Optimal QRS detector.
01 May 1983-Medical & Biological Engineering & Computing (Med Biol Eng Comput)-Vol. 21, Iss: 3, pp 343-350
TL;DR: The problem of detecting the QRS complex in the presence of noise was analysed and an optimised threshold criterion based on FP/FN was developed.
Abstract: The problem of detecting the QRS complex in the presence of noise was analysed. Most QRS detectors contain a filter to improve the signal-to-noise ratio and compare the signal with a threshold. In an earlier paper we identified an optimal filter. Various techniques to generate threshold and detector designs were studied. Automatic gain-control circuits with a fixed threshold have a very slow response to different rhythms. Automatic threshold circuits based on simple peak-detection schemes have a fast response, but are very sensitive to sudden variations in QRS amplitudes and noise transients. None of the methods described to date present any optimisation criteria for detecting the signal (QRS complex) in the presence of noise. The probabilities of FPs (false positives) and FNs (false negatives) were investigated and an optimised threshold criterion based on FP/FN was developed. Presently, data are being collected to compare various techniques from their ROC (receiver operating characteristics).
Citations
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TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
TL;DR: This work implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process, which has a sensitivity of 99.69 percent and positive predictivity of 98.77 percent when evaluated with the MIT/BIH arrhythmia database.
Abstract: We have investigated the quantitative effects of a number of common elements of QRS detection rules using the MIT/BIH arrhythmia database. A previously developed linear and nonlinear filtering scheme was used to provide input to the QRS detector decision section. We used the filtering to preprocess the database. This yielded a set of event vectors produced from QRS complexes and noise. After this preprocessing, we tested different decision rules on the event vectors. This step was carried out at processing speeds up to 100 times faster than real time. The role of the decision rule section is to discriminate the QRS events from the noise events. We started by optimizing a simple decision rule. Then we developed a progressively more complex decision process for QRS detection by adding new detection rules. We implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process. The resulting QRS detection algorithm has a sensitivity of 99.69 percent and positive predictivity of 99.77 percent when evaluated with the MIT/BIH arrhythmia database.
1,137 citations
TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >
1,083 citations
TL;DR: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection and an adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex.
Abstract: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy electrocardiogram (ECG), and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm. >
902 citations
TL;DR: A multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG) which incorporates a filter bank which decomposes the ECG into subbands with uniform frequency bandwidths and inherently lends itself to a computationally efficient structure.
Abstract: The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.
767 citations
References
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Book•
01 Jan 1971
TL;DR: In underwater sonar systems, external acoustic noise is generated by waves and wind on the water surface, by biological agents (fish, prawns, etc.), and by man-made sources such as engine noise.
Abstract: Probability. Random Processes. Narrowband Signals. Gaussian Derived Processes. Hypothesis Testing. Detection of Known Signals. Detection of Signals with Random Parameters. Multiple Pulse Detection of Signals. Detection of Signals in Colored Gaussian Noise. Estimation of Signal Parameters. Extensions. References. Bibliography. Index.
1,421 citations
01 Jan 2001
TL;DR: This work presents theoretical principles of the generalized approach to signal detection theory and experimental study of generalized detectors type of signals in communications detction performances.
Abstract: Classical and modern signal detection theories theoretical principles of the generalized approach to signal detection theory generalized approach to detection of signals with stochastic parameters study of the generalized approach to signal detection theory in communications detction performances experimental study of generalized detectors type of signals.
301 citations
Book•
01 Jan 1966
TL;DR: The starting point for signal detection theory is that nearly all decision making takes place in the presence of some uncertainty, and the classic example of detecting brief, dim flashes of light in a dark room is begun.
Abstract: The starting point for signal detection theory is that nearly all decision making takes place in the presence of some uncertainty. Signal detection theory provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty. I begin here with the classic example of detecting brief, dim flashes of light in a dark room. Imagine that we use a simple forced-choice method in which the light is flashed on half of the trials (randomly interleaved). On each trial, the subject must respond " yes " or " no " to indicate whether or not they think the light was flased. We assume that the subjects' performance is determined by the number of photon absorptions/photopigment isomerizations on each trial. There are two kinds of noise factors that limit the subject's performance: internal noise and external noise. External noise. There are many possible sources of external noise. The main source of external noise to consider here is the quantal nature of light. On average, the light source is set up to deliver a certain stimulus intensity, say 100 photons. A given trial, however, there will rarely be exactly 100 photons emitted. Instead, the photon count will vary from trial to trial following a Poisson distribution. Internal noise. Internal noise refers to the fact that neural responses would be noisy, even if the stimulus was exactly the same on each trial. Some of the emitted photons will be scattered by the cornea, the lens, and the other goopy stuff in the eye. The number of scattered photons will vary randomly from trial to trial. Of the photons that reach the photoreceptors, not all of them will be absorbed by the photopigments. There are other sources of internal (neural) noise as well, but I will ignore those for the time being.
277 citations
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TL;DR: A QRS complex detector based on optimum predetection with a matched filter is described, which shows that differentiation reduces Gaussian error by √6 and errors caused by variable QRS amplitudes are close to zero.
Abstract: A QRS complex detector based on optimum predetection with a matched filter is described. In order to improve the accuracy of the QRS complex recognition under conditions of Gaussian noise and variable QRS amplitude, the first derivative of the e.c.g. was used with zero threshold detection. In addition, two nonlinear circuits cut off low amplitude noise and all spikes which appear for a fixed time after QRS detection. Calculation of errors shows that differentiation reduces Gaussian error by √6 and errors caused by variable QRS amplitudes are close to zero. This detector is especially useful with biotelemetry systems since it reduces many interferences due to patient movement and communication channel distortion.
136 citations
"Optimal QRS detector." refers methods in this paper
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