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.
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01 Feb 2013
TL;DR: Psychlog is a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research, and allows administering self-report questionnaires at specific times or randomly within a day.
Abstract: Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user's experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users' psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source ( http://sourceforge.net/projects/psychlog/ ).
152 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...ematic transformation of into power spectral density (PSD) have been used to characterize a number of psychological illnesses, including major depression and panic disorders [26]....
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...The PsychLog application extracts QRS peaks through a dedicated algorithm [26] and R–R interval time series....
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01 Jan 2006
TL;DR: Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests.
Abstract: Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests
152 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...Therefore, it will inherit some of the problems already discussed while investigating WDD measure....
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06 Jun 2012
TL;DR: The results show that implementation of Bluetooth Low Energy (BLE) technology in the existing ECG monitoring system not only eliminates the physical constraints imposed by hard-wired link but also highly reduces the power consumption of the long-term monitoring system.
Abstract: A wireless electrocardiogram (ECG) monitoring system is developed which integrates Bluetooth Low Energy (BLE) technology. This BLE-based system is comprised of a single-chip ECG signal acquisition module, a Bluetooth module and a smart-phone. Apple's iPhone 4S is selected as the mobile device platform, which embedded with Bluetooth v4.0, Wi-Fi and iOS. In this paper, the monitoring system is able to acquire ECG signals through 2-lead electrocardiogram (ECG) sensor, transmit the ECG data via the Bluetooth wireless link, process and display the ECG waveform in a smart-phone. The results show that implementation of Bluetooth Low Energy (BLE) technology in the existing ECG monitoring system not only eliminates the physical constraints imposed by hard-wired link but also highly reduces the power consumption of the long-term monitoring system.
151 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...In the application of smart-phone, the communication with BLE peripheral can be accomplished by using the Core Bluetooth Framework [5]....
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TL;DR: A new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals is presented and a novel CAD Risk index is developed using significant features to discriminate the two classes using a single number.
Abstract: Coronary artery disease (CAD) is the narrowing of coronary arteries leading to inadequate supply of nutrients and oxygen to the heart muscles. Over time, the condition can weaken the heart muscles and may lead to heart failure, arrhythmias and even sudden cardiac death. Hence, the early diagnosis of CAD can save life and prevent the risk of stroke. Electrocardiogram (ECG) depicts the state of the heart and can be used to detect the CAD. Small changes in the ECG signal indicate a particular disease. It is very difficult to decipher these minute changes in the ECG signal, as it is prone to artifacts and noise. Hence, we detect the R peaks from the ECG and use heart rate signals for our analysis. The manual inspection of the heart rate signals is time consuming, taxing and prone to errors due to fatigue. Hence, a decision support system independent of human intervention can yield accurate repeatable results. In this paper, we present a new method for diagnosis of CAD using tunable-Q wavelet transform (TQWT) based features extracted from heart rate signals. The heart rate signals are decomposed into various sub-bands using TQWT for better diagnostic feature extraction. The nonlinear feature called centered correntropy ( CC ) is computed on decomposed detail sub-band. Then the principal component analysis (PCA) is performed on these CC to transform the number of features. These clinically significant features are subjected to least squares support vector machine (LS-SVM) with different kernel functions for automated diagnosis. The experimental results demonstrate better classification accuracy, sensitivity, specificity and Matthews correlation coefficient using Morlet wavelet kernel function with optimized kernel and regularization parameters. Also, we have developed a novel CAD Risk index using significant features to discriminate the two classes using a single number. Our proposed methodology is more suitable in classification of normal and CAD heart rate signals and can aid the clinicians while screening the CAD patients.
151 citations
TL;DR: Current works are summarized to suggest key directions for the development of future RR monitoring methodologies and the merits and limitations of each method are highlighted and discussed.
Abstract: Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.
151 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Beat detection is typically performed using a segmentation algorithm (such as that proposed by Li et al (2010) for PPG signals and Pan and Tompkins (1985) for ECG signals)....
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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