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Journal ArticleDOI

Adaptive Neuro-Fuzzy Inference System for antepartum antenatal care using phonocardiography

23 May 2012-International Journal of Biomedical Engineering and Technology (Inderscience Publishers)-Vol. 8, Iss: 4, pp 357
TL;DR: Development of a model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for evaluation of fetal health status using phonocardiography and results have indicated that the ANFIS can be implemented effectively and provides high accuracy for antepartum antenatal care through phonOCardiography.
Abstract: This work discusses development of a model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for evaluation of fetal health status using phonocardiography. The model integrates adaptable fuzzy inputs with a modular neural network to deal with the imprecision and uncertainty in the interpretation of the FHR data from phonocardiographic signals. A zero-order Takagi-Sugeno model is chosen for designing ANFIS architecture. The diagnostic parameters e.g., Baseline FHR, Baseline Variability, Acceleration and Deceleration of the FHR are derived from the fPCG signals for training and testing of the model. The elicited fuzzy rules derived from clinical guidelines and other resources are implemented into the ANFIS expert model. The performance of the ANFIS model is evaluated in terms of sensitivity and overall accuracy. The results have indicated that the ANFIS can be implemented effectively and provides high accuracy for antepartum antenatal care through phonocardiography.
Citations
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Journal ArticleDOI
TL;DR: The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio and can be used as first screening tool by the medical practitioners.
Abstract: In this paper, a non-invasive, portable and inexpensive antenatal care system is developed using fetal phonocardiography. The fPCG technique has the potential to provide low-cost and long-term diagnostics to the under-served population. The fPCG signal contains valuable diagnostic information regarding fetal health during antenatal period. The fPCG signals are acquired from the maternal abdominal surface using a wireless data acquisition and recording system. The diagnostic parameters e.g., baseline, variability, acceleration and deceleration of the fetal heart rate are derived from the fPCG signal. A model based on adaptive neuro-fuzzy inference system is developed for the evaluation of fetal health status. To study the performance of the developed system, experiments were carried out with real fPCG signals under the supervision of medical experts. Its performance is found to be in close proximity with the widely accepted Doppler ultrasound based fetal monitor results. The overall performance shows that the developed system has a long-term monitoring capability with very high performance to cost ratio. The system can be used as first screening tool by the medical practitioners.

30 citations


Cites methods from "Adaptive Neuro-Fuzzy Inference Syst..."

  • ...This present application considers the ANFIS structure with the zero-order Sugeno model containing 80 rules [67]....

    [...]


Proceedings ArticleDOI
03 Jul 2013-
TL;DR: An effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG).
Abstract: The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.

4 citations


Cites methods from "Adaptive Neuro-Fuzzy Inference Syst..."

  • ...In [13], an ANFIS model for evaluation of foetal health status using PCG signals was implemented effectively and provided high accuracy for antepartum antenatal care....

    [...]


References
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Journal ArticleDOI
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TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
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13,738 citations


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Performance
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No. of citations received by the Paper in previous years
YearCitations
20141
20131