A method enables analysis of (e.g. bioelectric) signals acquired by measurement. The method provides N signals U for an observation space and each has a time- and space-dependent signal characteristic U. Digitized signals for a time period T have M time points and define an M×N matrix with M tuples of N signal values each. Signal values acquired at time t form an N-tuple Ū.sub.t=(U.sub.1, . . . , U.sub.N).sub.t in a signal space. The method acquires all combinations of k tuples from the M tuples, and calculates distances between all tuples. Distance values are calculated and define edge lengths of a (k−1) simplex (SIM) with one simplex assigned to each combination of k time points. Quantity characteristics of the simplex (SIM) are encoded into color values (COL), and displays the colors in a combinatorial time lattice (CTL). Each lattice point (GP) is displayed with the color encoded for the assigned simplex.

A display control device includes processing circuitry configured to: obtain an intracardiac electrocardiogram of a subject; generate visualization data representing an excitation state of a myocardium based on the intracardiac electrocardiogram; determine a type of excitation dynamics of the myocardium based on the visualization data; and display, on a stereoscopic image of a heart of the subject, a site from which the intracardiac electrocardiogram is obtained with a color in accordance with a ratio of the determined type of the excitation dynamics.

A display control device includes processing circuitry configured to: obtain an intracardiac electrocardiogram of a subject; generate visualization data representing an excitation state of a myocardium based on the intracardiac electrocardiogram; determine a type of excitation dynamics of the myocardium based on the visualization data; and display, on a stereoscopic image of a heart of the subject, a site from which the intracardiac electrocardiogram is obtained with a color in accordance with a ratio of the determined type of the excitation dynamics.

A defibrillator and method for using a defibrillator which adopts an ECG analysis algorithm that can detect a cardiac arrhythmia in the presence of noise artifact induced by cardio pulmonary resuscitation (CPR) compressions. The apparatus and method offers guidance throughout a cardiac rescue protocol involving both defibrillation shocks and CPR that improves the effectiveness of the rescue, resulting in more CPR “hands-on” time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The processed ECG data is communicated from the server(s) for display in a user-friendly and interactive manner with enhanced accuracy.

A method and device for monitoring a vital sign of a user, the method including: acquiring a sensor signal related to a vital sign; sampling the sensor signal to obtain data related to the vital sign; and in response to a key event, adjusting the method for collecting and processing the sensor signal.

A method and device for monitoring a vital sign of a user, the method including: acquiring a sensor signal related to a vital sign; sampling the sensor signal to obtain data related to the vital sign; and in response to a key event, adjusting the method for collecting and processing the sensor signal.

An apparatus for producing information indicative of cardiac condition includes a processing system (301) for receiving a rotation signal indicative of rotational movement of a chest. The processing system is configured to form one or more indicator quantities each being derivable from an energy spectral density based on one or more samples of the rotation signal, where each sample has a temporal length. The processing system is configured to determine an indicator of cardiac condition on the basis of the one or more indicator quantities. The determination of the cardiac condition is based on that for example myocardial infarction causes changes in the energy spectral density. Thus, it is possible to distinguish between for example myocardial infarction and plain heartburn.

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 or to a set of rules developed from the database. An ST segment and one or more ST subwaveforms within the ST segment are identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the one or more ST subwaveforms within the ST segment is displayed.

There is provided a respiration estimation apparatus. The respiration estimation apparatus includes an R-wave amplitude detection unit (5) configured to detect an amplitude of an R wave from a cardiac potential waveform of a subject, an R-R interval detection unit (6) configured to detect an R-R interval as an interval between an R wave and an immediately preceding R wave from the cardiac potential waveform, an acceleration displacement detection unit (7) configured to detect an angular displacement of an acceleration vector from a triaxial acceleration signal by a respiratory motion of the subject, a Fourier transform unit (10) configured to Fourier-transform each of time-series signals of the R-wave amplitude, the R-R interval, and the angular displacement to obtain a frequency spectrum of each of the signals of the R-wave amplitude, the R-R interval, and the angular displacement, and a signal selection unit (11) configured to extract a frequency as a candidate of a respiration frequency of the subject from each of the frequency spectrum of the R-wave amplitude, the frequency spectrum of the R-R interval, and the frequency spectrum of the angular displacement, and select best data from the frequencies as the respiration frequency of the subject.