The principles of software QRS detection

Köhler, Bernd; Hennig, Ch.; Orglmeister, Reinhold

doi:10.1109/51.993193

Cited by 1,075 publications

(570 citation statements)

References 85 publications

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“…These are very high results and indicate that our approach is well suited for QRS detection. Indeed, our results are competitive, and sometimes better, to those obtained by many other researchers in this area [11] even having used much simpler pre-and post-processing algorithms. In particular our approach yields a consistently lower gap between the sensitivity and precision performance where many researchers obtain differences of ≈ 1%.…”

Section: F Performance Evaluationsupporting

confidence: 47%

A Machine Learning Approach to Objective Cardiac Event Detection

Twomey¹,

Flach

2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms A Machine Learning Approach to Objective Cardiac Event Detection N. Twomey, P. A. FlachAbstract-This paper presents an automated framework for the detection of the QRS complex from Electrocardiogram (ECG) signals. We introduce an artefact-tolerant pre-processing algorithm which emphasises a number of characteristics of the ECG that are representative of the QRS complex. With this processed ECG signal we train Logistic Regression and Support Vector Machine classification models. With our approach we obtain over 99.7% detection sensitivity and precision on the MIT-BIH database without using supplementary de-noising or pre-emphasis filters.

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Section: F Performance Evaluationsupporting

confidence: 47%

A Machine Learning Approach to Objective Cardiac Event Detection

Twomey¹,

Flach

2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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“…(17). By this method, each element of t IPG ( j) which was detected by the algorithm could be assigned to the according t PPG (i).…”

Section: Resultsmentioning

confidence: 99%

Development of a real-time, semi-capacitive impedance phlebography device

Weyer

Weber

Kleeberg

et al. 2015

Journal of Electrical Bioimpedance

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Chronic venous insufficiency of the lower limbs is a disease which is caused by an increased blood pressure inside the veins of the leg and the resulting increase of the contained blood volume. This work focuses on developing a device which uses impedance plethysmography, also known as impedance phlebography, to obtain information about the blood volume in the lower leg and provides the possibility to measure the impedance semi contact-less, e.g. through compression stockings. Furthermore a real-time beat-to-beat interval detection algorithm was implemented. Finally, the function of the developed impedance measuring system and the whole system was verified by comparing it with a gold standard. In comparison to the conductive coupling, the system performed similarly. The analysis showed that the developed system is suitable for semi-capacitive IPG. The algorithm was implemented conservatively since it provided a good false-positive rate of 0 %, but only a moderate sensitivity of about 68 %. Reliable and continuous measurement of the pulse signal was only possible in periods of immobility.

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“…Detector algorithms can be based on heuristic derivative equations that identify discrete measurements or adaptive thresholds, for example, the increasing edge of the R-peak (Bonner et al, 1972;Pryor et al, 1969). Alternatively, they can be based on complex statistical algorithms that use linear or nonlinear filters, different transformations, or discriminant function analysis (Köhler et al, 2003;Pan and Tompkins, 1985;Romhilt and Estes, 1968). Interpolation algorithms, to replace missing or abnormal heart period series, include proximal, piecewise cubic Hermite, non-linear predictive interpolation, linear, and cubic spline interpolations (Kim et al, 2009;Lippman et al, 1994;Malik and Camm, 1995).…”

Section: Heart Rate Variabilitymentioning

confidence: 99%

Measurement fidelity of heart rate variability signal processing: The devil is in the details

Jarrin

McGrath

Giovanniello

et al. 2012

International Journal of Psychophysiology

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Heart rate variability (HRV) is a particularly valuable quantitative marker of the flexibility and balance of the autonomic nervous system. Significant advances in software programs to automatically derive HRV have led to its extensive use in psychophysiological research. However, there is a lack of systematic comparisons across software programs used to derive HRV indices. Further, researchers report meager details on important signal processing decisions making synthesis across studies challenging. The aim of the present study was to evaluate the measurement fidelity of time-and frequency-domain HRV indices derived from three predominant signal processing software programs commonly used in clinical and research settings. Triplicate ECG recordings were derived from 20 participants using identical data acquisition hardware. Among the time-domain indices, there was strong to excellent correspondence (ICC avg =0.93) for SDNN, SDANN, SDNNi, rMSSD, and pNN50. The frequency-domain indices yielded excellent correspondence (ICC avg =0.91) for LF, HF, and LF/HF ratio, except for VLF which exhibited poor correspondence (ICC avg =0.19). Stringent user-decisions and technical specifications for nuanced HRV processing details are essential to ensure measurement fidelity across signal processing software programs.

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The principles of software QRS detection

Cited by 1,075 publications

References 85 publications

A Machine Learning Approach to Objective Cardiac Event Detection

A Machine Learning Approach to Objective Cardiac Event Detection

Development of a real-time, semi-capacitive impedance phlebography device

Measurement fidelity of heart rate variability signal processing: The devil is in the details

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