The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.

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The principles of software QRS detection

https://www.repositorio.ufop.br/bitstream/123456789/7011/1/ARTIGO_ECGHeartBeat.pdf

ECG-based heartbeat classification for arrhythmia detection

A detector for providing position detection of objects over a sensor with a first and second set of conductor lines forming a grid with a plurality of junctions there between at which the conductor lines do not contact, includes a signal generator providing a signal to at least one conductor line of the first set of conductor lines, and circuitry detecting output arising from one or both of an electromagnetic stylus and one or more fingers when present. The output arising from each of the one or more fingers is detected from at least one conductor line of the second set of conductor lines in response to the signal provided to the at least one conductor line of the first set of conductor lines. The circuitry detects positions of one or both the electromagnetic stylus and each of the one or more fingers when present responsive to the output detected.

Touch detection for a digitizer

This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.

Optimization of ECG Classification by Means of Feature Selection

A method of modifying the force of contraction of at least a portion of a heart chamber, including providing a subject having a heart, comprising at least a portion having an activation, and applying a non-excitatory electric field having a given duration, at a delay after the activation, to the portion, which causes the force of contraction to be increased by a least 5%.

Electrical muscle controller

The authors have investigated the QRS complex, extracted from electrocardiogram (EGG) data, using fuzzy adaptive resonance theory mapping (ARTMAP) to classify cardiac arrhythmias. Two different conditions have been analyzed: normal and abnormal premature ventricular contraction (PVC). Based on MIT/BIH database annotations, cardiac beats for normal and abnormal QRS complexes were extracted from this database, scaled, and Hamming windowed, after bandpass filtering, to yield a sequence of 100 samples for each QRS segment. From each of these sequences, two linear predictive coding (LPC) coefficients were generated using Burg's maximum entropy method. The two LPC coefficients, along with the mean-square value of the QRS complex segment, were utilized as features for each condition to train and test a fuzzy ARTMAP neural network for classification of normal and abnormal PVC conditions. The test results show that the fuzzy ARTMAP neural network can classify cardiac arrhythmias with greater than 99% specificity and 97% sensitivity.

F.M. Ham

Papers

Classification of cardiac arrhythmias using fuzzy ARTMAP