Apparatus and method are provided to collect and analyze heartbeat waveforms. In one novel aspect, the heartbeat waveforms are collected from wearable devices. In one embodiment, the wearable device collects heartbeat waveforms by attaching the device to the patient for a long period and sends the collected waveforms to a receiver through a wireless network. In another novel aspect, an application program is installed in a smart device to receive heartbeat waveforms from one or more wearable devices. The application program either relays the received waveform to a remote processing center or processes the data before sending. In another novel aspect, an analysis method compares received patient's current heartbeat waveform with historic data. In one embodiment, the historic data are stored in a cloud-based database. In another novel aspect, the remote processing center is an open platform data center, which takes in certified third party inputs.

A system for marking cardiac time intervals from heart valve signals includes a non-invasive sensor unit for capturing electrical signals and composite vibration objects, a memory containing computer instructions, and one or more processors coupled to the memory. The one or more processors causes the one or more processors to perform operations including separating a plurality of individual heart vibration events into heart valve signals from the composite vibration objects, and marking cardiac time interval from the heart valve signals by detecting individual heartbeats and processing cumulative energy within the individual heartbeat to set a threshold to set a marking point.

A system and method for electrocardiography and actigraphy data processing with the aid of a digital computer are provided. Samples of actigraphy event occurrences and samples of electrocardiographic signals are retrieved from a memory of a physiological monitor, wherein the samples of the actigraphy event occurrences are embedded among the samples of the electrocardiographic signals based on a time associated each of the samples of the actigraphy event occurrences and a time associated with each of the samples of the electrocardiographic signals. An actionable actigraphy event occurrence is identified from the samples of the actigraphy event occurrences. Those samples of the electrocardiographic signals that were sensed substantially concurrent to a time of the occurrence of the actionable actigraphy event are identified based on the embedding. The actionable event occurrence and the samples of the electrocardiographic signals that were identified are output.

Systems, methods and devices for reducing noise in cardiac monitoring including wearable monitoring devices having at least one electrode for cardiac monitoring; in some implementations, the wearable device using a composite adhesive having at least one conductive portion applied adjacent the electrode; and, in some implementations, including circuitry adaptations for the at least one electrode to act as a proxy driven right leg electrode.

When a user uses a finger to press and hold a first candidate display area, the first candidate display area is enclosed in a selection frame (W1), a simple window (W10) is displayed, and analysis results relating to a first candidate segment waveform are displayed in this simple window (W10). Analysis results can therefore be confirmed without switching screens and, as a result, inspection results can be confirmed with fewer steps and an electrocardiographic waveform and analysis results therefor can be compared on the same screen.

An electrocardiogram analyzer includes a first acquiring section that acquires a body surface electrocardiogram of a subject, a second acquiring section that acquires an intracardiac electrocardiogram of a ventricle of a heart of the subject, and an analyzing section that performs a frequency analysis on the intracardiac electrocardiogram and includes a range setting section that sets an analysis time range of the frequency analysis in the intracardiac electrocardiogram based on a unit waveform of the body surface electrocardiogram, and a calculating section that, in the analysis time range, performs the frequency analysis on the intracardiac electrocardiogram, and that calculates an index value indicating a ratio of local abnormal ventricular activities in the intracardiac electrocardiogram.

A self-authenticating electrocardiography and physiological sensor monitor is provided. An electrode patch includes an elongated strip and electrodes exposed on each end. A receptacle is adhered to the elongated strip and includes electrical pads. Circuit traces are electrically coupled to the electrocardiographic electrodes and the electrical pads. A monitor recorder having a sealed housing is adapted to be secured into the receptacle. Circuitry within the housing includes a microcontroller with a private key. A copy of the private key is stored on the patch. Self-authentication is performed each time the monitor recorder is inserted into a new patch by challenging the patch using a code hashed with the private key and by receiving a response from the patch in reply to the challenge. A front end circuit senses electrocardiographic signals via electrodes on the patch when the response received by the microcontroller is positive, until the electrode patch expires.

Disclosed systems include mobile ECG sensors, systems and methods. Some embodiments provide ECG sensors in a credit card form factor that allows a user to contact two electrically isolated electrodes to measure heart electrical signals for a Lead 1 ECG.

A computer Implemented method and system 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 far field cardiac activity (CA) signals and applies a direction related responsiveness (DRR) filter to the CA signals to produce DRR filtered signals. The method compares a current sample from the CA signals to a prior sample from the DRR filtered signals to identify a direction characteristic of the CA signals and defines the DRR filter based on a timing constant that is set based on the direction characteristic identified. The method analyzes the CA signals in connection with the DRR filtered signals to identify a peak characteristic of the CA signals and determines peak to peak intervals between successive peak characteristic. The method detects at least one of noise or an arrhythmia based on the peak to peak intervals and records results of the detecting.

Systems, methods and devices for providing combined ultrasound, electrocardiography, and auscultation data are provided. One such system includes an ultrasound sensor, an electrocardiogram (EKG) sensor, an auscultation sensor, and a computing device. The computing device includes memory and a processor, and the processor receives signals from the ultrasound sensor, the EKG sensor, and the auscultation sensor. Artificial intelligence techniques may be employed for automatically analyzing the data obtained from the ultrasound sensor, the EKG sensor, and the auscultation sensor and producing a clinically-relevant determination based on a combined analysis of the data.