiac cycle of the patient's heart.

A patient-worn ambulatory cardiac monitoring device for monitoring a patient during a patient activity includes at least one physiological sensor configured to detect signals indicative of cardiac activity, an activity sensor and associated circuitry configured to monitor patient movements, and a vibrational sensor configured to monitor a cardio-vibrational signal of the patient. The at least one physiological sensor can include one of an ECG sensor and a heart rate sensor. At least one processor in communication with the at least one physiological sensor, the activity sensor, and the vibrational sensor, is configured to measure, during the patient activity, at least one time interval between an ECG fiducial point in an ECG signal and a cardio-vibrational fiducial point in the cardio-vibrational signal during a cardiac cycle of the patient's heart.

A system for establishing a model for cardiac ventricular hypertrophy (VH) screening includes a storage and a processor. The storage stores multiple pieces of subject data respectively associated with multiple subjects. Each of the pieces of subject data contains a basic physiological parameter group, an electrocardiographic parameter group, and an actual VH condition that corresponds to a left or right ventricle of the subject associated with the piece of subject data. The processor is electrically connected to the storage, splits the pieces of subject data into a training set and a test set, and establishes the model for VH screening based on the pieces of subject data in the training set by using machine learning techniques.

A system for establishing a model for cardiac ventricular hypertrophy (VH) screening includes a storage and a processor. The storage stores multiple pieces of subject data respectively associated with multiple subjects. Each of the pieces of subject data contains a basic physiological parameter group, an electrocardiographic parameter group, and an actual VH condition that corresponds to a left or right ventricle of the subject associated with the piece of subject data. The processor is electrically connected to the storage, splits the pieces of subject data into a training set and a test set, and establishes the model for VH screening based on the pieces of subject data in the training set by using machine learning techniques.

Embodiments relate to a sensor system for a brain computer interface (BCI) that enable detection and decoding of brain activity by optical tomography. The sensor system includes an array of pixels arranged as grouped pixel units to provide increased dynamic range. One or more of the grouped pixel units can operate in a saturated mode while providing information useful for decoding brain activity. Furthermore, the grouped pixel units are arranged to enable fast readout by a pixel scanner, thereby increasing detection and decoding ability by systems implementing the sensor design. The grouped pixel units of the sensor system are aligned with optical fibers of an interface to a body region of a user, where the optical fibers can be retained in position relative to the grouped pixel units by an optically transparent substrate that provides mechanical support while minimizing factors associated with divergence of light transmitted through optical fibers.

In embodiments, a wearable cardioverter defibrillator (WCD) system includes a support structure for wearing by an ambulatory patient. When worn, the support structure maintains electrodes on the patient's body. Different pairs of these electrodes define different channels, and different patient ECG signals can be sensed from the channels. The ECG signals can be analyzed to determine which one is the best to use, for the WCD system to make a shock/no shock decision. The analysis can be according to widths of the QRS complexes, consistency of the QRS complexes, or heart rate agreement statistics.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.