65 results about "Flattened T wave" patented technology

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.

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 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.

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.

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.

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.

The measurement of beat-to-beat QT interval variability (QTV) shows clinical promise for identifying several types of cardiac pathology. However, until now, there has been no device capable of displaying, in real time on a beat-to-beat basis, changes in QTV in all 12 conventional leads in a continuously monitored patient. While several software programs have been designed to analyze QTV, heretofore, such programs have all involved only a few channels (at most) and/or have required laborious user interaction or off-line calculations and post-processing, limiting their clinical utility. This invention discloses a PC-based ECG software program that in real time, acquires, analyzes and displays QTV and PQ interval variability (PQV) in each of the independent channels that constitute the 12-lead conventional and/or Frank X, Y, Z lead ECG. The system also analyzes and displays the QTV and PQV from QT and PQ interval signals that are derived from multiple channels and from singular value decomposition such that the effect of noise and other artifacts on the QTV and PQV results are substantially reduced compared to existing single-channel methods. Moreover, this invention also discloses certain new parameters of T-wave (and QRS and P-wave) morphology, that in initial studies have improved clinical diagnostic utility and/or reproducibility and reliability compared to known existing parameters of T-wave morphology. Finally, it also discloses a method for determining the beat-to-beat variability these T, QRS and P-wave morphologic parameters.

A technique is provided for detecting episodes of cardiac ischemia based on an examination of post-T-wave signal segments. Since cardiac ischemia is often a precursor to acute myocardial infarction (AMI) or ventricular fibrillation (VF), the technique thereby provides a method for predicting the possible onset of AMI or VF so that a warning may be delivered to the patient. The warning preferably includes both a perceptible electrical notification signal applied directly to subcutaneous tissue and a warning signal delivered via short range telemetry to a handheld warning device external to the patient. In one example, the onset of cardiac ischemia is identified by detecting a sharp falling edge within post-T-wave signals by filtering the signals using a high-pass filter having a cutoff frequency of at least 1 Hz. The total amount of energy in the filtered signal is calculated and compared against various thresholds.

A technique is provided for detecting episodes of cardiac ischemia based on an examination of the total energy of T-waves. Since cardiac ischemia is often a precursor to acute myocardial infarction (AMI) or ventricular fibrillation (VF), the technique thereby provides a method for predicting the possible onset of AMI or VF. Briefly, the technique integrates internal electrical cardiac signals occurring during T-waves and then compares the result against a running average. If the result exceeds the average by some predetermined amount, ischemia is thereby detected and a warning signal is provided to the patient. The maximum slope of the T-wave is also exploited. Techniques are also set forth herein for reliably detecting T-waves, which help prevent P-waves from being misinterpreted as T-waves on unipolar sensing channels. The T-wave detection technique may be used in conjunction with ischemia detection or for other purposes.

A cardiac magnetic field diagnostic apparatus for evaluating intracardiac three-dimensional localization of a myocardial injury by means of cardiac magnetic field measurement and a three-dimensional localization evaluating method of myocardial injury are disclosed. A magnetic field distribution measuring instrument (1) creates magnetic field distribution data by contactless magnetic field measurement on coordinates on the breast of a subject. An arithmetic operation unit (2) computers intracardiac three-dimensional current density distribution data from the magnetic field distribution data, draws a magnetic field integral cubic diagram as a cardiac contour cubic diagram according to the three-dimensional current density distribution data, creates data to draw the three-dimensional distribution of the QRS difference, the T-wave vector, or the RT dispersion of the same subject according to the three-dimensional current density distribution data, and reconstructs it on the cardiac contour. With this, evaluation of three-dimensional localization of a myocardial injury is possible.

The invention discloses a method for performing superposition analysis on electrocardio data of any time segment. The method comprises the following steps of: selecting any segment of representative waveforms from an electrocardiogram; reading electrocardio data and waveform information in the time period; superposing the waveform of each heart beat in the time period and analyzing the waveform type of the superposed waveforms, the forward voltage and the negative voltage of a P wave, the voltage of a Q wave, an R wave and an S wave, the average voltage of an ST segment, the forward voltage and the negative voltage of a T wave, the time limit of the P wave, the Q wave, the R wave, the S wave and the T wave, a QT interval and other data; and providing a human-computer interaction interface, displaying an analysis result obtained after waveforms are superposed, and allowing a user to perform manual intervention on the analysis result. The application scope of an exercise load test is broadened, the accuracy of the test result is improved, and the diagnostic report has higher reference value.

The invention discloses a method and system for detecting the start-end points of P-waves and T-waves in multi-lead ECG signals. The method includes the steps that firstly, according to the waveform and quality of each lead signal, three leads most suitable for P-wave and T-wave location and start-end detection are adaptively selected to be superimposed into a virtual lead; then, through the end point of the former one in two adjacent QRS waves and the start point of the latter one, the virtual lead is divided into a series of regions of search, it is determined that the first half of each region of search is a T-wave search range and the second half of each region of search is a P-wave search range, and the maximum peak values in the ranges are selected as the crests of corresponding characteristic waves; finally, the start-end points of P-waves and T-waves are detected respectively through an accumulative descent method based on dynamic thresholds. According to the method and system,by adopting the multi-lead adaptive selection technology, the robustness of the system is enhanced; by adopting the superposition-to-virtual-lead technology, the accuracy of detecting the start-end points of P-waves and T-waves is improved, and the number of algorithm detection times is reduced.

Embodiments of the present invention relate to implantable systems, and methods for use therein, that can detect T-wave alternans and analyze the detected alternans to provide information regarding cardiac instabilities and predict impending arrhythmias.

A method of differentiating between ischemic and cardiac memory inverted T-waves includes performing an electrocardiogram of a patient; identifying inverted T-waves in at least some of precordial leads; identifying T-waves in leads I and aVL; diagnosing ischemia if leads I and aVL show inverted T-waves; and diagnosing cardiac memory if the leads I and aVL show non-inverted T-waves. The method may also include identifying T-waves in lead III; confirming ischemic diagnosis if the lead III shows deeper inverted T-waves than in the precordial leads; and confirming cardiac memory diagnosis otherwise.

The invention discloses an R wave detection algorithm based on extremum field mean mode decomposition and improved Hilbert enveloping and belongs to the technical field of weak biological signal processing. An electrocardio signal pre-processing algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping and an R wave detection algorithm based on slope threshold are provided. Detection criteria are set according to wave form characteristics and time domain distribution characters of electrocardio signals, and positions of R waves with most obvious characters and highest information amount in the electrocardio signals are detected. An extremum field mean mode decomposition algorithm improves empirical mode decomposition speed and can effectively restrain mode superimposition and boundary effect. The improved Hilbert enveloping can effectively restrain interference of noise and other characteristic waves an can also enhance energy of the R waves. The R wave detection algorithm based on the extremum field mean mode decomposition and the improved Hilbert enveloping can also detect positions of R points accurately even if interference of strong noise and large P/T waves exists. A Massachusetts institute of technology-Beth Israel hospital (MIT-BIH) data base is used for detecting the R wave detection algorithm. Sensitivity of the R wave detection algorithm is 99.94%, and positive predictive rate is 99.87%.