A Real-Time QRS Detection Algorithm
01 Mar 1985-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 32, Iss: 3, pp 230-236
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.
Citations
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TL;DR: Results for pre-ejection period reactivity supported the hypothesis that the relationship between listening demand and listening effort is moderated by other variables and suggested that a broader perspective on the determinants of listening effort was warranted.
Abstract: A common element of the psychophysiological research on listening effort is the focus on listening demand as determinant of effort. The article discusses preceding studies and theorizing on effort to show that the link between listening demand and listening effort is moderated by various variables. Moreover, I will present a recent study that examined the joint effect of listening demand and success importance on effort-related cardiovascular reactivity in an auditory discrimination task. Results for pre-ejection period reactivity-an indicator of sympathetic activity-supported the hypothesis that the relationship between listening demand and listening effort is moderated by other variables: Pre-ejection period reactivity was higher in the high-demand-high-success-importance condition than in the other three conditions. This new finding as well as the findings of previous research on effort suggest that a broader perspective on the determinants of listening effort is warranted.
63 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...R-peaks in the ECG signal were automatically identified using the algorithm proposed by Pan and Tompkins (1985)....
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...R-peaks in the ECG signal were automatically identified using the algorithm proposed by Pan and Tompkins (1985). The...
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TL;DR: In this article, the authors defined fog computing in the context of medical IoT and implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.
Abstract: In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.
63 citations
TL;DR: A novel approach, Hybrid Complex Wavelet, has been proposed to identify and detect the components of ECG signal such as QRS complex and R peak and reached better recognition accuracy in comparison to other well-known approaches.
63 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...Different Linear, Nonlinear and Morphological algorithms have been proposed to detect QRS complex [1,2]....
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TL;DR: The experimental results show that the proposed method is suitable for various morphologies of ECG data, and that it achieves higher compression ratio with the characteristic features well preserved.
63 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...Except the subband LLM at the lowest resolution and the subbands LH1, HL1, and HH1 at the highest resolution, a point in a lower resolution subbands correspond to four points at the same spatial position in a higher resolution subbands....
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References
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TL;DR: This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure and a discussion of some of the current detection schemes is presented.
Abstract: The QRS detection algorithm is an essential part of any computer-based system for the analysis of ambulatory ECG recordings. This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure. A discussion of some of the current detection schemes is presented with regard to this structure. Some additional features of QRS detectors are mentioned. The evaluation of performance and the problem of multichannel detection, which is now gaining importance, are also briefly treated.
254 citations
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).
151 citations
TL;DR: An automated Holtes scanning system based on two microcomputers that detects QRS complexes and measures the QRS durations using computations of first and second derivatives, and can process Holter tapes at 60 times real time and produce printed summaries and 24 h trend plots.
Abstract: We have developed an automated Holtes scanning system based on two microcomputers. One is a preprocessor that detects QRS complexes and measures the QRS durations using computations of first and second derivatives. Thismicrocomputer interfaces to a secondmicro-computer that does arrhythmia analysis, logging, and reporting using R-R intervals and QRS durations. This system can process Holter tapes at 60 times real time and produce printed summaries and 24 h trend plots of several variables including heart rate and PVC count.
127 citations
"A Real-Time QRS Detection Algorithm..." refers methods in this paper
...The slope of the R wave is a popular signal feature used to locate the QRS complex in many QRS detectors [5]....
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TL;DR: The possibilities for extending the class of lowpass recursive digital filters to include high pass, bandpass, and bandstop filters are described, and experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible.
Abstract: After reviewing the design of a class of lowpass recursive digital filters having integer multiplier and linear phase characteristics, the possibilities for extending the class to include high pass, bandpass, and bandstop (‘notch’) filters are described. Experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible. The practical application of such filters is illustrated by using a notch desgin to remove mains-frequency interference from an e.c.g. waveform.
104 citations
TL;DR: In this paper a new robust single lead QRS-detection algorithm is presented, allowing real-time applications and results are presented.
101 citations