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 includes a confidence analyzer circuit which determines the confidence level of an electrotherapy shock decision based on the detection. If the confidence level is low, the apparatus adjusts its shock decision criteria.

A defibrillator (AED) using two different ECG analysis algorithms which work sequentially to improve the accuracy of AED shock decisions. A first algorithm, such as (ART), is particularly suited for analysis in the presence of CPR periods. A second algorithm, such as (PAS), is particularly suited for analysis during hands-off periods. The AED switches algorithms depending on the period and on the current analysis of the cardiac rhythm. The inventions thus provide an optimized ECG analysis scheme in a manner 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.

A system and method for non-invasively detecting abnormal electrical propagation in the heart are disclosed. The system and method include an interface for receiving a pacing signal applied to a heart of a patient, the pacing signal comprising (i) a sequence of regular pacing stimuli shorter than the sinus-rate intervals, and (ii) one or more extra pacing stimuli at intervals that are shorter than the regular pacing stimuli, a processor to assess the envelope of a body-surface ECG component after the regular pacing stimuli, assess the envelope of a body surface ECG component after the one or more extra pacing stimuli, and compare the assessed component after the extra pacing stimuli to the assessed component after the regular pacing stimuli. The interface outputting the comparison as an indication of regions of arrhythmogenicity and ablation targets in the heart.

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.

An arousal-level determining apparatus includes a generating unit, a calculating unit, an identifying unit, and a determining unit. The generating unit generates heartbeat-interval variation data, which indicates changes in heartbeat interval, on the basis of heartbeat signals indicating subject's heartbeats. The calculating unit applies a band-pass filter, which allows passage of a certain range of frequencies, to each frequency band in the heartbeat-interval variation data while changing the frequency band, and calculates spectral density with respect to each frequency band applied with the band-pass filter. The identifying unit identifies a feature point corresponding to a spectral density peak in the calculated spectral densities in the frequency bands. The determining unit determines subject's arousal level on the basis of the identified feature point.

A computer-implemented method of heart rate estimation includes receiving heart beat data, detecting sequential beats within the heart beat data, identifying a beat interval of each sequential beat, and generating a beat array containing the beat intervals of sequential beats within an array window. The beat array is then sorted based on the beat intervals of the sequential beats so as to generate a sorted beat array. A weight array is calculated by applying a weight control parameter to each beat interval in the sorted beat array, wherein the weight array includes a weight value for each beat interval that is proportional to a corresponding beat interval value in the sorted beat array. A weighted median is calculated based on the weight array, and a heart rate estimation for the array window is determined based on the weighted median of the weight array and the sorted beat array.

A method and associated apparatus combine a calculation of chaos of a quantifiable cardiac characteristic associated with a patient with a measurement of a non-increasing parasympathetic activity of the patient to detect a physiological abnormality of the patient.

Disclosed is a human body frequency diagnostic analysis apparatus including a transceiver with an end connected to a human head and driven to transmit a transmission frequency and drive the human head to feed back a feedback frequency, a detection unit connected to the transceiver for driving the transceiver to transmit the transmission frequency and receive the feedback frequency, and a control unit connected to the detection unit for controlling the detection unit and includes a computing unit for computing and comparing the transmission frequency with the feedback frequency to generate a dual line spectrum, a database for comparing the dual line spectrum to generate detected information, and a screen for displaying the dual line spectrum and the detected information. A maximum drop value detected in a frequency section of the dual line spectrum indicates an imbalance of a human body frequency.

An ECG detection method includes in a wearable device, a first electrode detects a first electrical signal and a second electrode detects a second electrical signal, a processor determines a frequency bandwidth based on a current status of the wearable device and/or a status of a user in contact with the first electrode and the second electrode, and the processor determines an electrocardiogram ECG based on an electrical signal that is in the first electrical signal and the second electrical signal and for which a frequency falls within the frequency bandwidth. The wearable device selects an appropriate frequency bandwidth based on the status of the wearable device and/or the status of the user in contact with the first electrode and the second electrode, and then obtains the ECG based on the frequency bandwidth.

An ECG detection method includes in a wearable device, a first electrode detects a first electrical signal and a second electrode detects a second electrical signal, a processor determines a frequency bandwidth based on a current status of the wearable device and/or a status of a user in contact with the first electrode and the second electrode, and the processor determines an electrocardiogram ECG based on an electrical signal that is in the first electrical signal and the second electrical signal and for which a frequency falls within the frequency bandwidth. The wearable device selects an appropriate frequency bandwidth based on the status of the wearable device and/or the status of the user in contact with the first electrode and the second electrode, and then obtains the ECG based on the frequency bandwidth.