The present disclosure is directed towards systems and methods built for predictively timing the inflation and/or deflation of an intra-aortic balloon pump. A controller operates in three states: (1) initialization state, (2) learning state, and (3) peak detection state. The controller decomposes a patient's electrocardiogram signal to a power signal. It then learns characteristics of the patient's electrocardiogram signal during the learning state and computes adaptive threshold parameter values. During the peak detection state, the controller applies the learnt threshold parameter values on a current electrocardiogram signal to identify occurrence and timings of R peaks in the electrocardiogram signal. The R-to-R peak timings are then used to trigger inflation of an intra-aortic balloon pump.

A physiological data detection method is provided. The physiological data detection method includes the following steps. Firstly, an ECG signal and a PPG signal are detected. Then, a plurality of RRI values is calculated according to the ECG signal, and a plurality of PPI values is calculated according to the PPG signal. Thereafter, wrong RRI values are excluded according to the RRI values and/or the PPI values. Then, whether an abnormal state occurs or not is determined by using the remaining RRI values. A wearable device therefor is also provided.

A device and method for analyzing of a disturbed pattern of pulse wave front results in a non-invasive, real-time diagnostic tool of arterial vascular performance on both a global and regional scale. The device provides a single number quantifying how well the arterial tree as a whole is coupled to receive and distribute a stroke volume of a single heartbeat. Changing heart rate, contractility, volume status, and afterload will change stroke volume and ejection time. Different vasculatures with different properties (e.g., size and intrinsic stiffness) will be best matched for different stroke volumes and ejection times to provide optimal coupling. The device will allow finding the optimal set of parameters for individual patient.

Described herein are implantable systems and devices, and methods for use therewith, that distinguish between different signal components of interest in sensed physiologic signals with high sensitivity and specificity. Such a method can include obtaining a sensed signal using an IMD and using a plurality of different filters that are parallel to one another to simultaneously filter the sensed signal, and/or copies thereof, to produce different filtered signals. Where each filter has a respective passband that does not substantially overlap with the passband(s) of the other filter(s), each of the different filtered signals will be indicative of different frequency content of the sensed signal. Additionally, amplitudes of temporally aligned peaks in at least two of the different filtered signals can be detected, and one or more peaks of the sensed signal can be classified based on the detected amplitudes of the temporally aligned peaks in the different filtered signals.

A method for accurately extracting an abnormal potential within a QRS, comprising: in an ideal electrocardiographic signal pre-estimation stage, pre-estimating an ideal electrocardiographic signal using a non-linear transformation technology;

according to the pre-estimated ideal electrocardiographic signal, further estimating the ideal electrocardiographic signal by using a spline method, so as to accurately estimate the ideal electrocardiographic signal; and according to the accurately estimated ideal electrocardiographic signal, accurately extracting an abnormal potential within the QRS by means of a mobile standard deviation analysis technology. The method can be used not only on an average electrocardiographic signal after multiple superimposition, but also on a single beat electrocardiographic signal.

A time series data analysis method according to an embodiment of the inventive concept is performed by a computing device. The method includes inputting, for each of a plurality of units, into which the time series data is split on a time axis, a feature of each of the units to an intermediate neural network, obtaining m-dimensional intermediate output data (m is a natural number of 2 or more) from the intermediate neural network, inputting the intermediate output data of a plurality of units immediately adjacent in time to a final neural network to obtain final output data output from the final neural network, and generating an analysis result of the time series data using the final output data.

Systems and methods of arrhythmia detection and associated apparatus that utilize machine learning techniques that allow for the consideration individual characteristics and the tailoring/personalization of biometric data allow for early detection and treatment, especially of cardiac arrhythmias and other abnormalities.

A heart monitoring system enables a person or user to create and evaluate and ECG without the assistance of a medical professional. A self-adhesive patch with leads is placed over the heart of the person and places the leads in an optimal position to thoroughly monitor the patterns emitted from the heart. The data samples are transmitted to a personal device for display and analysis.

An electrocardiogram analysis apparatus according to an embodiment of the present invention includes an inputter configured to receive an electrocardiogram of a subject, a beat analyzer configured to first detect and classify one or more normal beats among a plurality of beats included in the received electrocardiogram, request beat classification for remaining beats except for the one or more normal beats from a remote diagnosis server, and label each of the plurality of beats according to a normal beat detection result and a beat classification result received from the remote diagnosis server, and a rhythm analyzer configured to perform rhythm analysis on the electrocardiogram based on a labeling result.

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.