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Zhong W, Luo J, Du W. Deep learning with fetal ECG recognition. Physiol Meas 2023; 44:115006. [PMID: 37939396 DOI: 10.1088/1361-6579/ad0ab7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.
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Affiliation(s)
- Wei Zhong
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Jiahui Luo
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Wei Du
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
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2
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Tanasković I, Miljković N. A new algorithm for fetal heart rate detection: Fractional order calculus approach. Med Eng Phys 2023; 118:104007. [PMID: 37536830 DOI: 10.1016/j.medengphy.2023.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 05/23/2023] [Accepted: 06/15/2023] [Indexed: 08/05/2023]
Abstract
OBJECTIVES A new modified Pan-Tompkins' (mPT) method for fetal heart rate detection is presented. The mPT method is based on the hypothesis that optimal fractional order derivative and optimal window width of the moving average filter would enable efficient estimation of fetal heart rate from surface abdominal electrophysiological recordings with relatively low signal-to-noise ratios. METHODS The algorithm is tested on signals recorded from the abdomen of pregnant women available from the PhysioNet Computing in Cardiology Challenge database. Fetal heart rate detection is performed on 10-s long segments selected by the estimation of signal-to-noise ratios (the extravagance of the fetal QRS peak to its surroundings and to the whole signal; and the mean ratio of fetal and maternal QRS peaks) and on the manually selected segments. RESULTS The best results are obtained via criteria based on the extravagance of the fetal QRS peak to its surroundings that reached average sensitivity of 97%, positive predictive value of 97%, error rate of ∼3.5%, and F1 score of 97%. The obtained averaged optimal parameters for mPT are 0.51 for fractional order and 24.5 ms for the window width of the moving average filter. CONCLUSION Proposed mPT algorithm showed satisfactory performance for fetal heart rate detection. Further adaptations of the presented mPT method could be used for peak detection in noisy environments in biomedical signal analysis in general.
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Affiliation(s)
- Ilija Tanasković
- University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia; Institute for Artificial Intelligence R&D, Fruskogorska 1, 21000 Novi Sad, Serbia
| | - Nadica Miljković
- University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia; Faculty of Electrical Engineering, University of Ljubljana. Tržaška c. 25, 1000 Ljubljana, Slovenia.
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Baldazzi G, Sulas E, Vullings R, Urru M, Tumbarello R, Raffo L, Pani D. Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography. Front Bioeng Biotechnol 2023; 11:1059119. [PMID: 36923461 PMCID: PMC10009887 DOI: 10.3389/fbioe.2023.1059119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.
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Affiliation(s)
- Giulia Baldazzi
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Eleonora Sulas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Monica Urru
- Pediatric Cardiology and Congenital Heart Disease Unit, ARNAS G. Brotzu Hospital, Cagliari, Italy
| | - Roberto Tumbarello
- Pediatric Cardiology and Congenital Heart Disease Unit, ARNAS G. Brotzu Hospital, Cagliari, Italy
| | - Luigi Raffo
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Danilo Pani
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
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4
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Design and evaluation of an autonomic nerve monitoring system based on skin sympathetic nerve activity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103681] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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5
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Chowdhury S, Frasch MG, Lucchini M, Shuffrey LC, Sania A, Malette C, Odendaal HJ, Myers MM, Fifer WP, Pini N. A Novel Method for the Extraction of Fetal ECG Signals from Wearable Devices. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1319-1322. [PMID: 36085704 DOI: 10.1109/embc48229.2022.9870899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The role of fetal surveillance for the prediction and timely assessment of fetal distress is widely established. Fetal ECG (fECG) monitoring via wearable devices is a feasible solution for performing continuous monitoring of fetal wellbeing and it has seen a net increase in popularity in recent years. In this paper, we propose a novel adaptation of the Smart AdaptiVe Ecg Recognition (SAVER) algorithm for the detection of fECG in long-duration recordings acquired in clinical as well as unconventional settings. The methodology was trained and tested on 50 recordings of duration 1 hour ( 59.33 ±5.54 min) obtained using the Monica AN24 fetal monitor. We validated the performance against the automatic extraction performed by the Monica DK software. Our results show superior reliability of the proposed methodology in extracting fECG and associated estimates of fetal heart rate (fHR). Clinical relevance- The proposed methodology provides an efficient and reliable approach for the extraction of fECG signals acquired via wearable technologies, enabling continuous monitoring of fECG in and outside clinical settings.
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Zhang Y, Gu A, Xiao Z, Xing Y, Yang C, Li J, Liu C. Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording. BIOSENSORS 2022; 12:bios12070475. [PMID: 35884277 PMCID: PMC9313261 DOI: 10.3390/bios12070475] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/25/2022] [Accepted: 06/28/2022] [Indexed: 01/31/2023]
Abstract
Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.
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Affiliation(s)
- Yuwei Zhang
- The State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China;
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
| | - Aihua Gu
- The State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China;
- State Key Laboratory of Reproductive Medicine, Institute of Toxicology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Correspondence: (A.G.); (C.L.)
| | - Zhijun Xiao
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
| | - Yantao Xing
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
| | - Chenxi Yang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
| | - Jianqing Li
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
| | - Chengyu Liu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Z.X.); (Y.X.); (C.Y.); (J.L.)
- Correspondence: (A.G.); (C.L.)
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Xing Y, Zhang Y, Xiao Z, Yang C, Li J, Cui C, Wang J, Chen H, Li J, Liu C. An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal. BIOSENSORS 2022; 12:355. [PMID: 35624656 PMCID: PMC9138869 DOI: 10.3390/bios12050355] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/15/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager−Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests.
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Affiliation(s)
- Yantao Xing
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Yike Zhang
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Zhijun Xiao
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chenxi Yang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Jiayi Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chang Cui
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Jing Wang
- Division of Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China;
| | - Hongwu Chen
- Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210096, China; (Y.Z.); (C.C.); (H.C.)
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (Y.X.); (Z.X.); (C.Y.); (J.L.)
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8
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Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition. SENSORS 2022; 22:s22103705. [PMID: 35632114 PMCID: PMC9146186 DOI: 10.3390/s22103705] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 02/04/2023]
Abstract
Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.
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Jallouli M, Arfaoui S, Ben Mabrouk A, Cattani C. Clifford Wavelet Entropy for Fetal ECG Extraction. ENTROPY (BASEL, SWITZERLAND) 2021; 23:844. [PMID: 34209158 PMCID: PMC8305949 DOI: 10.3390/e23070844] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 12/05/2022]
Abstract
Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73-97, and Arfaoui, et al.Acta Appl. Math.2020, 170, 1-35.. Additionally, classical Haar-Faber-Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se=100%, PPV=100% for FECG extraction and peak detection.
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Affiliation(s)
- Malika Jallouli
- LATIS Laboratory of Advanced Technology and Intelligent Systems, Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, Sousse 4023, Tunisia;
| | - Sabrine Arfaoui
- Laboratory of Algebra, Number Theory and Nonlinear Analysis, Department of Mathematics, Faculty of Sciences, University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia; (S.A.); (A.B.M.)
- Department of Mathematics, Faculty of Sciences, University of Tabuk, Tabuk 47512, Saudi Arabia
| | - Anouar Ben Mabrouk
- Laboratory of Algebra, Number Theory and Nonlinear Analysis, Department of Mathematics, Faculty of Sciences, University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia; (S.A.); (A.B.M.)
- Department of Mathematics, Faculty of Sciences, University of Tabuk, Tabuk 47512, Saudi Arabia
- Department of Mathematics, Higher Institute of Applied Mathematics and Computer Science, University of Kairouan, Street of Assad Ibn Alfourat, Kairouan 3100, Tunisia
| | - Carlo Cattani
- Engineering School (DEIM), Tuscia University, Largo dell’Università, 01100 Viterbo, Italy
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Rasti-Meymandi A, Ghaffari A. AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model. Physiol Meas 2021; 42. [PMID: 33706298 DOI: 10.1088/1361-6579/abedc1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
Objective.The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus's health. However, the AECG is often affected by different noises and interferences, such as the maternal ECG (MECG), making it hard to evaluate the FECG signal. In this paper, we propose a deep-learning-based framework, namely 'AECG-DecompNet', to efficiently extract both MECG and FECG from a single-channel abdominal electrode recording.Approach.AECG-DecompNet is based on two series networks to decompose AECG, one for MECG estimation and the other to eliminate interference and noise. Both networks are based on an encoder-decoder architecture with internal and external skip connections to reconstruct the signals better.Main results.Experimental results show that the proposed framework performs much better than utilizing one network for direct FECG extraction. In addition, the comparison of the proposed framework with popular single-channel extraction techniques shows superior results in terms of QRS detection while indicating its ability to preserve morphological information. AECG-DecompNet achieves exceptional accuracy in theprecisionmetric (97.4%), higher accuracy inrecallandF1metrics (93.52% and 95.42% respectively), and outperforms other state-of-the-art approaches.Significance.The proposed method shows a notable performance in preserving the morphological information when the FECG within the AECG signal is weak.
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Affiliation(s)
- Arash Rasti-Meymandi
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Aboozar Ghaffari
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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Fu F, Xiang W, An Y, Liu B, Chen X, Zhu S, Li J. Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices. J Med Biol Eng 2021. [DOI: 10.1007/s40846-020-00588-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Abstract
Purpose
Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms.
Methods
To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input.
Results
The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively).
Conclusions
Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.
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Mollakazemi MJ, Asadi F, Tajnesaei M, Ghaffari A. Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation. J Biomed Phys Eng 2021; 11:197-204. [PMID: 33945588 PMCID: PMC8064132 DOI: 10.31661/jbpe.v0i0.397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 08/10/2015] [Indexed: 11/17/2022]
Abstract
Background: Fetal heart rate (FHR) extracted from abdominal electrocardiogram (ECG) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. Despite significant advances in the field of electrocardiography, the analysis of fetal ECG (FECG) signal is considered a challenging issue which is mainly due to low signal to noise ratio (SNR) of FECG. Objective: In this study, we present an approach for accurately locating the fetal QRS complexes in non-invasive FECG. Materials and Methods: In this experimental study, the proposed method included 4 steps. In step 1, comb notching filter was employed to pre-process the abdominal ECG (AECG). Furthermore, low frequency noises were omitted using wavelet decomposition. In next step, principal component analysis (PCA) and signal quality assessment (SQA) were used to obtain an optimal AECG reference channel for maternal R-peaks detection. In step 3, maternal ECG (MECG) was removed from mixture signal and FECG was extracted. In final step, the extracted FECG was first decomposed by discrete wavelet transforms at level 10. Then, by employing details of levels 2, 3, 4, the new FECG signal was reconstructed in which various noises and artifacts were removed and FECG components whose frequency were close to the fetal QRS complexes remained which increased the performance of the method. Results: For evaluation, 15 recordings of PhysioNet Noninvasive FECG database were used and the average F1 measure of 98.77% was obtained. Conclusion: The results indicate that use of both an efficient analysis of major component of AECG along with a signal quality assessment technique has a promising performance in FECG analysis.
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Affiliation(s)
- Mohammad Javad Mollakazemi
- PhD Candidate, Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farhad Asadi
- MSc, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mahsa Tajnesaei
- MSc, Department of Health Management and Economics, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Ghaffari
- PhD, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
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Fetal electrocardiography extraction with residual convolutional encoder-decoder networks. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:1081-1089. [PMID: 31617154 DOI: 10.1007/s13246-019-00805-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 09/30/2019] [Indexed: 12/31/2022]
Abstract
In the context of fetal monitoring, non-invasive fetal electrocardiography is an alternative approach to the traditional Doppler ultrasound technique. However, separating the fetal electrocardiography (FECG) component from the abdominal electrocardiography (AECG) remains a challenging task. This is mainly due to the interference from maternal electrocardiography, which has larger amplitude and overlaps with the FECG in both temporal and frequency domains. The main objective is to present a novel approach to FECG extraction by using a deep learning strategy from single-channel AECG recording. A residual convolutional encoder-decoder network (RCED-Net) is developed for this task of FECG extraction. The single-channel AECG recording is the input to the RCED-Net. And the RCED-Net extracts the feature of AECG and directly outputs the estimate of FECG component in the AECG recording. The AECG recordings from two different databases are collected to illustrate the efficiency of the proposed method. And the achieved results show that the proposed technique exhibits the best performance when compared to the existing methods in the literature. This work is a proof of concept that the proposed method could effectively extract the FECG component from AECG recordings. The focus on single-channel FECG extraction technique contributes to the commercial applications for long-term fetal monitoring.
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QRStree: A prefix tree-based model to fetal QRS complexes detection. PLoS One 2019; 14:e0223057. [PMID: 31574123 PMCID: PMC6772072 DOI: 10.1371/journal.pone.0223057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/12/2019] [Indexed: 11/23/2022] Open
Abstract
Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home.
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John RG, Ramachandran KI. Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:193-204. [PMID: 31104707 DOI: 10.1016/j.cmpb.2019.04.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/13/2019] [Accepted: 04/20/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE This paper proposes a simple yet effective method for the extraction of foetal ECG from abdominal ECG which is necessary due to similar spatial and temporal content of mother and foetal ECG. METHODS The proposed algorithm for extraction of foetal ECG (fECG) from abdominal signal uses single channel. Pre-processing of abdominal ECG (abdECG) has been done to eliminate noise and condition the signal. The maternal ECG R-peaks have been detected based on thresholding, first order Gaussian differentiation and zero cross detection on pre-processed signal. Having identified R-peaks and pre-processed signal as base, using Maximum Likelihood Estimation, one beat including QRS complex morphology of maternal ECG (mECG) has been constructed. Extraction of maternal ECG from abdECG is done based on the constructed beat, R-peak locations and its corresponding QRS complex of abdECG. Extracted mECG has been cancelled from abdECG. This results in foetal ECG with residual noise. The noise has been reduced by Polynomial Approximation and Total Variation (PATV) to improve SNR. This approach ensures no loss of partially or completely overlapped fECG signals due to mECG removal. The algorithm is tested on three database namely daISy (DBI), Physiobank challenge 2013 (DBII) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DBIII). RESULTS The algorithm detected no false positives or false negatives with certain channel for DBI, DBII and DBIII which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DBI. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DBII. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DBIII. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DBII and 0.59 for DBIII is obtained. The method has obtained overall F1 score of 99.25% for DBI, 96.04% for DBII and 94.25% for DBIII. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm2 and 2.2 ms for DBII, 12bpm2 and 2.14 ms for DBIII. CONCLUSION The results for different public databases show that the proposed method is capable of providing good results. The foetal QRS, R-peaks and R-R intervals have also been obtained in this method. Thus, it gives a significant contribution in the required area of research.
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Affiliation(s)
- Rolant Gini John
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
| | - K I Ramachandran
- Center for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India
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Gurve D, Krishnan S. Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization. IEEE J Biomed Health Inform 2019; 24:669-680. [PMID: 31170084 DOI: 10.1109/jbhi.2019.2920356] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Performing a fetal electrocardiogram (ECG) analysis, which contains important information about the status of a fetal, can help to detect fetus health even before birth. Since the fetal ECG extracted from the ECG signal recorded from the mother's abdomen, this extraction problem can be seen as a source separation problem, of recovering source signals from signal mixtures. In this paper, a method for separation of fetal ECG from abdominal ECG using activation scaled non-negative matrix factorization (NMF) is proposed. The performance of the proposed method is also compared with independent component analysis. The proposed method is tested under three different scenarios. First, the original abdominal ECG signal is used for fetal separation. Second, the recovered abdominal ECG after compression is used for separation. Third, the fetal ECG is extracted from the compressed domain of the abdominal ECG. We applied scaling on the activation matrix obtained using NMF for emphasizing the fetal ECG present in abdominal ECG. The improved-regularized least-squares [Formula: see text] algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on real abdominal recordings obtained from two different datasets from Physionet. The first dataset used for this paper is Silesia dataset for abdominal and direct f-ECG, and the second dataset we considered is Set-A of the Physionet challenge. The obtained outcomes reveal that it is possible to separate fetal ECG from single-channel abdominal ECG signal, which can help us to achieve energy-efficient transmission, and cost-effective fetal ECG remote monitoring for Internet-of-Things applications, where device battery and computational capacity are limited.
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An Improved Sliding Window Area Method for T Wave Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:3130527. [PMID: 31065291 PMCID: PMC6466942 DOI: 10.1155/2019/3130527] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 03/05/2019] [Indexed: 11/29/2022]
Abstract
Background The T wave represents ECG repolarization, whose detection is required during myocardial ischemia, and the first significant change in the ECG signal is being observed in the ST segment followed by changes in other waves like P wave and QRS complex. To offer guidance in clinical diagnosis, decision-making, and daily mobile ECG monitoring, the T wave needs to be detected firstly. Recently, the sliding area-based method has received an increasing amount of attention due to its robustness and low computational burden. However, the parameter setting of the search window's boundaries in this method is not adaptive. Therefore, in this study, we proposed an improved sliding window area method with more adaptive parameter setting for T wave detection. Methods Firstly, k-means clustering was used in the annotated MIT QT database to generate three piecewise functions for delineating the relationship between the RR interval and the interval from the R peak to the T wave onset and that between the RR interval and the interval from the R peak to the T wave offset. Then, the grid search technique combined with 5-fold cross validation was used to select the suitable parameters' combination for the sliding window area method. Results With respect to onset detection in the QT database, F1 improved from 54.70% to 70.46% and 54.05% to 72.94% for the first and second electrocardiogram (ECG) channels, respectively. For offset detection, F1 also improved in both channels as it did in the European ST-T database. Conclusions F1 results from the improved algorithm version were higher than those from the traditional method, indicating a potentially useful application for the proposed method in ECG monitoring.
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Zhong W, Liao L, Guo X, Wang G. A deep learning approach for fetal QRS complex detection. Physiol Meas 2018; 39:045004. [DOI: 10.1088/1361-6579/aab297] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Patient-Specific Deep Architectural Model for ECG Classification. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:4108720. [PMID: 29065597 PMCID: PMC5499251 DOI: 10.1155/2017/4108720] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Revised: 02/02/2017] [Accepted: 02/16/2017] [Indexed: 11/18/2022]
Abstract
Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.
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Zhang Y, Wei S, Di Maria C, Liu C. Using Lempel-Ziv Complexity to Assess ECG Signal Quality. J Med Biol Eng 2016; 36:625-634. [PMID: 27853413 PMCID: PMC5083778 DOI: 10.1007/s40846-016-0165-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 02/02/2016] [Indexed: 11/30/2022]
Abstract
The poor quality of wireless electrocardiography (ECG) recordings can lead to misdiagnosis and waste of medical resources. This study presents an interpretation of Lempel–Ziv (LZ) complexity in terms of ECG quality assessment, and verifies its performance on real ECG signals. Firstly, LZ complexities for typical signals, namely high-frequency (HF) noise, low-frequency (LF) noise, power-line (PL) noise, impulse (IM) noise, clean artificial ECG signals, and ECG signals with various types of noise added (ECG plus HF, LF, PL, and IM noise, respectively) were analyzed. Then, the effects of noise, signal length, and signal-to-noise ratio (SNR) on the LZ complexity of ECG signals were analyzed. The simulation results show that LZ complexity for HF noise was obviously different from those for PL and LF noise. The LZ value can be used to determine the presence of HF noise. ECG plus HF noise had the highest LZ values. Other types of noise had low LZ values. Signal lengths of over 40 s had only a small effect on LZ values. The LZ values for ECG plus all types of noise increased monotonically with decreasing SNR except for LF and PL noise. For the test of real ECG signals plus three types of noise, namely muscle artefacts (MAs), baseline wander (BW), and electrode motion (EM) artefacts, LZ complexity varied obviously with increasing MA but not for BW and EM noise. This study demonstrates that LZ complexity is sensitive to noise level (especially for HF noise) and can thus be a valuable reference index for the assessment of ECG signal quality.
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Affiliation(s)
- Yatao Zhang
- School of Control Science and Engineering, Shandong University, Jinan, 250061 People's Republic of China ; School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, 264209 People's Republic of China
| | - Shoushui Wei
- School of Control Science and Engineering, Shandong University, Jinan, 250061 People's Republic of China
| | - Costanzo Di Maria
- Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 4LP UK ; Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne, NE7 7DN UK
| | - Chengyu Liu
- School of Control Science and Engineering, Shandong University, Jinan, 250061 People's Republic of China ; Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 4LP UK
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Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiol Meas 2016; 37:1298-312. [PMID: 27454710 DOI: 10.1088/0967-3334/37/8/1298] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
False alarm (FA) rates as high as 86% have been reported in intensive care unit monitors. High FA rates decrease quality of care by slowing staff response times while increasing patient burdens and stresses. In this study, we proposed a rule-based and multi-channel information fusion method for accurately classifying the true or false alarms for five life-threatening arrhythmias: asystole (ASY), extreme bradycardia (EBR), extreme tachycardia (ETC), ventricular tachycardia (VTA) and ventricular flutter/fibrillation (VFB). The proposed method consisted of five steps: (1) signal pre-processing, (2) feature detection and validation, (3) true/false alarm determination for each channel, (4) 'real-time' true/false alarm determination and (5) 'retrospective' true/false alarm determination (if needed). Up to four signal channels, that is, two electrocardiogram signals, one arterial blood pressure and/or one photoplethysmogram signal were included in the analysis. Two events were set for the method validation: event 1 for 'real-time' and event 2 for 'retrospective' alarm classification. The results showed that 100% true positive ratio (i.e. sensitivity) on the training set were obtained for ASY, EBR, ETC and VFB types, and 94% for VTA type, accompanied by the corresponding true negative ratio (i.e. specificity) results of 93%, 81%, 78%, 85% and 50% respectively, resulting in the score values of 96.50, 90.70, 88.89, 92.31 and 64.90, as well as with a final score of 80.57 for event 1 and 79.12 for event 2. For the test set, the proposed method obtained the score of 88.73 for ASY, 77.78 for EBR, 89.92 for ETC, 67.74 for VFB and 61.04 for VTA types, with the final score of 71.68 for event 1 and 75.91 for event 2.
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Affiliation(s)
- Chengyu Liu
- Departments of Biomedical Informatics, Emory University, Atlanta, GA, USA. School of Control Science and Engineering, Shandong University, Jinan, People's Republic of China
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Behar J, Andreotti F, Zaunseder S, Oster J, Clifford GD. A practical guide to non-invasive foetal electrocardiogram extraction and analysis. Physiol Meas 2016; 37:R1-R35. [DOI: 10.1088/0967-3334/37/5/r1] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. ENTROPY 2015. [DOI: 10.3390/e17096270] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms. ENTROPY 2015. [DOI: 10.3390/e17096179] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Yan H, Liu H, Huang X, Zhao Y, Si J, Liu T. Invariant heart beat span versus variant heart beat intervals and its application to fetal ECG extraction. Biomed Eng Online 2014; 13:163. [PMID: 25494711 PMCID: PMC4320593 DOI: 10.1186/1475-925x-13-163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 12/05/2014] [Indexed: 11/11/2022] Open
Abstract
Background The fundamental assumptions for various kinds of fetal electrocardiogram (fECG) extraction methods are not consistent with each other, which is a very important problem needed to be ascertained. Methods Based on two public databases, the regularity on ECG wave durations for normal sinus rhythm is investigated statistically. Taking the ascertained regularity as an assumption, a new fECG extraction algorithm is proposed, called Partial R-R interval Resampling (PRR). Results Both synthetic and real abdominal ECG signals are used to test the algorithm. The results indicate that the PRR algorithm has better performance over the whole R-R interval resampling based comb filtering method (RR) and linear template method (LP), which takes advantages of both LP and RR. Conclusions The final drawn conclusion is: (1) the proposition should be true that the individual’s heart beat span is invariable for normal sinus rhythm; (2) the proposed PRR fetal ECG extraction algorithm can estimate the maternal ECG (mECG) more accurately and stably even in the condition of large HRV, finally resulting in better fetal ECG extraction.
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Affiliation(s)
| | - Hongxing Liu
- School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China.
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Abstract
Despite the important advances achieved in the field of adult electrocardiography signal processing, the analysis of the non-invasive fetal electrocardiogram (NI-FECG) remains a challenge. Currently no gold standard database exists which provides labelled FECG QRS complexes (and other morphological parameters), and publications rely either on proprietary databases or a very limited set of data recorded from few (or more often, just one) individuals.The PhysioNet/Computing in Cardiology Challenge 2013 enables to tackle some of these limitations by releasing a set of NI-FECG data publicly to the scientific community in order to evaluate signal processing techniques for NI-FECG extraction. The Challenge aim was to encourage development of accurate algorithms for locating QRS complexes and estimating the QT interval in non-invasive FECG signals. Using carefully reviewed reference QRS annotations and QT intervals as a gold standard, based on simultaneous direct FECG when possible, the Challenge was designed to measure and compare the performance of participants' algorithms objectively. Multiple challenge events were designed to test basic FHR estimation accuracy, as well as accuracy in measurement of inter-beat (RR) and QT intervals needed as a basis for derivation of other FECG features.This editorial reviews the background issues, the design of the Challenge, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement.
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Affiliation(s)
- Gari D Clifford
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK. Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA. Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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