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Journal ArticleDOI

A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV

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TLDR
The design, test methods, and results of an ambulatory QRS detector are presented and the aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device.
Abstract
The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively.

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Citations
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References
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Journal ArticleDOI

A Real-Time QRS Detection Algorithm

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.
Journal ArticleDOI

Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database

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.
Journal ArticleDOI

A comparison of the noise sensitivity of nine QRS detection algorithms

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.
Journal ArticleDOI

Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection

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.
Book

Probability, Random Variables and Random Signal Principles

TL;DR: 1 Probability 2 The Random Variable 3 Operations on one Random Variable--Expectation 4 Multiple Random Variables 5 Operations of Multiple Randomvariables 6 Random Processes-Temporal Characteristics 7 Random processes-Spectral Characteristics 8 Linear Systems with Random Inputs 9 Optimum Linear Systems 10 Some Practical Applications of the Theory.
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