A computer-implemented method for direct photoplethysmography or direct PPG comprises obtaining during a time interval plural PPG signals for respective sensors in a wearable device; and combining the plural PPG signals to thereby obtain a multi-sensor PPG signal.

Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Method and apparatus for performing automatic cardiac diagnosis. The apparatuses described herein may be handheld devices which enables self-recording of cardiac signals by the patient, including entering relevant data by patients regarding their cardiac history, including cardiac disease risk factors, and/or current conditions and symptoms. Based on recorded cardiac signals, cardiac risk factors and the current symptoms, the apparatus may calculates a cardiac risk score and may provide simplified diagnostic information and actionable instructions to the patient.

A process for measuring heart beats fiducial points and classifying heart beats includes sampling a raw ECG signal,—providing a first filtered signal,—providing a second filtered signal, detecting left and right limits data for the beats in said second filtered signal, and receiving the first filtered signal and receiving said left and right limits data, sampling and storing ECG curve data of beats from said first filtered signal synchronized by said left and right limits data and extracting fiducial points of said beats from said ECG curve data, said fiducial points including at least the QRS points values of the beats, and classifying each of said beats in classes based on a correlation of its ECG curve data with respect to average ECG curves data of classes of previously averaged classified beats.

Systems and methods for detecting arrhythmias in cardiac activity are provided and include memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining first and second far field cardiac activity (CA) data sets over primary and secondary sensing channels, respectively, in connection with a series of beats. The system detects candidate atrial features from the second CA data set, identifies ventricular features from the first CA data set and utilizes the ventricular features to separate beat segments within the second CA data set. The system automatically iteratively analyzes the beat segments by overlaying an atrial activity search window with the second CA data set and determines whether one or more of the candidate atrial features occur within the atrial activity search window. The system adjusts an atrial sensitivity profile based on whether the atrial activity search window includes the one or more of the candidate atrial features and detects atrial events based on the atrial sensitivity profile.

Systems and methods for detecting arrhythmias in cardiac activity are provided and include memory to store specific executable instructions. One or more processors are configured to execute the specific executable instructions for obtaining first and second far field cardiac activity (CA) data sets over primary and secondary sensing channels, respectively, in connection with a series of beats. The system detects candidate atrial features from the second CA data set, identifies ventricular features from the first CA data set and utilizes the ventricular features to separate beat segments within the second CA data set. The system automatically iteratively analyzes the beat segments by overlaying an atrial activity search window with the second CA data set and determines whether one or more of the candidate atrial features occur within the atrial activity search window. The system adjusts an atrial sensitivity profile based on whether the atrial activity search window includes the one or more of the candidate atrial features and detects atrial events based on the atrial sensitivity profile.

A belt used in an electrocardiographic measurement apparatus includes a belt body to be wrapped around a living body, three or more base electrodes disposed in a longitudinal direction of the belt body, and two or more cap electrodes detachable and attachable to the base electrodes and smaller in number than the number of base electrodes.

An ECG monitoring device includes a vibration meter sensor unit including at least one vibration meter sensor attached to an instrument at which a person to be observed is positioned, and configured to acquire a vibration signal by detecting a vibration transmitted through the instrument in a non-contact or non-invasive method, a filter unit configured to extract a seismocardiography signal (“SCG signal”) generated by a heart vibration of the person to be observed by receiving the vibration signal and filtering a predetermined frequency band from the received vibration signal, and an ECG waveform acquisition unit including an artificial neural network learned in advance and configured to generate an electrocardiogram signal (“ECG signal”) corresponding to the applied SCG signal according to a learned method.

An ECG monitoring device includes a vibration meter sensor unit including at least one vibration meter sensor attached to an instrument at which a person to be observed is positioned, and configured to acquire a vibration signal by detecting a vibration transmitted through the instrument in a non-contact or non-invasive method, a filter unit configured to extract a seismocardiography signal (“SCG signal”) generated by a heart vibration of the person to be observed by receiving the vibration signal and filtering a predetermined frequency band from the received vibration signal, and an ECG waveform acquisition unit including an artificial neural network learned in advance and configured to generate an electrocardiogram signal (“ECG signal”) corresponding to the applied SCG signal according to a learned method.