Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.

A method for characterizing an electrocardiogram, including receiving a first unipolar signal from a first location of a heart and a second unipolar signal from a second location of the heart. The method further includes generating a bipolar signal from the first and second unipolar signals, and analyzing the bipolar signal to delineate a time period during which the first and second locations generate a bipolar complex. The method also includes analyzing the first unipolar signal within the time period to determine an activation time of the first location.

An apparatus has a bottom sphere and a top sphere connected to the bottom sphere to define an interior volume with a central plane. The top sphere includes a concave surface at an angle to the central plane. A motor is positioned within the interior volume. A processor is positioned within the interior volume and is connected to the motor. A memory is positioned within the interior volume and is connected to the processor. The memory stores instructions executed by the processor. The instructions include hand stimulation sessions. Each hand stimulation session comprises a sequence of cycles, where each cycle is a sequence of vibration intensity values applied to the motor.

A plurality of electrophysiological signals measured by a respective plurality of electrodes carried by a multi-dimensional catheter can be sorted relative to a direction of interest, such as a cardiac activation wavefront direction. An electroanatomical mapping system can be used to determine the orientation of the multi-dimensional catheter relative to the direction of interest. For example, the user can manually adjust the orientation by manipulating a slider, a wheel, or a similar graphical user interface control. As another example, the user can draw a presumed orientation on a geometric model. Once the orientation is determined, the system can sort the plurality of electrophysiological signals and output a graphical representation of the sorted plurality of electrophysiological signals, for example as a plurality of traces.

A vital information monitor (1) includes a vital sign acquiring section (39) which acquires vital signs of a patient into whom a tracheal tube (14) connected to a respirator (13) is intubated, a producing section (31) which produces an extubation process display screen (72) on which determination items contained in an extubation process for removing the tracheal tube (14) from the patient are displayed, a displaying section (7) on which the vital signs acquired by the vital sign acquiring section and the extubation process display screen (72) are displayed, and a determining section (30) which determines whether the determination items satisfy predetermined conditions.

Current technologies analyze electrocardiogram (ECG) signals for a long duration, which is not always a practical scenario. Moreover the current scenarios perform a binary classification between normal and Atrial Fibrillation (AF) only, whereas there are many abnormal rhythms apart from AF. Conventional systems/methods have their own limitations and may tend to misclassify ECG signals, thereby resulting in an unbalanced multi-label classification problem. Embodiments of the present disclosure provide systems and methods that are robust and more efficient for classifying rhythms for example, normal, AF, other abnormal rhythms and noisy ECG recordings by implementing a spectrogram based noise removal that obtains clean ECG signal from an acquired single-lead ECG signal, an optimum feature selection at each layer of classification that selects optimum features from a pool of extracted features, and a multi-layer cascaded binary classifier that identifies rhythms in the clean ECG signal at each layer of the classifier.

A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

Systems and methods can be used to provide an indication of heart function, such as an indication of mechanical function or hemodynamics of the heart, based on electrical data. For example, a method for assessing a function of the heart can include determining a time-based electrical characteristic for a plurality of points distributed across a spatial region of the heart. The plurality of points can be grouped into at least two subsets of points based on at least one of a spatial location for the plurality of points or the time-based electrical characteristics for the plurality of points. An indication of synchrony for the heart can be quantified based on relative analysis of the determined time-based electrical characteristic for each of the at least two subsets of points.

Systems and methods for detecting cardiac arrhythmias such as an atrial fibrillation (AF) are described herein. The AF detection system includes a sensor circuit to sense a physiological signal, a heartbeat processor to recognize a plurality of beat patterns using cycle length of two more consecutive cardiac cycles. The beat patterns can be indicative of temporal relationship between the consecutive cardiac cycles. The heartbeat processor may generate a repetitiveness indictor based on a statistical measurement of various beat patterns. The AF detection system includes an arrhythmia detector to detect an episode of AF based on the repetitiveness indictor, and to discriminate the AF from other arrhythmias of atrio-ventricular conduction abnormalities.

A device for measuring a ventricular-arterial coupling of a subject includes first and second inputs. The first input receives signals from a plurality of electrocardiogram sensors that are coupled to the subject at a plurality of first locations. The second input receives signals from a plurality of photoplethysmogram sensors that are coupled to the subject at a plurality of second locations. The second locations are selected from the group consisting of a head of the subject, an arm of the subject, and a leg of the subject. The signals received from the electrocardiogram sensors and the signals received from the photoplethysmogram sensors are received simultaneously. The device also includes a monitor configured to display the signals from the electrocardiogram sensors and the signals from the photoplethysmogram sensors.