Curvature based ECG signal compression for effective communication on WPAN

Kim, Se-Yun; Kim, Jeong-Hong; Yun, Byoung-Ju; Park, Kil−Houm

doi:10.1109/jcn.2012.6184547

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…where O(t) is the ECG original signal, t is a time variable, s(t) is a sample index at t and v(t) is a signal voltage. We converted O(t) into the CSS by a Gaussian low pass filter and got the smoothed ECG signal C(t, σ), defined by the following equation [7].…”

Section: The Curvature Scale-space Techniquementioning

confidence: 99%

“…Figure 2(a) shows an example of original ECG signal. The feature points including the peak of R wave and T wave, the start and the end of ST-segment were located by using the vertex selection method [7]. The start of ST-segment, also called J-point, is very important in locating the ST-segment and detecting the ST level.…”

Section: The Curvature Scale-space Techniquementioning

confidence: 99%

“…As shown in Figure 2(b), the extreme point corresponding to the end of ST segment is not obvious. We located it by a supplemental vertex selection method [7].…”

Section: Feature Extraction Of St Segmentmentioning

confidence: 99%

“…However, most of these algorithms focused on the detection of the ST level deviation and only a few methods directly classify the ST shape. The curvature analysis has been widely used in locating the end of T-wave [6] and finding characteristic corner points of ECG [7]. The point of peak curvature can not only distinguish whether the current ST segment is straight line or curve, but also identify concave and convex.…”

Section: Introductionmentioning

confidence: 99%

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A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space

Hu¹,

Li²,

Zhang³

et al. 2015

JBM

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Anomalous changes in the ST segment, including ST level deviation and ST shape change, are the major parameters in clinical electrocardiogram (ECG) diagnosis of myocardial ischemia. Automatic detection of ST segment morphology can provide a more accurate evidence for clinical diagnosis of myocardial ischemia. In this paper, we proposed a method for classifying the shape of the ST-segment based on the curvature scale space (CSS) technique. First, we established a reference ST set and preprocessed the ECG signal by using the CSS technique. Then, the corner points in the ST-segment were detected at a high scale of the CSS and tracked through multiple lower scales, in order to improve its localization. Finally, the current beat of ST morphology can be distinguished by the corner points. We applied the developed algorithm to the ECG recordings in European ST-T database and QT database to validate the accuracy of the algorithm. The experimental results showed that the average detection accuracy of our algorithm was 91.60%. We could conclude that the proposed method is able to provide a new way for the automatic detection of myocardial ischemia.

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Section: The Curvature Scale-space Techniquementioning

confidence: 99%

Section: The Curvature Scale-space Techniquementioning

confidence: 99%

“…As shown in Figure 2(b), the extreme point corresponding to the end of ST segment is not obvious. We located it by a supplemental vertex selection method [7].…”

Section: Feature Extraction Of St Segmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space

Hu¹,

Li²,

Zhang³

et al. 2015

JBM

View full text Add to dashboard Cite

show abstract

“…These methods are primarily based on linear prediction and long-term prediction method (Nave and Cohen, 1993). These methods include feature extraction using Hilbert transform (Bolton and Westphal, 1985), curvature-based ECG signal compression (Kim et al, 2012) etc. Lai et al (2013) proposed an algorithm which is based on normalisation, DCT-IV calculation, amplitude and sign bit separation, hybrid differential computation, non-uniform quantisation and entropy coding.…”

Section: Introductionmentioning

confidence: 99%

ECG data compression algorithm for tele-monitoring of cardiac patients

Jha¹,

Kolekar²

2017

IJTMCP

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This paper reports an efficient electrocardiogram (ECG) data compression algorithm for tele-monitoring of cardiac patients from rural area, based on combination of two encoding techniques with discrete cosine transform. The proposed technique provides good compression ratio (CR) with low percent root-mean-square difference (PRD) values. For performance evaluation of the proposed algorithm 48 records of ECG signals are taken from MIT-BIH arrhythmia database. Each record of ECG signal is of duration 1 minute and sampled at sampling frequency of 360 Hz. Noise of the ECG signal has been removed using Savitzky-Golay filter. To transform the signal from time domain to frequency domain, discrete cosine transform has been used which compacts energy of the signal to lower order of frequency coefficients. After normalisation and rounding of transform coefficients, signals are encoded using dual encoding technique which consists of run length encoding and Huffman encoding. The dual encoding technique compresses data significantly without any loss of information. The proposed algorithm offers average values of CR, PRD, quality score, percent root mean square difference normalised, RMS error and SNR of 11.49, 3.43, 3.82, 5.51, 0.012 and 60.11 dB respectively.

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Biometric template extraction from a heartbeat signal captured from fingers

Islam

Alajlan

2016

Multimed Tools Appl

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Curvature based ECG signal compression for effective communication on WPAN

Cited by 15 publications

References 14 publications

A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space

A Morphological Classification Method of ECG ST-Segment Based on Curvature Scale Space

ECG data compression algorithm for tele-monitoring of cardiac patients

Biometric template extraction from a heartbeat signal captured from fingers

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