Realtime ECG baseline removal: An isoelectric point estimation approach

Güven, Onur; Eftekhar, Amir; Hoshyar, Reza; Frattini, Giovanni; Kindt, W.J.; Constandinou, Timothy G.

doi:10.1109/biocas.2014.6981637

Cited by 10 publications

(13 citation statements)

References 11 publications

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“…Once these points are successfully located and their elevation differences are compensated, baseline wander is estimated through spline interpolation and subtracted from the raw ECG data. The detailed operation of the baseline wander estimation algorithm is explained in our previous work [5].…”

Section: A Baseline Wander Estimationmentioning

confidence: 99%

“…For this reason, the AHA limits the highpass cut-off frequency of such systems to 0.05 and 0.67 Hz for filters with phase and non-phase distortion respectively [4]. The noise sources and the signal processing techniques to remove the baseline wander were discussed in our previous work [5]. Hardware realisations of several methods to remove baseline wander based on digital signal processors (DSPs), microcontroller units (MCUs) and field-programmable gate arrays (FPGAs) have been reported in the literature [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…Typical ECG waveform showing key features and fiducial points (J1-3) used to track the baseline wander[5].…”

mentioning

confidence: 99%

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Low-power real-time ECG baseline wander removal: Hardware implementation

Güven

Eftekhar

Kindt

et al. 2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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This paper presents a hardware realisation of a novel ECG baseline drift removal that preserves the ECG signal integrity. The microcontroller implementation detects the fiducial markers of the ECG signal and the baseline wander estimation is achieved through a weighted piecewise linear interpolation. This estimated drift is then removed to recover a "clean" ECG signal without significantly distorting the ST segment. Experimental results using real data from the MIT-BIH Arrhythmia Database (recording 100 and 101) with added baseline wander (BWM1) from the MIT-BIH Noise Stress Database show an average root mean square error of 34.3 µV (mean), 30.4 µV (median) and 18.4 µV (standard deviation) per heart beat.

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Section: A Baseline Wander Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-power real-time ECG baseline wander removal: Hardware implementation

Güven

Eftekhar

Kindt

et al. 2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…Techniques exist to remove each of these noise sources; however, on occasions where signal integrity is crucial these methods do not meet clinical standards. As discussed in our previous work [ 7 ], baseline wander can be removed by detecting fiducial points and estimating the baseline wander by interpolating through those points with a piecewise cubic hermite interpolation (PCHIP). However, PCHIP is still somewhat complex; therefore, a new method is investigated where baseline wander estimation is accurately achieved with less computational hardware resources required.…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Güven

Eftekhar

Kindt

et al. 2016

Healthcare Technology Letters

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This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp–p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat.

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“…And finally, the method of Guven (Guven et al, 2014) presents a method for ECG baseline drift removal. For this, authors propose to detect onset and end of QRS complex and a point of T-P segment.…”

Section: Introductionmentioning

confidence: 99%

Improvement of a FPGA-based Detection of QRS Complexes in ECG Signal using an Adaptive Windowing Strategy

Habiboullah¹,

Terosiet²,

Histace³

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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This paper presents an FPGA-based algorithm for automatic detection of QRS complexes in ECG signals, first step for the estimation of cardiac intervals. The proposed algorithm is divided into 3 parts : Filtering, Contrast Enhancement, and finally a Detection block based on an adaptive windowing and a thresholding of the enhanced data. The entire detection scheme was developed in accordance with embedding constraints and in the perspective of a real-time use. We evaluated the algorithm on manually annotated databases, such MIT-BIH Arrythmia and QT databases. The FPGA-based algorithm correctly detects 91,85 % percent of the QRS complexes, with a very limited ratio of false detection (only 5%) on standard databases, while for realtime records obtained from young subjects between 20 and 25 years, the sensitivity reaches 93,77 % with a false detection ratio of only 4 %. These results are in accordance with the most recent state-of-the-art off-line algorithms on the same database, and improves significantly FPGA-based ones that were tested on a limited number of ECG extracted from the MIT-BIH set of data only.

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Realtime ECG baseline removal: An isoelectric point estimation approach

Cited by 10 publications

References 11 publications

Low-power real-time ECG baseline wander removal: Hardware implementation

Low-power real-time ECG baseline wander removal: Hardware implementation

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Improvement of a FPGA-based Detection of QRS Complexes in ECG Signal using an Adaptive Windowing Strategy

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