487 results about "P-wave" patented technology

Methods of locating a tip of a central venous catheter (“CVC”) relative to the superior vena cava, sino-atrial node, right atrium, and/or right ventricle using electrocardiogram data. The CVC includes at least one electrode. In particular embodiments, the CVC includes two or three pairs of electrodes. Further, depending upon the embodiment implemented, one or more electrodes may be attached to the patient's skin. The voltage across the electrodes is used to generate a P wave. A reference deflection value is determined for the P wave detected when the tip is within the proximal superior vena cava. Then, the tip is advanced and a new deflection value determined. A ratio of the new and reference deflection values is used to determine a tip location. The ratio may be used to instruct a user to advance or withdraw the tip.

Devices and methods for obtaining and using endovascular electrograms in a number of clinical applications and settings are disclosed. In one embodiment, methods are disclosed for triggering analysis of an endovascular ECG waveform based on the detection of a peak in a skin-based ECG waveform in order to determine a location of an indwelling medical device, such as a catheter. In another embodiment, the position of a catheter or other medical device within the vasculature can be determined by analysis of the energy profile of a detected P-wave. In yet other embodiments, magnetic connecting devices for establishing an operable connection through a sterile field are disclosed.

A computer-based method for positioning an endovascular device in or near the heart using electrocardiogram (ECG) signals is provided. The computer-based method includes receiving an endovascular ECG signal, associated with an endovascular device, including a plurality of waveforms, each waveform having at least a P-wave component, processing the endovascular ECG signal, over a plurality of predetermined time periods, to calculate a P-wave amplitude and a spectral power for each predetermined time period, determining a maximum P-wave amplitude from the plurality of P-wave amplitudes, and an associated maximum spectral power from the plurality of spectral powers, associating the maximum P-wave amplitude and the maximum spectral power with a predetermined location in or near the heart, calculating a location of the endovascular device, for each predetermined time period, based on a ratio of the P-wave amplitude to the maximum P-wave amplitude and a ratio of the spectral power to the maximum spectral power, and displaying the location of the endovascular device to a user.

The instant invention provides a method for processing converted-wave data into interpretable images using a compact two-parameter model. The method broadly comprises the steps of: collecting both P-wave and converted-wave seismic data; identifying the arrival times of the P-wave and the converted-wave data; computing the vertical velocity ratio from the arrival time data; computing the moveout velocity ratio from the corresponding moveout velocities; computing the effective velocity ratio from the vertical velocity ratio and the moveout velocity ratio; and computing the conversion point from the short-spread P-wave moveout velocity for each reflector, from the effective velocity ratio, the C-wave moveout velocity ratio, and from the arrival time data.

The instant invention is a method for numerically propagating waves or solving wave equations on a digital computer. This invention can be used to compute highly accurate solutions to the wave equation, in fact in some cases it computes the analytical solution, something previously considered impossible. The instant method can also propagate waves that are not described by differential equations, such as anisotropic scalar waves. The invention has the advantages that it is computationally efficient, accurate, and flexible. Of importance is the ability to propagate waves that simulate the P-wave arrivals in both isotropic and anisotropic media with a scalar as opposed to a vector equation.

The invention includes a method of locating a tip of a central venous catheter (“CVC”) having a distal and proximal pair of electrodes disposed within the superior vena cava, right atrium, and/or right ventricle. The method includes obtaining a distal and proximal electrical signal from the distal and proximal pair and using those signals to generate a distal and proximal P wave, respectively. A deflection value is determined for each of the P waves. A ratio of the deflection values is then used to determine a location of the tip of the CVC. Optionally, the CVC may include a reference pair of electrodes disposed within the superior vena cava from which a reference deflection value may be obtained. A ratio of one of the other deflection values to the reference deflection value may be used to determine the location of the tip of the CVC.

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.

An atrioventricular delay (AVD) for cardiac pacing therapy is determined based, at least in part, on one or more cardiac activation signals sensed using body-implantable electrodes providing non-local sensing. A conduction delay is estimated using a non-local cardiac activation signal and the AVD is determined based on the conduction delay. Estimating the conduction delay may involve measuring a P-wave width or measuring a QRS complex width of the non-local signal. If multiple signals are sensed, the conduction delay may be estimated from a selected signal or may be estimated by forming a representative signal such as through averaging or other methods. The AVD may be determined as a function of the estimated conduction delay.

A system and method are provided for developing a database of body surface ECG P-wave maps for classification and localization of left-sided atrial arrhythmias. The system and method include generating and receiving P-wave data in a subject by left atrial pacing or receiving P-wave data in a subject during spontaneously occurring or induced left atrial arrhythmias; computing (e.g. potential or integral) maps of the P-wave data; classifying the maps specific to a left atrial ectopic origin; verifying the classification procedure; averaging the classified maps into mean maps; and storing and accessing the mean maps in the database. The mean maps of the P-wave data in the database can be used to automatically classify and localize P-wave data from a patient obtained during a left atrial arrhythmia such as atrial tachycardia, focal atrial fibrillation, or orthodromic atrioventricular reentrant tachycardia.

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.

A system for heart performance characterization and abnormality detection processes a heart electrical activity signal in determining multiple first signal characteristic values over multiple heart cycles. A first signal characteristic value substantially comprises a time interval between a peak of a P wave to a peak of a succeeding R wave representing a repolarization time interval in an individual heart cycle and the signal processor uses a peak detector and time detector for identifying the peaks and detecting a time difference between the identified peaks. A comparator compares at least one of the multiple first signal characteristic values or a value derived from the multiple first signal characteristic values with a threshold value to provide a comparison indicator. A patient monitor in response to the comparison indicator indicating a calculated signal characteristic value exceeds the threshold value, generates an alert message associated with the threshold.

Embodiments of close loop optimization of atrio-ventricular (A-V) delay interval and/or inter-ventricular (V-V) timing are disclosed. An implantable medical device includes a housing that supports a processing means adapted for implantation in a patient. There can be two or more electrodes electrically coupled to the processing means where the two or more electrodes can be used for sensing a patient's cardiac signals, which include a far-field EGM. The processing means can determine a width of a P-wave from the sensed far-field EGM. Also included can be a means for delivering an adapted cardiac pacing therapy based upon the width of the P-wave, including revised A-V delay and/or V-V temporal intervals.

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.

Techniques for providing capture verification during overdrive pacing are described. If an overdrive pacing pulse fails to evoke capture (i.e. a loss of capture occurs), a high voltage backup pulse is automatically delivered. Once a second loss of capture occurs during a single sequence of overdrive pacing pulses, an overdrive pulse capture threshold detection search, described herein, is performed while overdrive pacing continues. Various techniques for providing rate recovery are also described herein. The rate recovery techniques are designed to avoid problems that might arise from possible fusion of intrinsic beats and overdrive pacing pulses that fail to evoke capture. In a first rate recovery technique, capture detection is suspended during rate recovery due to the possibility of fusion. Instead, an extra safety margin is added to the overdrive pulses. Once two intrinsic beats are detected, automatic capture verification is reactivated for the next two beats to verify capture before the new overdrive rate is finally established. In a second rate recovery technique, capture verification is maintained throughout rate recovery but the pulse magnitude is increased to a high output mode voltage to avoid any risks of fusion. After the output is increased to the high output mode voltage, a subsequent loss of capture is considered to be an intrinsic event for the purposes of terminating rate recovery. In a third rate recovery technique, capture verification is maintained during rate recovery and the output energy is not increased to the high output mode voltage unless loss of capture is first detected. Subsequent loss of capture events are then counted as P-waves.

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.

The invention discloses a mine micro-quake signal P wave first arrival moment joint pickup method and mainly solves the technical problems that a single PAI-K and AIC method is high in pickup precision but is unstable in pickup of low signal to noise ratio, spike, tail shock and pure noise signals. The method comprises the following steps: extracting mine micro-quake signal waveform data; carrying out maximum kurtosis value arrival time (K1) pickup; carrying out minimum self-correlation value arrival time (K2) pickup; and judging whether | k1-k2 | is larger than a threshold value L, if so, abandoning the micro-quake signals, or otherwise, outputting P wave arrival time k=k2. Through PAI-K_AIC joint identification, interference of low signal to noise ratio, spike, tail shock and pure noise signals is effectively eliminated, mistake pickup rate is greatly reduced, and P wave pickup stability is enhanced. The method has the advantages of simple processing, high applicability and high accuracy and the like.

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.

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.

A phased array of transducers is used to generate elastic waves into the earth formation in a selected direction. The impedance measured at the input to the transducer array is indicative of the elastic impedance of the earth formation in the selected direction.

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.

Coherent wave noise energy is removed from seismic data by modeling both the P-wave primary energy and the coherent wave noise energy. The P-wave primary energy is modeled first and then subtracted from the input data. The data with the P-wave primary energy removed is used as the input for coherent wave energy removal. The coherent wave energy is modeled and subtracted from the original input data, i.e. the data input into P-wave primary removal. This leaves a dataset with P-wave primary energy and noise energy not related to coherent waves. This method can be utilized to remove all types of coherent noise with a velocity difference to the desired P-wave primary energy or with a different type of moveout (change of time of arrival with source-receiver distance) such as, for example, linear moveout.