A low-complexity data-adaptive approach for premature ventricular contraction recognition

Li, Peng; Liu, Chengyu; Wang, Xinpei; Zheng, Dingchang; Li, Yuanyang; Liu, Changchun

doi:10.1007/s11760-013-0478-6

Cited by 57 publications

(46 citation statements)

References 21 publications

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“…Measures OA PVC Non-PVC Se +P Se +P Manu [9] 99.3 97.8 99.5 ----------------Javadi [11] 96.0 92.3 -------98.0 --------Laurent [12] 95.2 82.6 93.4 --------------Bazi [14] 96.7 97.3 96.6 --------------Li [16] 98. Table 3 displays the experimental results.…”

Section: Methodsmentioning

confidence: 99%

“…The results indicated that, using Lyapunov exponents, PVCs could be easily classified and differentiated from normal ECG beats and other arrhythmias. Peng Li developed a low complexity data-adaptive PVC recognition approach that exhibited good robustness against noise, generalization capabilities, and a PVC recognition accuracy of 98.2%, indicating that it could be effectively used for real-time applications [16]. Using these algorithms, the features of ECGs were manually extracted based on time domain information, such as ECG morphology [6,7,11,12], and transform domain information [4,5,9,[12][13][14], such as the wavelet transforms or statistical parameters [10,15,16].…”

Section: Introductionmentioning

confidence: 99%

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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Abstract. Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang

Bai

et al. 2015

BME

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“…Figure 1 gives a demonstration of the synchronously recorded ECG and PPG signals. First, the slow varying components (0-0.05 Hz) were removed from the ECG and PPG signals; second, R-wave peaks of ECG signals were extracted by a template-matching procedure [26]. Ectopic beats were identified and excluded using our previously developed method [27].…”

Section: Protocolmentioning

confidence: 99%

Effect of a Percutaneous Coronary Intervention Procedure on Heart Rate Variability and Pulse Transit Time Variability: A Comparison Study Based on Fuzzy Measure Entropy

Zhang

Liu

et al. 2016

Entropy

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Percutaneous coronary intervention (PCI) is a common treatment method for patients with coronary artery disease (CAD), but its effect on synchronously measured heart rate variability (HRV) and pulse transit time variability (PTTV) have not been well established. This study aimed to verify whether PCI for CAD patients affects both HRV and PTTV parameters. Sixteen CAD patients were enrolled. Two five-minute ECG and finger photoplethysmography (PPG) signals were recorded, one within 24 h before PCI and another within 24 h after PCI. The changes of RR and pulse transit time (PTT) intervals due to the PCI procedure were first compared. Then, HRV and PTTV were evaluated by a standard short-term time-domain variability index of standard deviation of time series (SDTS) and our previously developed entropy-based index of fuzzy measure entropy (FuzzyMEn). To test the effect of different time series length on HRV and PTTV results, we segmented the RR and PTT time series using four time windows of 200, 100, 50 and 25 beats respectively. The PCI-induced changes in HRV and PTTV, as well as in RR and PTT intervals, are different. PCI procedure significantly decreased RR intervals (before PCI 973˘85 vs. after PCI 907˘100 ms, p < 0.05) while significantly increasing PTT intervals (207˘18 vs. 214˘19 ms, p < 0.01). For HRV, SDTS-only output significant lower values after PCI when time windows are 100 and 25 beats while presenting no significant decreases for other two time windows. By contrast, FuzzyMEn gave significant lower values after PCI for all four time windows (all p < 0.05). For PTTV, SDTS hardly changed after PCI at any time window (all p > 0.90) whereas FuzzyMEn still reported significant lower values (p < 0.05 for 25 beats time window and p < 0.01 for other three time windows). For both HRV and PTTV, with the increase of time window values, SDTS decreased while FuzzyMEn increased. This pilot study demonstrated that the RR interval decreased whereas the PTT interval increased after the PCI procedure and that there were significant reductions in both HRV and PTTV immediately after PCI using the FuzzyMEn method, indicating the changes in underlying mechanisms in cardiovascular system.

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“…The fuzzy logic classifier provided sensitivity, specificity and accuracy of 100%, 76% and 88% respectively. Other techniques have been applied to classify AF rhythm including nonlinear complexity measures, wavelet transform and artificial neural networks such as described in [12][13][14][15] However, these techniques may not always be technically feasible for real-time processing of ECG data due to the requirement of high computational resources [16].…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Analysis for Real Time Patient Monitoring

Aliami

Stranieri

Marzbanrad

et al. 2017

Computing in Cardiology Conference (CinC)

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A low-complexity data-adaptive approach for premature ventricular contraction recognition

Cited by 57 publications

References 21 publications

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Effect of a Percutaneous Coronary Intervention Procedure on Heart Rate Variability and Pulse Transit Time Variability: A Comparison Study Based on Fuzzy Measure Entropy

Atrial Fibrillation Analysis for Real Time Patient Monitoring

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