Ventricular Depolarization

Classical and Modern Features for Interpretation of ECG Signal

João Paulo do Vale Madeiro , ... Angelo Roncalli Alencar Brayner , in Developments and Applications for ECG Signal Processing, 2019

Atrioventricular Node

First-Degree AV Block

The first-degree AV block is characterized by the difficulty of the atria impulses in reaching the ventricles, reflecting in the ECG a sustained delay prolongation betwixt atrial and ventricular depolarization, a P–R interval college than 0.ii s in all beats ( Fig. ane.thirteen). It can exist caused past structural defects of the conduction pathway as seen, for example, in chronic conduction degenerative diseases and myocardial infarction (Lilly, 2012).

Figure 1.13

Figure 1.13. An excerpt of ECG bespeak showing an episode of Start-degree AV block.

Second-Degree AV Block

In the 2nd-degree AV block, the atria impulses fail to achieve the ventricles in all beats. It is due a conduction failure from the AV node, where atria impulses propagates intermittently. The ECG of this intrinsic beliefs is characterized by a QRS complex not ever preceded by a P wave (Lilly, 2012). The second-degree AV block can have two different forms:

•

Mobits Type I block is characterized by a gradual and progressive conduction defect between atria and ventricles until an impulse is completely blocked and ventricular stimulation lacks every bit a outcome of a single trounce (Pastore et al., 2016; Lilly, 2012). The ECG shows a progressive P–R interval increment and R–R interval reduction betwixt beats, until the next QRS circuitous is absent, starting the cycle anew. During the intermission, the R–R interval is twice shorter than the previous, see Fig. 1.xiv (this figure was published in Textbook of Medical Physiology, Arthur C. Guyton and John Eastward. Hall, Chapter 13: Cardiac Arrhythmias and Their Electrocardiographic Interpretation, Folio 149, Copyright Elsevier Inc. (2006)) (Pastore et al., 2009);

Figure 1.14

Figure 1.14. Case of a second degree AV block, showing a dropped beat (failure of the ventricles to receive the excitatory signals) (pb V3).
•

Mobits Type Ii block is characterized past a sudden conduction interruption, where the AV node ceases to acquit ii or more than beats, unexpectedly, without whatsoever ECG warning. As consequence, the ECG shows sequential P waves without a correspondent QRS circuitous (Fig. 1.15). The His-Purkinje areas might play a role in this ceased behavior, resulting in abnormally wide QRS complexes and extensive infarction or chronic degenerative of the conduction pathway (Lilly, 2012).

Figure 1.15

Figure 1.15. An excerpt of ECG signal showing an episode of Mobits Blazon-II block: register 231 from MIT–BIH Arrhythmia Database.

Third-Degree AV Block

The tertiary-degree AV block is characterized by a consummate collapse of the impulses conduction betwixt the atria and ventricles, dividing the heart into two unconnected zones, without P waves and QRS complexes sequential relationships (see Fig. i.16; this figure was published in Textbook of Medical Physiology, Arthur C. Guyton and John E. Hall, Chapter 13: Cardiac Arrhythmias and Their Electrocardiographic Interpretation, Folio 149, Copyright Elsevier Inc. (2006)). In this scenario, the atria depolarize by the SA node at a college rate (Pastore et al., 2009), and the ventricles by their own intrinsic escape rate, usually between 40 bpm to 55 bpm (Lilly, 2012).

Figure 1.16

Figure 1.16. Case of a complete AV block (lead Ii).

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The Electrocardiogram—A Brief Background

Leif Sörnmo , Pablo Laguna , in Bioelectrical Indicate Processing in Cardiac and Neurological Applications, 2005

half-dozen.ii.iii ECG Waves and Time Intervals

We will now describe some of import ECG wave characteristics, central to the development of signal processing algorithms, forth with the wave-naming convention. Atrial depolarization is reflected by the P wave, and ventricular depolarization is reflected by the QRS complex, whereas the T wave reflects ventricular repolarization, run across Figure vi.10. Atrial repolarization cannot unremarkably be discerned from the ECG since it coincides with the much larger QRS circuitous. The aamplitude of a moving ridge is measured with reference to the ECG baseline level, commonly defined by the isoelectric line which immediately precedes the QRS complex. The elapsing of a moving ridge is defined past the two time instants at which the wave either deviates significantly from the baseline or crosses it.

Figure 6.10. Moving ridge definitions of the cardiac cycle and important moving ridge durations and intervals. The J bespeak defines the point in time when the QRS complex curves into the ST segment.

The P moving ridge reflects the sequential depolarization of the right and left atria. In most leads, the P wave has positive polarity and a smooth, monophasic morphology. Its aamplitude is unremarkably less than 300 µv, and its duration is less than 120 ms. An absent P wave may, for example, suggest that the rhythm has its origin in the ventricles, i.eastward., a ventricular ectopic focus has taken precedence over the SA node causing atrial depolarization to coincide with ventricular depolarization.

The spectral characteristic of a normal P wave is usually considered to be depression-frequency, below x–15 Hz (Effigy 6.11). All the same, the application of ensemble averaging techniques to produce a dissonance-reduced ECG has helped demonstrate that much higher frequency components of the P wave exist; such components have been found useful for predicting the occurrence of sure arrhythmias of atrial origin.

Effigy 6.11. Power spectrum of the P moving ridge, QRS complex, and T moving ridge. The diagram serves primarily as a rough guide to where the spectral components are located; large variations exist between beats of different pb, origin, and subjects.

It is sometimes problematic to determine the time instants that define the onset and finish of a P wave because of a depression amplitude and shine morphology. As a result, the analysis of private P waves is excluded from sure ECG applications where the presence of noise is considerable.

The QRS complex reflects depolarization of the correct and left ventricles which in the normal middle lasts for about 70–110 ms. The starting time negative deflection of the QRS complex is denoted the Q wave, and the first positive is denoted the R wave, while the negative deflection subsequent to the R wave is denoted the S wave (Figure 6.10). Although the QRS complex may exist composed of less than three individual waves, it is all the same referred to as a QRS complex. The morphology of the QRS complex is highly variable and depends on the origin of the heartbeat: the QRS elapsing of an ectopic shell may extend up to 250 ms, and is sometimes composed of more than three waves.

Since the QRS complex has the largest amplitude of the ECG waveforms, sometimes reaching 2−3 mV, information technology is the waveform of the ECG which is kickoff identified in any blazon of computer-based analysis. The algorithm that performs the search is termed the QRS detector and produces the "landmark data" required to further clarify the ECG characteristics, see Section 7.iv.

Due to its steep slopes, the frequency content of the QRS complex is considerably higher than that of the other ECG waves and is mostly concentrated in the interval 10–50 Hz (Effigy half-dozen.eleven). Similar to the P wave, ensemble averaging of the QRS circuitous has, in certain ECG recordings, uncovered high-frequency components which accept been found to convey diagnostic information. In particular, the presence of late potentials in the terminal portion of the QRS complex has received considerable attention; encounter page 447 for further details.

The ST segment is not actually a wave, just represents the interval during which the ventricles remain in an agile, depolarized state. The ST segment begins at the cease of the S wave (the J point) from where it proceeds well-nigh horizontally until information technology curves into the T wave (Figure half-dozen.ten). Changes in the ST segment, which make it either more elevated, depressed, or more steeply sloped, ofttimes signal various underlying cardiac conditions.

The T wave reflects ventricular repolarization and extends about 300 ms after the QRS complex. The position of the T wave is strongly dependent on middle rate, becoming narrower and closer to the QRS complex at rapid rates; this "wrinkle" property does non apply to the P wave or the QRS complex. The normal T wave has a smooth, rounded morphology which, in most leads, is associated with a single positive peak.

The T wave is sometimes followed by another wearisome wave (the U wave) whose origin is unclear simply is probably ventricular after-repolarization. At rapid center rates, the P wave merges with the T wave, causing the T moving ridge stop point to become fuzzy also every bit the P moving ridge onset. As a result, information technology becomes extremely hard to make up one's mind the T wave stop point because of the gradual transition from moving ridge to baseline.

The RR interval represents the length of a ventricular cardiac cycle, measured between two successive R waves, and serves equally an indicator of ventricular rate. The RR interval is the primal rhythm quantity in whatsoever blazon of ECG interpretation and is used to characterize different arrhythmias equally well as to study middle rate variability.

The PQ interval is the time interval from the onset of atrial depolarization to the onset of ventricular depolarization. Accordingly, the PQ interval reflects the fourth dimension required for the electrical impulse to propagate from the SA node to the ventricles. The length of the PQ interval is weakly dependent on center charge per unit.

The QT interval represents the time from the onset of ventricular depolarization to the completion of ventricular repolarization. This interval normally varies with center rate and becomes shorter at more than rapid rates. It is therefore customary to correct the QT interval for heart rate—using nonlinear [xvi] or, meliorate, linear techniques [17]—so that the corrected QT interval allows an assessment that is roughly contained of heart rate. Prolongation of the QT interval has been observed in diverse cardiac disorders associated with increased take chances of sudden death.

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Overview of source separation applications

Y. Deville , ... R. Vigario , in Handbook of Blind Source Separation, 2010

xvi.seven Using physical mixture models or not

In addition to the dimension of the mixing model that we discussed in the previous section, a parameter of utmost importance when developing a BSS application is the nature (linear instantaneous, convolutive, nonlinear) of this model, because one should select accordingly the model used in the separating organisation. As already noted in a higher place, 1 should therefore derive a physical model of the relationship between the source signals and the observations, whenever this is possible. Nosotros have illustrated this arroyo in particular in this affiliate, by means of two examples dealing with communications (see sectionsixteen.4.two.2) and astrophysics hyperspectral images (see section16.v.1). BSS investigations not based on such physical models accept also been presented in the literature, however. Even for a single class of signals, different approaches may exist used from that bespeak of view, as shown beneath by considering two types of investigations that have been reported for electrocardiogram (ECG) analysis.

xvi.seven.1 Mother vs fetus heartbeat separation from multi-channel ECG recordings

1 of the well-nigh classical biomedical applications of BSS methods is related to ECG assay. It concerns the extraction of fetal heartbeats from a set of ECG signals recorded by means of cutaneous leads placed on the mother'southward skin. The signals thus obtained besides include mother's heartbeats and noise components. Moreover, concrete considerations show that the recorded signals are linear instantaneous mixtures of fetal heartbeats, female parent'south heartbeats and noise components[24].

This fetal heartbeat extraction problem was already tackled three decades agone by ways of adaptive dissonance canceling techniques, which are now conventional[83]. The performance thus achievable was peculiarly limited by the need for such techniques to utilise reference signals, i.east. unmixed sources. BSS algorithms now provide a more full general framework which avoids this restriction and makes it possible to reconsider such applications while expecting higher performance. Results obtained in such applications are reported for example in[24,87,88]. A comparison of BSS methods and of more classical approaches based on Singular Value Decomposition (SVD) is also provided in[24].

A typical investigation of fetal ECG extraction, reported by De Lathauwer et al.[24], is illustrated in Figs xvi.17 and 16.xviii. The measured signals are shown in Fig. 16.17 (such signals are available on the Cyberspace: run into section16.viii). All recorded channels include an almost periodic component, corresponding to the mothers's heartbeats. Each beat out yields a major superlative sixteen in these signals. 7 such peaks appear over the recorded period (i.eastward. five s). In addition, the acme three channels of Fig. 16.17 may exist expected to contain a lower-magnitude, almost periodic, component. However, the associated waveform cannot exist easily interpreted from these signals.

FIGURE 16.17. Eight-channel ECG recording[24] (courtesy of 50. De Lathauwer).

Effigy 16.eighteen. Source estimates derived by BSS method from ECG recording[24] (courtesy of L. De Lathauwer).

Applying the linear instantaneous BSS method considered in[22] to these recordings yields the signals shown in Fig. 16.xviii (two other methods led to similar results in[24]). The mother'south seven-peak periodic signals conspicuously appear in the top 3 output channels (an interpretation of the dimension of the associated subspace is provided in[24]). Moreover, another periodic betoken is extracted, especially in the 6th aqueduct. The considered recording period contains 12 peaks, i.due east. 12 cycles of this signal. This periodic point, which has a higher frequency than the mother's heartbeats, corresponds to the fetal ECG.

16.vii.2 Analysis of middle control from single-channel ECG

ECG signals have also been analyzed with other goals. In particular, Vetter et al.[81] presented an investigation where the BSS aspect of the trouble appears in a much less natural way than above to not-specialists. This investigation concerns the analysis of the command of the middle past the autonomic nervous system, whose alterations have been shown to play an important office in many pathophysiological situations. This middle command organization contains two antagonistic parts, corresponding to the cardiac sympathetic (CSNA) and parasympathetic (CPNA) nervous activities. Variations in these activities influence heart behavior and yield modifications in the ECG. The reported investigation aims at extracting the original CSNA and CPNA signals only from a single-channel observed ECG, or more than precisely from 2 parameters derived from it. These parameters are the successive so-called RR and QT intervals, which respectively correspond to the time interval betwixt adjacent heartbeats and to the duration of a specific portion of the ECG bike.

This separation of CSNA and CPNA signals from their RR and QT mixtures is performed past means of a BSS method suited to linear instantaneous mixtures. Unlike in most applications, this linear instantaneous mixing structure is not selected every bit a outcome of detailed modeling of the considered physical organization, which would evidence that the measured signals provide this type of mixture. Instead, the approach used here contains two aspects. Qualitative physiological noesis corresponding to the above-mentioned middle control model is first used. It shows that each of the RR and QT parameters depends on the CSNA and CPNA signals, i.e. is a mixture (of unspecified type at this phase) of these signals (and perhaps of others signals, which is confirmed below). Then, a linear instantaneous mixture model is selected, based on two motivations. On the one hand, this investigation is focused on a "small-signal" approximation and only aims at extracting the almost salient features of the variations of the CSNA and CPNA signals, which leads the authors to utilise linear modeling. On the other hand, they want to develop a uncomplicated analysis tool and they therefore but consider an instantaneous mixture model.

The other major upshot of the BSS problem thus introduced concerns the independence of the sources to be restored (or at least their uncorrelation). It has been shown that the CSNA and CPNA signals are not independent. Even so, previous work of the authors leads them to presume that two independent components, respectively sensitive to the CSNA and CPNA signals, may be derived from the observed ECG parameters.

The BSS method applied to the considered ECG parameters is a classical approach intended for temporally correlated sources, often referred to equally SOBI (2nd-Gild Bullheaded Identification)[vii]. Moreover, it is preceded past a noise reduction stage based on PCA. This stage aims at reducing the influence of all the "noise" signals independent past the considered ECG parameters, in addition to the mixed contributions of CNSA and CPNA. These noise sources correspond to the influence of respiration and unknown stochastic phenomena on ECG parameters, together with measurement and quantization racket.

In biomedical applications, BSS methods frequently provide estimates of hidden variables, such as CSNA and CPNA here, which are not accessible in humans. These estimated variables cannot be compared to the lacking original sources and are therefore hardly interpreted. This makes information technology difficult to validate the performance of BSS methods in such applications. The authors here solve this problem by using specific experimental protocols. These protocols are selected because they elicit or inhibit sympathetic or parasympathetic responses and therefore brand it possible to cheque if the proposed approach is able to highlight changes in the levels of CPNA and CNSA. This shows the effectiveness of linear instantaneous BSS methods in this application and the need to use a denoising stage. This arroyo outperforms the traditional indicator based on Fast Fourier Transform (FFT). 17

Information technology should be mentioned that the same authors previously reported a related approach [79,80]. However, the latter method requires non-causal convolutive BSS algorithms and simultaneous recordings of ECG and arterial blood pressure, which may be cumbersome in clinical applications. The above-described approach therefore has the advantage of requiring only instantaneous BSS methods and recording of one ECG channel.

sixteen.7.3 Additional comments nigh operation evaluation

The application that nosotros but described highlighted the trouble of performance evaluation in applications where source signals are not accessible. A general solution to this problem still, exists in cases when, even if the source signals to be restored are not known, prior data virtually their properties is available. Indeed, 1 may indirectly verify that the considered BSS methods are successful, by checking to what extent the source estimates that they provide really accept the expected properties.

This arroyo is, for example, used in[sixteen], still in the framework of cardiac signal analysis. This paper concerns the extraction of atrial activity during atrial fibrillation episodes. The authors take advantage of the fact that atrial activeness has a narrowband spectrum, which contains a major peak at a frequency situated between three and 9 Hz, unlike the other signals involved in this awarding. The approach proposed for approximately evaluating the quality of the extraction of atrial activity by means of a BSS method then consists in measuring the spectral concentration of the estimated source. A BSS method is considered to yield good performance in this application if the spectrum of the bespeak that information technology provides is concentrated around its peak situated in the frequency band ranging from 3 to nine Hz. On the opposite, this spectrum is more than spread out if the restored indicate contains undesired contributions resulting from other sources.

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Biosignal monitoring using wearables: Observations and opportunities

Yashodhan Athavale , Sridhar Krishnan , in Biomedical Signal Processing and Command, 2017

2.3.ane About ECG Signals: properties and acquisition

Electrocardiography (ECG) involves recording of electrical impulses generated by heart muscles during its regular or irregular beating activity, past using electrodes placed over specific regions on the human trunk (mostly around the chest region). The intention is to capture minute heart beat signal changes which happens when the heart muscles depolarize during each beating wheel [33] . A typical ECG wave is characterized past 3 morphological patterns: a P-moving ridge (atrial polarization wave), a QRS-complex wave (ventricular depolarization moving ridge) and a T-moving ridge (ventricular repolarization wave). In terms of signal backdrop, a surface ECG signal has a frequency range of 0.05–150  Hz in diagnostic mode and 0.v–xl   Hz in monitoring fashion, with amplitude ranging from 0.1 to five   mV [33].

Detecting abnormalities in center rhythms usually involves analyzing for irregular patterns in either of the three aforementioned signal patterns. Most unremarkably occurring disorders include atrial and ventricular fibrillation, myocardial infarction and sudden cardiac death. ECG information when monitored with other body parameters such as claret pressure, glucose levels and pulse rate, could as well serve as an indicator of diseases such every bit diabetes, high/low claret force per unit area and stress levels. In a clinical setting, ECG is typically monitored using a 12-electrode placement configuration [10] connected to a standard heart rhythm recording and monitoring system. This organization allows for continuous betoken acquisition, filtering and analysis which aids the medico is making real-time decisions about a patient's cardiac health. But this arrangement cannot exist hands transferred to a dwelling or remote setting due to its size and installation complexity.

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A survey on ECG analysis

Selcan Kaplan Berkaya , ... M. Bilginer Gulmezoglu , in Biomedical Signal Processing and Control, 2018

3.1 P-QRS-T complex features

The P-QRS-T complex features for an ECG point basically correspond to the locations, durations, amplitudes, and shapes of particular waves or deflections within the signal [106,107]. Typically, an ECG signal has a full of v major deflections, including P, Q, R, S, and T waves, plus a minor deflection, namely, the U moving ridge, equally shown in Fig. 2 [108 ]. The P wave is a minor depression-voltage deflection abroad from the baseline that is caused by the depolarization of the atria prior to atrial contraction as the activation (depolarization) wave-forepart propagates from the sinoatrial node through the atria. The Q moving ridge is a downward deflection after the P wave. The R wave follows equally an upward deflection, and the S wave is a downward deflection following the R wave. Q, R, and S waves together indicate a single event. Hence, they are usually considered to be the QRS complex. The features based on the QRS complex are amid the most powerful features for ECG assay. The QRS-complex is caused by currents that are generated when the ventricles depolarize prior to their contraction. Although atrial repolarization occurs earlier ventricular depolarization, the latter waveform (i.e., the QRS-complex) has a much greater amplitude, and atrial repolarization is, therefore, not seen on an ECG. The T wave, which follows the S wave, is ventricular repolarization, whereby the cardiac muscle is prepared for the next cycle of the ECG. Finally, the U wave is a small-scale deflection that immediately follows the T moving ridge. The U wave is usually in the same direction as the T moving ridge.

Fig. 2

Fig. 2. Standard fiducial points in the ECG (P, Q, R, S, T, and U) together with clinical features (listed in Table 2) [108].

Researchers utilize diverse attributes of the QRS complex as the features. Some of those attributes are the R wave duration, P+ amplitude, QRS p-p aamplitude, R moving ridge amplitude, ST aamplitude, T+ amplitude, QRS wave area and ST slope. The R wave duration is the time that passes betwixt the beginning and end of the R wave [109]. The P+ amplitude can be defined as the difference betwixt the P signal and the other subsequent points where the signal rises again. The QRS p-p aamplitude is the difference between the R and Q points in the QRS complex in terms of the amplitude. The R wave amplitude tin be defined as the height of the R wave from the baseline. The ST aamplitude is the difference between the Southward and T points in terms of the amplitude values. The T+ amplitude can be defined as the difference between the T bespeak and the subsequent point where the signal rises again. The QRS moving ridge area is the area of the region when a rectangle is drawn on the QRS circuitous using the Q, R and S points [110]. The ST slope is the angle of the line, which can be drawn from the S point to the T point of the QRS complex. Some of the recent studies on ECG analysis that employ QRS features are [35,twoscore,67,70,76,80,88,89,91,106,107,111–117].

In addition, certain intervals within the ECG signal carry meaningful information and are employed as the features. For example, the PR interval is the duration between the first of the P wave and the QRS complex of an electrocardiogram. This duration contains signals between the onset of atrial depolarization and the onset of ventricular depolarization [118]. The QT-interval is the fourth dimension between the onset of ventricular depolarization and the end of ventricular repolarization. The ST-interval is the time betwixt the terminate of the S-wave and the beginning of the T-wave [119]. The RR interval (or heartbeat interval) is the fourth dimension between the R tiptop of a heartbeat and the post-obit heartbeat, which could be its predecessor or successor [ix]. The RR-interval features are determined to realize the dynamic characteristics of the ECG signals [120]. Unlike RR interval features are used in [4,120,121].

Standard values for several QRS circuitous features of a normal ECG betoken and healthy subjects with no cardiac abnormalities are listed in Table 2. The detailed information for these features is provided in [18] besides.

Tabular array 2. Typical lead Ii ECG features and their normal values in the sinus rhythm at a middle rate of sixty bpm for a healthy male adult [xviii].

Characteristic Normal Value Normal Limit
P width 110 ms ±20 ms
PR interval 160 ms ±40 ms
QRS width 100 ms ±20 ms
QTc (corrected) interval 400 ms ±twoscore ms
P amplitude 0.15 mV ±0.05 mV
QRS height ane.5 mV ±0.v mV
ST level 0 mV ±0.i mV
T amplitude 0.three mV ±0.2 mV

Since the extraction of the QRS features requires detection of the abovementioned fiducial points, various QRS circuitous detection algorithms are proposed in the literature. Although the most common one is the Pan-Tompkins algorithm [104], QRS detection algorithms tin can exist categorized as derivative [122], digital filters [104,123,124], wavelet transform [125], neural networks [126] and phasor transform [127] based algorithms. In add-on, high-order moments are used to detect QRS complexes in [111]. The difference operation method is used to find the fiducial points in [77]. In that location are also some studies that especially brand use of R meridian detection, such as [39,86,111,128–133]. In [105], a dynamic threshold based on a finite country automobile is used to notice the R peaks. In [93], the techniques based on differentiation are used to detect the fiducial points. In [57,75,120], the Pan-Tompkins algorithm is used as well.

There also are several open up-source QRS detectors that can be used past researchers. For case, EP-LIMITED, based on the Hamilton and Tompkins algorithm, and WQRS, based on a length transform, are used in [72]; the Augsburg Biosignal Toolbox is used in [81]; and ECGPUWAVE software is used in [134,135] to notice QRS and recognize the ECG moving ridge boundary and extract the morphological features.

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