264 results about "T wave" patented technology

A method for identifying and classifying various types of oversensing in implantable medical devices (IMDs), such as implantable cardioverter defibrillators (ICDs), to assist a physician in choosing corrective action to reduce the likelihood of oversensing and inappropriate therapy delivery. Far-field electrogram (EGM) signals are analyzed to detect the occurrence of R-waves, and the result is compared to the number and pattern of R-waves sensed by the IMD and indicated on the marker channel. A marker channel with more sensed R-waves than indicated by analysis of the far-field EGM indicates the presence of oversensing, including double-counting of R-waves, T-wave oversensing, lead malfunction or failure, poor lead connections, noise associated with electromagnetic interference, non-cardiac myopotentials, etc. Identification of the type of oversensing may be determined by analysis of the number and pattern of marker channel sensed R-waves with respect to the timing of the R-waves detected from the far-field EGM.

A minimally invasive, implantable heart sound and ECG monitor and associated method for deriving blood pressure from heart sound data. The device is equipped with an acoustical sensor for detecting first and second heart sounds which are sampled and stored during sensing windows following R-wave and T-wave detections, respectively. ECG and heart sound data are stored in a continuous, looping memory, and segments of data are stored in long-term memory upon an automatic or manual data storage triggering event. Estimated blood pressure is calculated based on custom spectral analysis and processing of the first and second heart sounds. A calibration method includes measuring a patient's blood pressure using a standard clinical method and performing regression analysis on multiple spectral variables to identify a set of best fit weighted equations for predicting blood pressure. Concurrent ECG and estimated blood pressure may be displayed for review by a physician.

T-wave amplitude and QT interval are derived from patient cardiac signals. Then blood glucose levels are determined based on a combination of the T-wave amplitude and the QT interval. By using a combination of both T-wave-based and QT interval-based signals, blood glucose levels can be reliably detected throughout a wide range of blood glucose levels. Once the blood glucose level has been detected, the implanted device compares the blood glucose level against upper and lower acceptable bounds and appropriate warning signals are generated if the level falls outside the bounds. In one example, wherein an implantable insulin pump is additionally provided, the pump is controlled based on the detected blood glucose level to maintain glucose levels within an acceptable range. A calibration technique is also provided for determining patient-specific parameters for use in the detection of blood glucose levels.

A system and method provides monitoring for atrial fibrillation. A data acquisition processor acquires a cardiac signal data stream from a patient and a wave detector detects an R-wave in a cardiac signal of the data stream. A T-wave in the cardiac signal occurring after the detected R-wave and a Q-wave in a subsequent cardiac signal of the data stream is also detected by the wave detector. A filter provides signal gating and extraction of data representing a Region of Interest (ROI) time window from the detected T-wave to the Q-wave. An integration processor detects characteristics of a P wave signal occurring within the ROI time window. At least one of the detected P wave characteristics is compared to characteristics derived from data representing at least one P wave signal and generating an output signal in response to the comparison for use in determining if the patient is in atrial fibrillation.

The invention discloses an automatic electrocardiogram recognition system. The system comprises an electrocardiogram acquisition device, a wireless/wired network transmission module, an electrocardiogram collection and time domain feature recognizer, an electrocardiogram dominant wave interphase recognizer, an electrocardiogram QRS wave group similarity recognizer and an electrocardiogram queuing recognizer, the electrocardiogram acquisition device inputs acquired data to the electrocardiogram collection and time domain feature recognizer via the transmission module, the electrocardiogram collection and time domain feature recognizer recognizes to obtain positions of peak points of P waves, QRS waves and T waves on a 12-lead, the electrocardiogram dominant wave interphase recognizer recognizes heart rate to obtain normal and abnormal results of the heart rate, the electrocardiogram QRS wave group similarity recognizer recognizes whether an electrocardiogram probably has premature beat or not, and the electrocardiogram queuing recognizer sequences and outputs. The system performs real-time computer-aided analysis of clinically acquired 12-lead electrocardiograms to automatically recognize arrhythmia and premature beat electrocardiograms, and accordingly efficiency of electrocardiogram analysis is improved while a priority processing means is provided for emergency electrocardiograms.

Techniques are provided for detecting and evaluating ventricular dyssynchrony based on morphological features of the T-wave and for controlling therapy in response thereto. For example, the number of peaks in the T-wave, the area under the peaks, the number of points of inflection, and the slope of the T-wave can be used to detect ventricular dyssynchrony and evaluate its severity. As ventricular dyssynchrony often arises due to heart failure, the degree of dyssynchrony may also be used as a proxy for tracking the progression of heart failure. Pacing therapy is automatically and adaptively adjusted based on the degree of ventricular dyssynchrony so as to reduce the dyssynchrony and thereby improve cardiac function.

Cardiac electrical events are detected by comparing signal vectors with pre-determined classification zones representative of different cardiac events. The signal vector is generated by sensing the voltages between various combinations of electrodes, such as A-tip to V-tip, A-tip to A-ring, and A-ring to V-ring. The signal vector is compared with a set of classification zones corresponding to different events, such as P-waves, R-waves, T-waves, A-pulses, and V-pulses, to determine whether the vector lies within any of the classification zones. In this manner, cardiac events are detected using only the voltages received from the electrodes and no refractory periods or blanking periods are required to distinguish one event from another. The classification zones vary from patient to patient and a technique is provided herein for generating a set of vector classification zones for a particular patient. Signal vectors corresponding to various unknown cardiac events are generated by the implanted device and are transmitted to an external device programmer. ECG signals, generated by a surface ECG detector, are simultaneously received by the external programmer. The external programmer identifies the cardiac electrical event corresponding to each signal vector based on the ECG signals and then generates classification zones for each event type using only the signal vectors corresponding to the event.

Techniques are provided for tracking patient respiration based upon intracardiac electrogram (IEGM) signals or other electrical cardiac signals. Briefly, respiration patterns are detected based upon cycle-to-cycle changes in morphological features associated with individual electrical events with the IEGM signals. For example, slight changes in the peak amplitudes of QRS-complexes, P-waves or T-waves are tracked to identify cyclical variations representative of patient respiration. Alternatively, the integrals of the morphological features of the individual events may be calculated for use in tracking respiration. In any case, once respiration patterns have been identified, episodes of abnormal respiration, such as apnea, hyperpnea, nocturnal asthma, or the like, may be detected and therapy automatically delivered. In particular, techniques for detecting abnormal respiration based on various respiratory parameters derived from the IEGM—such as respiration depth and respiration power—are described.

Various techniques are described for preventing pacemaker mediated tachycardia (PMT) within biventricular pacing systems and for detecting and terminating PMT should it nevertheless arise. In a first prevention technique, refractory periods applied to the atrial channel are synchronized to begin with a second of a pair of ventricular pacing pulses to more effectively prevent T-wave oversensing on the atrial channel. In a second prevention technique, the sensitivity of the atrial channel is reduced during T-waves also to prevent T-wave oversensing. In a third prevention technique, template matching is performed on the ventricular channels to prevent T-wave oversensing. In a fourth prevention technique, T-wave detection windows are applied to both the ventricular and atrial channels subsequent to any paced or sensed events. In a first detection technique, PMT is detected based upon a degree of variation within V-pulse to P-wave pacing intervals. In a second detection technique, PMT is detected based upon a degree variation within ventricular pacing intervals. In either case, if the degree of variation is too low, indicative of PMT, ventricular refractory periods are expanded to terminate the PMT.

The invention discloses a method and equipment for identification and classification of an electrocardiogram, which identifies a QRS wave configuration pattern to obtain the configuration characteristics of P waves, T waves and QRS waves by calculation of time-domain characteristics, configuration extraction pretreatment and point calculation of QRS wave configuration characteristics. The technical scheme of the invention fully uses the identification process of ECG configuration characteristics from thinking and experience of doctors to realize the effect using small amount of work to achieve a high diagnostic accuracy of cardiac arrhythmia and provides a good base for the application of an ECG diagnostic apparatus.

A Fast Fourier Transform (FFT) converts time-varying event waveforms into the frequency domain waveforms to thereby decompose the events into their spectral components, which are analyzed to distinguish R-waves from T-waves. In some embodiments, the FFT is only activated if a ventricular tachyarrhythmia is already indicated. For example, an initial ventricular rate may be derived from a ventricular IEGM based on all events detected therein. The initial ventricular rate is compared against one or more thresholds representative of ventricular tachycardia (VT) and/or ventricular fibrillation (VF) to determine if VT/VF is indicated. If so, the FFT is activated to distinguish R-waves from T-waves and, in particular, to detect and eliminate T-wave oversensing. Then, the ventricular rate is re-determined based only on the rate of true R-waves. Therapy is delivered if VT/VF is still detected.

A system to detect the presence or absence of T wave alternans is described based on statistical tests and periodicity transform. T wave and ST segment boundaries are detected in multi-lead ECG signals acquired from the regular clinical leads. Once the fiducial point and the above boundaries are delineated, computation of regular parameters like T wave amplitude, area under the T waves or segments of T wave, ST segment slope and/or the curvature of T wave are performed. Each parameter forms a rolling array of values with each successive beat. The array of values, or the time series, is used to make the decision about the T wave alternans. Two different methods are employed based on periodicity transforms and statistical tests. A set of numerical values (e.g. norm of the projection on to p-2 space, sums of adjacent terms after the trend removal, t-value, and number of deviations from alternans pattern) are all computed and compared to threshold values. Threshold values are computed from past information and experience with clinical databases and simulations. Final system comprises a software module, which can be part of the existing ECG monitoring programs as well as external defibrillator modules, apart from being stand-alone algorithms.

The invention discloses an electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving and belongs to a weak bioelectrical signal processing technology field. The current electrocardiogram signal detection technology applied clinically can not give consideration to both a detection precision requirement and a real time requirement. Electrocardiogram signal pretreatment algorithm based on wavelet lifting for improving semi-soft threshold denoising and approximate envelope improving and electrocardiogram feature detection algorithm based on a slope threshold are provided in the invention. Detection criterions are set based on waveform characteristics and time domain distribution characteristics of the electrocardiogram signals. Position detections of R wave, start-stop points of the QRS waves, P wave and T wave are carried out respectively to the electrocardiogram signals. The electrocardiogram signal feature detection algorithm provided in the invention is easy, quick and suitable for parallel processing, and occupies little memory space and is convenient for DSP chip realization. Even in strong noise and P/T wave interference circumstances, R point position can be accurately detected through the algorithm provided in the invention. An R wave false detecting rate of 105 data containing serious noise disturbance is only 0.27% compared with MIT-BIH Database annotation.

The invention provides an electrocardiogram classifying method based on a deep learning algorithm. The method comprises the steps of acquiring original electrocardiogram waveform data with a measuring time being longer than 8s and electrocardiogram additional information, and acquiring electrocardiogram rhythm information and representative PQRST waveform data according to the original electrocardiogram waveform data; inputting the representative PQRST waveform data from an input end of a trained deep learning algorithm to obtain P-wave type data, QRS-wave type data and T-wave type data, and analyzing the representative PQRST waveform data and calculating representative PQRST waveform characteristic data; inputting the representative PQRST waveform characteristic data, the electrocardiogram additional information, and the electrocardiogram rhythm information into a conventional electrocardiogram computer automatic classifying algorithm to obtain an electrocardiogram classifying result. Based on the characteristics of electrocardiogram classification, the method trains a deep learning method via the above-mentioned steps and performs waveform classification by using the deep learning method, so that the accuracy of electrocardiogram classification results can be greatly increased.

Methods and systems are provided for determining pacing parameters for an implantable medical device (IMD). The methods and systems provide electrodes in the right atrium (RA), right ventricle (RV) and left ventricle (LV). The methods and systems sense RV cardiac signals and LV cardiac signals at an RV electrode and an LV electrode, respectively, over multiple cardiac cycles, to collect global activation information. The methods and systems identify a T-wave in the LV cardiac signal. The methods and systems calculate a repolarization index based at least in part on a timing of the T-wave identified in the LV cardiac signal. The methods and systems set at least one pacing parameter based on the repolarization index, wherein the at least one pacing parameter that is set represents at least one of an AV delay, an inter-ventricular interval and an intra-ventricular interval. Optionally, the methods and systems may deliver an RV pacing stimulus at the RV electrode such that the LV cardiac signal sensed thereafter includes the RV pacing stimulus followed by a T-wave. The methods and systems determine a waveform metric such as at least one of a QT interval, T-wave duration, and T-wave amplitude, and utilize the waveform metric to determine as the repolarization index.

A system for heart performance characterization and abnormality detection includes an acquisition device for acquiring an electrophysiological signal representing heart beat cycles of a patient heart. A detector detects one or more parameters of the electrophysiological signal of parameter type comprising at least one of, (a) amplitude, (b) time duration, (c) peak frequency and (d) frequency bandwidth, of multiple different portions of a single heart beat cycle of the heart beat cycles selected in response to first predetermined data. The multiple different portions of the single heart beat cycle being selected from, a P wave portion, a QRS complex portion, an ST segment portion and a T wave portion in response to second predetermined data. A signal analyzer calculates a ratio of detected parameters of a single parameter type of the multiple different portions of the single heart beat cycle. An output processor generates data representing an alert message in response to a calculated ratio exceeding a predetermined threshold.

An implantable cardioverter defibrillator (ICD) senses ventricular depolarizations (R-waves) in an electrogram signal to detect a ventricular tachycardia or fibrillation episodes. The EGM signal is also monitored in real time for characteristics that uniquely identify instances of T-wave oversensing. The ICD determines whether detection of a tachycardia or fibrillation episode is appropriate based upon counts of each of the unique characteristics evidencing T-wave oversensing.

A system and method for cardiac assist of a heart. The system comprises a catheter on which is mounted an inflatable balloon configured for inflation inside a ventricle. The system preferably includes a fluid pump and a processor configured to activate the pump to inflate the intra-ventricular balloon in the ventricle during a predetermined first phase of the cardiac cycle and to deflate the balloon during a predetermined second phase of the cardiac cycle. The predetermined first phase may be the end of the slow ejection phase corresponding to the ascending part of the T wave, and the predetermined second phase may begin at the peak of systolic augmentation resulting from inflation of the balloon which corresponds to the descending part of the T-wave. The method of the invention includes delivering the balloon to a ventricle of the heart and transiently inflating the balloon to achieve a predetermined intra-ventricular systolic pressure

The invention discloses an electrocardiosignal (ECG) data analysis method suitable for a mobile platform. The electrocardiosignal (ECG) data analysis method includes subjecting collected electrocardiosignal data to original ECG electrocardiosignal median filtering; detecting and positioning an R wave through a self-adaption dual-threshold algorithm; detecting two forward waves before and after the R wave, wherein the two forward waves are a P wave and a T wave; detecting two negative waves on two sides of the R wave, wherein the two negative waves are a Q wave and an S wave; using starting point and terminal point positions of the R wave, the P wave, the T wave, the Q wave, and the S wave to calculate ECG feature values of wave point intervals for arrhythmia diagnosis. According to the ECG data analysis method suitable for the mobile platform, the accuracy for detection of arrhythmia can reach over 92%; since an algorithm structure is concise, processing efficiency is high, only 12s is required for processing one-minute ECG data on a Cortex A8/Android mobile platform, and the method is suitable for developing a mobile platform based portable admeasuring apparatus.