Cardiac signal estimation based on the arterial and venous pressure signals of a hemodialysis machine.

Continuous cardiac monitoring is usually not performed during hemodialysis treatment, although a majority of patients with kidney failure suffer from cardiovascular disease. In the present paper, a method is proposed for estimating a cardiac pressure signal by combining the arterial and the venous pressure sensor signals of the hemodialysis machine. The estimation is complicated by the periodic pressure disturbance caused by the peristaltic blood pump, with an amplitude much larger than that of the cardiac pressure signal. Using different techniques for combining the arterial and venous pressure signals, the performance is evaluated and compared to that of an earlier method which made use of the venous pressure only. The heart rate and the heartbeat occurrence times, determined from the estimated cardiac pressure signal, are compared to the corresponding quantities determined from a photoplethysmographic reference signal. Signals from 9 complete hemodialysis treatments were analyzed. For a heartbeat amplitude of 0.5 mmHg, the median absolute deviation between estimated and reference heart rate was 1.3 bpm when using the venous pressure signal only, but dropped to 0.6 bpm when combining the pressure signals. The results show that the proposed method offers superior estimation at low heartbeat amplitudes. Consequently, more patients can be successfully monitored during treatment without the need of extra sensors. The results are preliminary, and need to be verified on a separate dataset.

Identification of patients prone to hypotension during hemodialysis based on the analysis of cardiovascular signals.

Intradialytic hypotension (IDH) is a major complication during hemodialysis treatment, and therefore it is highly desirable to identify, at an early stage during treatment, whether the patient is prone to IDH. Heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) were analyzed during the first 30 min of treatment to assess information on the autonomic nervous system. Using the sequential floating forward selection method and linear classification, the set of features with the best discriminative power was selected, resulting in an accuracy of 92.1%. Using a classifier based on the HRV features only, thereby avoiding that continuous blood pressure has to be recorded, accuracy decreased to 90.2%. The results suggest that an HRV-based classifier is useful for determining whether a patient is prone to IDH at the beginning of the treatment.

A modified Lewis ECG lead system for ambulatory monitoring of atrial arrhythmias.

BACKGROUND: When using a reduced ECG lead system with three leads or less, the analysis of atrial activity during arrhythmias can become problematic due to low signal amplitude and noise. Long ago, Sir Thomas Lewis proposed a lead system for atrial activity enhancement in which two electrodes are placed directly on the chest, however, these leads are sensitive to arm movement artifacts. In this study, a modified Lewis lead system is proposed that is better suited for ambulatory applications since the electrodes are placed in areas with less muscle. MATERIALS AND METHODS: The proposed modification is compared to the original Lewis leads as well as to the ES lead of the EASI system. Forty-one healthy volunteers and 10 patients with atrial fibrillation participated. RESULTS: The modified Lewis lead has nearly 3 times as large atrial amplitude as the original Lewis leads, and is associated with the largest amplitude of atrial activity when compared to the amplitude of electromyographic activity in healthy volunteers. CONCLUSION: The results suggest that the proposed modification of the Lewis lead system has potential to improve ambulatory monitoring of atrial arrhythmias.

Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal.

This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

Computer-based detection and analysis of heart sound and murmur.

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05-0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20-70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.

Signal-averaged electrocardiography in normal newborn infants.

Signal-averaged electrocardiograms (SAECGs) were recorded with bipolar orthogonal and standard 12-lead settings in 29 term healthy newborn infants. SAECGs performed with the two lead configurations were similar in quality and quantity. For the duration of the QRS complex (QRSD), the upper limit of normality for newborn infants can be set to 100 ms. It is similar in individual bipolar orthogonal and individual standard 12-lead recordings as well as in filtered QRSD in the vectorcardiograms (the vector magnitude, VM, and the sum of extremity leads). In individual leads of bipolar orthogonal and standard 12-lead recordings, root-mean-square voltage of the terminal 40 ms of the QRS complex (RMS40) is widely scattered and unsuitable as discriminant variable. For VM and the sum of the extremity leads, the same reference values for RMS40 and duration of low-amplitude signal can be used as applied in adults. Individual lead recordings provide complementary information in addition to vectorcardiograms. There are practical advantages using standard 12-lead compared to conventional bipolar orthogonal configurations. The widespread use of standard 12-lead ECG in routine medical practice makes its utility advantageous also in performing signal-averaged electrocardiography.

Time course of ECG depolarization and repolarization changes during ischemia in PTCA recordings.

OBJECTIVES: In this work we studied the temporal evolution of changes in the electrocardiogram (ECG) as a consequence of the induced ischemia during prolonged coronary angioplasty, comparing the time course of indexes reflecting depolarization and those reflecting repolarization. METHODS: We considered both local (measured at specific points of the ECG) and global (obtained from the Karhunen-Loève transform) indexes. In particular, the evolution of Q, R and S wave amplitudes during ischemia was analyzed with respect to classical indexes such as ST level. As a measurement of sensitivity we used an Ischemic Changes Sensor (ICS), which reflects the capacity of an index to detect changes in the ECG. RESULTS: The results showed that, in leads with low-amplitude ST-T complexes, the S wave amplitude was more sensitive in detecting ischemia than was the commonly used index ST60. It was found that in such leads the S wave amplitude initially exhibited a delayed response to ischemia when compared to ST60, but its performance was better from the second minute of occlusion. The global indexes describing the ST-T complex were, in terms of the ICS, superior to the S wave amplitude for ischemia detection. CONCLUSIONS: Ischemic ECG changes occur both at repolarization and depolarization, with alterations in the depolarization period appearing later in time. Local indexes are less sensitive to ischemia than global ones.

Detection of autonomic modulation in permanent atrial fibrillation.

A new signal processing method for the detection of cyclic variations in atrial fibrillation frequency is presented. The objective was to investigate whether or not respiration, through the autonomic nervous system, modulates the fibrillation frequency in patients with permanent atrial fibrillation. A group of eight patients with permanent atrial fibrillation, atrioventricular block III and a permanent pacemaker were studied during rest, rhythm-controlled respiration, with each breath lasting for 8 s (i.e. a breathing frequency of 0.125 Hz), and rhythm-controlled respiration after full vagal blockade by atropine. Using the new method, a spectral peak could be detected, in two of the patients, at the breathing frequency during rhythm-controlled respiration then disappeared after injection of atropine.

Estimation of the respiratory frequency using spatial information in the VCG.

A new method for extracting respiratory signals from the ECG/VCG is presented. The method is based on the alignment of an observed VCG loop to a reference loop with respect to the transformations of rotation and time synchronisation. The resulting series of estimated rotation angles reflects respiratory-induced changes in the electrical axis of the heart. The respiratory frequency is estimated by power spectral analysis of the derived respiration signal. The value of respiratory modulation of the heart rate is considered by analysing the cross power spectrum of the signals related to rotation angles and heart rate. For comparison, a respiratory signal derived from the QRS area of two different leads is implemented. The performance of the methods is validated on a database with simultaneously recorded VCG and respiratory signals acquired from 20 healthy subjects. The agreement between the respiratory frequencies obtained from the derived and the respiratory signals is presented. The angle-based respiratory signal is found to produce the best agreement with a gross median error of only 4.2%.

Detection of body position changes using the surface electrocardiogram.

A method for detecting body position changes that uses the surface vectorcardiogram (VCG) is presented. Such changes are often manifested as sudden shifts in the electrical axis of the heart and can erroneously be interpreted as acute ischaemic events. Axis shifts were detected by analysing the rotation angles obtained from the alignment of successive VCG loops to a reference loop. Following the rejection of angles originating from noise events, the detection of body position changes was performed on the angle series using a Bayesian approach. On a database of ECG recordings from normal subjects performing a predefined sequence of body position changes, a detection rate of 92% and a false alarm rate of 7% was achieved.

High-frequency components in ECG analysed in guinea-pig Langendorf preparations.

High-frequency components in ECG during global ischaemia were studied in isolated guinea-pig hearts perfused ad modum Langendorf. Electrocardiograph recordings were carried out from the epicardial surface both in normo- and low-flow perfusion. After bandpass filtering (5-500 Hz), signal-averaging, was undertaken. The high-frequency components either increased or decreased after low-flow perfusion was instituted. Root-mean-square voltage (RMS) of the depolarization signal correlated poorly with the signal amplitude, but highly with the first and second derivative, i.e. the velocity and the acceleration of the signal. It is concluded that high-frequency components are not pathological phenomena per se, but reflect the shape of the original electrocardiographic signal.

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation.

A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.

Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties.

Time-frequency analysis is considered for characterizing atrial fibrillation in the surface electrocardiogram (ECG). Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions which are appropriate to short- and long-term variations. The cross Wigner-Ville distribution is found to be particularly useful for short-term analysis due to its ability to handle poor signal-to-noise ratios. In patients with chronic atrial fibrillation, substantial short-term variations exist in fibrillation frequency and variations up to 2.5 Hz can be observed within a few seconds. Although time-frequency analysis is performed independently in each lead, short-term variations in fibrillation frequency often exhibit a similar pattern in the leads V1, V2 and V3. Using different techniques for short- and long-term analysis, it is possible to reliably detect subtle long-term changes in fibrillation frequency, e.g., related to an intervention, which otherwise would have been obscured by spontaneous variations in fibrillation frequency.

Otoacoustic emissions and improved pass/fail separation using wavelet analysis and time windowing.

A new method is presented for the purpose of improving pass/fail separation during transient evoked otoacoustic emission (TEOAE) hearing screening. The method combines signal decomposition in scales using the discrete wavelet transform, non-linear denoising and scale-dependent time windowing. The cross-correlation coefficient between two subaveraged, processed TEOAE signals is used as a pass/fail criterion and assessed in relation to the pure-tone, mean hearing level. The performance is presented in terms of receiver operating characteristics for a database of 5,214 individuals. The results show that the specificity improves from 68% to 83% at a sensitivity of 90% when compared with the conventional wave reproducibility parameter.

Non-invasive assessment of atrial fibrillation (AF) cycle length in man: potential application for studying AF.

Non-invasive assessment of the fibrillatory frequency of atrial fibrillation (AF) can be performed by frequency domain analysis. The peak frequency in the derived spectrum can be converted to a dominant atrial cycle length (DACL). The DACL can be altered through autonomic modulation or pharmacologic manipulation, but the change in DACL is less marked in those with a short DACL value. In patients with AF, those with a short duration of the arrhythmia have longer DACL values. Finally, patients with paroxysmal AF generally exhibit longer DACL values than patients with permanent AF. Thus non-invasive assessment of the atrial fibrillatory cycle length provides a useful index of atrial refractoriness and has the potential of clinical utility in patient assessment and treatment planning.

Sampling rate and the estimation of ensemble variability for repetitive signals.

The measurement of ensemble variability in time-aligned event signals is studied in relation to sampling rate requirements. The theoretical analysis is based on statistical modelling of time misalignment in which the time resolution is limited by the length of the sampling interval. For different signal-to-noise ratios (SNRs), the sampling rate is derived which limits the misalignment effect to less than 10% of the noise effect. Each signal is assumed to be corrupted by additive noise. Using a normal QRS complex with a high SNR (approximately equal to 30 dB), a sampling rate of approximately 3 kHz is needed for accurate ensemble variability measurements. This result is surprising since it implies that the Nyquist rate is far too low for accurate variability measurements. The theoretical results are supplemented with results obtained from an ECG database of 94 subjects for which the ensemble variability is computed at different sampling rates using signal interpolation. The ensemble variability is substantially reduced (40%) when increasing the rate from 1 to 3 kHz, thus corroborating the results suggested by the theoretical analysis.

Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion.

OBJECTIVES: This study describes changes in high-frequency QRS components (HF-QRS) during percutaneous transluminal coronary angioplasty (PTCA) and compares the ability of these changes in HF-QRS and ST-segment deviation in the standard 12-lead electrocardiogram (ECG) to detect acute coronary artery occlusion. BACKGROUND: Previous studies have shown decreased HF-QRS in the frequency range of 150-250 Hz during acute myocardial ischemia. It would be important to know whether the high-frequency analysis could add information to that available from the ST segments in the standard ECG. METHODS: The study population consisted of 52 patients undergoing prolonged balloon occlusion during PTCA. Signal-averaged electrocardiograms (SAECG) were recorded prior to and during the balloon inflation. The HF-QRS were determined within a bandwidth of 150-250 Hz in the preinflation and inflation SAECGs. The ST-segment deviation during inflation was determined in the standard frequency range. RESULTS: The sensitivity for detecting acute coronary artery occlusion was 88% using the high-frequency method. In 71% of the patients there was ST elevation during inflation. If both ST elevation and depression were considered, the sensitivity was 79%. The sensitivity was significantly higher using the high-frequency method, p<0.002, compared with the assessment of ST elevation. CONCLUSIONS: Acute coronary artery occlusion is detected with higher sensitivity using high-frequency QRS analysis compared with conventional assessment of ST segments. This result suggests that analysis of HF-QRS could provide an adjunctive tool with high sensitivity for detecting acute myocardial ischemia.

Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring.

A new detector is presented which finds changes in the repolarization phase (ST-T complex) of the cardiac cycle. It operates by applying a detection algorithm to the filtered root mean square (rms) series of differences between the beat segment (ST segment or ST-T complex) and an average pattern segment. The detector has been validated using the European ST-T database, which contains ST-T complex episodes manually annotated by cardiologists, resulting in sensitivity/positive predictivity of 85/86%, and 85/76%, for ST segment deviations and ST-T complex changes, respectively. The proposed detector has a performance similar to those which have a more complicated structure. The detector has the advantage of finding both ST segment deviations and entire ST-T complex changes thereby providing a wider characterization of the potential ischemic events. A post-processing stage, based on a cross-correlation analysis for the episodes in the rms series, is presented. With this stage subclinical events with repetitive pattern were found in around 20% of the recordings and improved the performance to 90/85%, and 89/76%, for ST segment and ST-T complex changes, respectively.

Clustering ECG complexes using hermite functions and self-organizing maps.

An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.

Vectorcardiographic loop alignment and the measurement of morphologic beat-to-beat variability in noisy signals.

The measurement of subtle morphologic beat-to-beat variability in the electrocardiogram (ECG)/vectorcardiogram (VCG) is complicated by the presence of noise which is caused by, e.g., respiration and muscular activity. A method was recently presented which reduces the influence of such noise by performing spatial and temporal alignment of VCG loops. The alignment is performed in terms of scaling, rotation and time synchronization of the loops. Using an ECG simulation model based on propagation of action potentials in cardiac tissue, the ability of the method to separate morphologic variability of physiological origin from respiratory activity was studied. Morphologic variability was created by introducing a random variation in action potential propagation between different compartments. The results indicate that the separation of these two activities can be done accurately at low to moderate noise levels (less than 10 microV). At high noise levels, the estimation of the rotation angles was found to break down in an abrupt manner. It was also shown that the breakdown noise level is strongly dependent on loop morphology; a planar loop corresponds to a lower breakdown noise level than does a nonplanar loop.