Heart rate
Andrew R Houghton in Making Sense of the ECG, 2019
Measurement of the heart rate and the identification of the cardiac rhythm go hand in hand, as many abnormalities of heart rate result from arrhythmias. This chapter describes ways to measure the heart rate and the abnormalities that can affect it. Depolarization of the ventricles produces the QRS complex on the electrocardiogram (ECG), and so it is the rate of QRS complexes that needs to be measured to determine the heart rate. Some ECG machines will calculate heart rate and print it on the ECG, but always check machine-derived values, as the machines do occasionally make errors. Asystole implies the absence of ventricular activity, and so the heart rate is zero. The chaotic nature of the underlying ventricular activity can give rise to a variety of ECG appearances, but all have the characteristics of being unpredictable and chaotic.
Syntactic Methods
Arnon Cohen in Biomedical Signal Processing, 2019
The syntactic method uses structural information to define and classify patterns. Syntactic methods have been applied to the general problem of scene analysis, e.g., to the automatic recognition of chromosomes and finger prints. The syntactic approach has gained a lot of attention from researchers in the field of scene analysis since this approach possesses the structure-handling capability which seems to be important in analyzing patterns and scenes. The syntactic approach is also known by the terms linguistic, structural, and grammatical approach. The basic ideas behind the syntactic approach are similar in principal to those of the decision-theoretic approach. In most applications of syntactic signal processsing, a class of signals is given and a recognizer has to be designed to recognize the class of signals of interest. Several syntactic algorithms have been suggested for the analysis of the Electrocardiograms signal, especially for the problem of QRS complex detection.
Ventricular rhythms
Andrew R Houghton, David Gray in Making Sense of the ECG, 2014
Ventricular rhythms are those which arise from the ventricles, that is, below the level of the atrioventricular node. Like atrial and atrioventricular junctional ectopic beats, ventricular ectopic beats (VEB) appear earlier than expected. VEBs are also called ventricular extrasystoles, ventricular premature complexes, ventricular premature beats or premature ventricular contractions. Multiple VEBs which share the same QRS complex morphology originate from a single focus within the ventricles and are therefore called unifocal. VEBs can be harmless, particularly when the heart is structurally normal, but can also be associated with more hazardous arrhythmias, especially when heart disease is present. Accelerated idioventricular rhythm is essentially a slow form of ventricular tachycardia, with a heart rate of less than 120 beats/min. It occurs when an ectopic focus within the ventricles starts firing with a rate just higher than that of the sinoatrial node — this ventricular focus then takes over the cardiac rhythm.
Orderly display of limb lead ECGs raises Chinese intern’s diagnostic accuracy when determining frontal plane QRS axis
Published in Medical Education Online, 2019
Gang Li, Kheshav Banarsee, Jari A. Laukkanen, Lan Hao
Background: There is limited information on whether the orderly display of limb lead ECGs (electrocardiograms) can facilitate students to determine frontal plane QRS complex wave electrical axis. Objectives: The study investigated whether the orderly display of limb lead ECGs can raise Chinese undergraduate intern’s diagnostic accuracy when determining frontal plane axis. Design: A total of 147 fifth-year undergraduate interns aged between 21 and 25 years were randomly arranged into 2 groups: one group was given classically displayed ECGs of limb leads while the other group was given orderly displayed ECGs of limb leads. They were then taught to determine frontal plane axis with one of the above displays. The intern’s diagnostic accuracy and time used were measured. Results: After teaching, the orderly display can more effectively raise diagnostic accuracy when determining axis as compared to the classical display (76.65 ± 23.16% vs. 68.88 ± 23.21%, P
Detection of the QRS complex by linear prediction
Published in Journal of Medical Engineering & Technology, 2006
Z. E. Hadj Slimane, F. Bereksi Reguig
The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.
QRS complex detection based on simple robust 2-D pictorial-geometrical feature
Published in Journal of Medical Engineering & Technology, 2014
S. A. Hoseini Sabzevari, Majid Moavenian
In this paper a heuristic method aimed for detecting of QRS complexes without any pre-process was developed. All the methods developed in previous studies were used pre-process, the most novelty of this study was suggesting a simple method which did not need any pre-process. Toward this objective, a new simple 2-D geometrical feature space was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the pre-processed ECG signal. During each forward slide of the analysis window an artificial image was generated from the excerpted segment allocated in the window. Then, a geometrical feature extraction technique based on curve-length and angle of highest point was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Adaptive Network Fuzzy Inference Systems (ANFIS) were designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se = 99.93% and positive predictivity P+ = 99.92% were obtained.