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Journal ArticleDOI

A Real-Time QRS Detection Algorithm

TLDR
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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Proceedings ArticleDOI

The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition

TL;DR: A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique is presented and able to detect R peak with high sensitivity when compared to other algorithms used in Physionet ECG database.
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A Hidden Markov Model for Seismocardiography

TL;DR: The proposed hidden Markov model approach for processing seismocardiograms is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.
Proceedings Article

Intelligent Heartsound Diagnostics on a Cellphone Using a Hands-Free Kit

TL;DR: Results indicate that HR and HRV can be accurately assessed from acoustic recordings of heart sounds using only a cellphone and hands-free kit and that the low-frequency response (<100 Hz) is key to the success of heart sound analysis on cellphones.
Journal ArticleDOI

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals.

TL;DR: The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia and Bagged Trees proved to be the most suitable classifier type among the classification models studied.
Proceedings ArticleDOI

PEAR: Power efficiency through activity recognition (for ECG-based sensing)

TL;DR: The PEAR (Power Efficiency though Activity Recognition) framework is presented using an ECG-based body sensor network as a case study and an activity-aware energy model is presented along with energy profiling results.
References
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Journal ArticleDOI

Software QRS detection in ambulatory monitoring--a review.

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.
Journal ArticleDOI

Optimal QRS detector.

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.
Journal ArticleDOI

Automated High-Speed Analysis of Holter Tapes with Microcomputers

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.
Journal ArticleDOI

Online digital filters for biological signals: some fast designs for a small computer.

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.
Journal ArticleDOI

A robust-digital QRS-detection algorithm for arrhythmia monitoring

TL;DR: In this paper a new robust single lead QRS-detection algorithm is presented, allowing real-time applications and results are presented.
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