Extraction of Fetal Electrocardiogram Using Adaptive Neuro-Fuzzy Inference Systems Khaled Assaleh, Senior Member,IEEE M97G0224 黃阡

OUTLINE INTRODUCTION INTRODUCTION PROBLEM FORMULATION PROBLEM FORMULATION ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS PROPOSED SOLUTION FOR FECG EXTRACTION PROPOSED SOLUTION FOR FECG EXTRACTION ECG DATA USED IN THIS STUDY ECG DATA USED IN THIS STUDY EXPERIMENTAL RESULTS EXPERIMENTAL RESULTS

INTRODUCTION The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart Technical problems Technical problems –Low power of the FECG signal which is contaminated by various sources of interference

INTRODUCTION (cont.) Contaminated sources Contaminated sources –Maternal ECG – –Maternal EMG – –50 Hz power line interference – –Baseline wander – –Random electronic noise

INTRODUCTION (cont.) Solution Solution –Using low noise electronic amplifiers with high common mode rejection ratio 50 Hz interference and electronic random noise can be eliminated EMG noise can also be reduced but not necessarily eliminated

INTRODUCTION (cont.) These techniques include These techniques include –Adaptive filters –Correlation techniques –Singular-value decomposition (SVD) –Wavelet transform –Neural networks –Blind source separation (BSS)

INTRODUCTION (cont.) BSS via independent component analysis (ICA) is considered among the most recent and most successful methods used for FECG extraction BSS via independent component analysis (ICA) is considered among the most recent and most successful methods used for FECG extraction ICA requires multiple leads for collecting several ECG signals ICA requires multiple leads for collecting several ECG signals –Not enough for satisfactory FECG extraction via ICA

INTRODUCTION (cont.) We aim to apply adaptive neuro-fuzzy inference systems (ANFIS) for estimating the FECG component from one abdominal ECG recording and one reference thoracic maternal ECG (MECG) signal We aim to apply adaptive neuro-fuzzy inference systems (ANFIS) for estimating the FECG component from one abdominal ECG recording and one reference thoracic maternal ECG (MECG) signal

PROBLEM FORMULATION Two Leads are attached to the body of a pregnant woman Two Leads are attached to the body of a pregnant woman These signals are denoted as and to correspond to the thoracic and abdominal ECG signals respectively These signals are denoted as and to correspond to the thoracic and abdominal ECG signals respectively

PROBLEM FORMULATION (cont.) Abdominal signal are three signals Abdominal signal are three signals –Deformed version of –Fetal ECG –Additive noise from other sources

PROBLEM FORMULATION (cont.)

The abdominal signal can be expressed as the sum of a deformed version of the maternal ECG and a noisy version of the fetal ECG such that The abdominal signal can be expressed as the sum of a deformed version of the maternal ECG and a noisy version of the fetal ECG such that

PROBLEM FORMULATION (cont.) Maternal ECG is measured far away from its source and consequently it encounters some nonlinear transformation as it travels to the abdominal area Maternal ECG is measured far away from its source and consequently it encounters some nonlinear transformation as it travels to the abdominal area

PROBLEM FORMULATION (cont.) The problem becomes trivial if the transformation was linear The problem becomes trivial if the transformation was linear In that case, can be aligned with via correlation and the signal can be extracted by simply subtracting the aligned from In that case, can be aligned with via correlation and the signal can be extracted by simply subtracting the aligned from

PROBLEM FORMULATION (cont.) In fact, the transformation between and the maternal component in, In fact, the transformation between and the maternal component in,

PROBLEM FORMULATION (cont.)

The thoracic signal is predominantly maternal, and hence we assume that the fetal component in it is negligible The thoracic signal is predominantly maternal, and hence we assume that the fetal component in it is negligible A proper placement of the thoracic and abdomen electrodes would result in a clean estimate of the FECG such that A proper placement of the thoracic and abdomen electrodes would result in a clean estimate of the FECG such that

PROBLEM FORMULATION (cont.) Our goal is to approximate the nonlinear transformation which will operate on and yield a signal Our goal is to approximate the nonlinear transformation which will operate on and yield a signal We do so by an ANFIS network with multi- input and a single output We do so by an ANFIS network with multi- input and a single output –Input is the MECG

PROBLEM FORMULATION (cont.) The ANFIS network will find a nonlinear transformation that operates on and aligns it with The ANFIS network will find a nonlinear transformation that operates on and aligns it with The right ANFIS network should, therefore, output an estimate of the maternal component in which we denoted by The right ANFIS network should, therefore, output an estimate of the maternal component in which we denoted by

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS Fuzzy Logic has been widely used in the design and enhancement of a vast number of applications Fuzzy Logic has been widely used in the design and enhancement of a vast number of applications

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) A. ANFIS Architecture A. ANFIS Architecture Rule1:if (x is A1) and (y is B1), then Rule1:if (x is A1) and (y is B1), then Rule2:if (x is A2) and (y is B2), then Rule2:if (x is A2) and (y is B2), then

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Layer1: Layer1: –i is the degree of the membership of the input to the fuzzy membership function (MF) represented by the node

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Ai and Bi can be any appropriate fuzzy sets in parameter form. For example, if bell MF is used then Ai and Bi can be any appropriate fuzzy sets in parameter form. For example, if bell MF is used then

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Layer2 Layer2 –These are labeled to indicate that they play the role of a simple multiplier.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Layer3: Layer3: –These are labeled N to indicate that these perform a normalization of the firing strength from previous layer.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Layer4: Layer4: –The output of each node is simply the product of the normalized firing strength and a first- order polynomial

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Layer5: Layer5: –This layer has only one node labeled to indicate that is performs the function of a simple summer.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) B. Learning Method of ANFIS B. Learning Method of ANFIS

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) For this observation, we can divide the parameter set into two sets such as For this observation, we can divide the parameter set into two sets such as set of total parameters set of total parameters set of premise (nonlinear) parameters set of premise (nonlinear) parameters set of consequent (linear) parameters set of consequent (linear) parameters direct sum direct sum

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Now for a given set of values of, we can plug training data and obtain a matrix equation Now for a given set of values of, we can plug training data and obtain a matrix equation Where contains the unknown parameters in

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) Solution for, which is minimizes, is the least square estimator Solution for, which is minimizes, is the least square estimator

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) For the backward path, the error signals propagate backward. The premise parameters are updated by descent method For the backward path, the error signals propagate backward. The premise parameters are updated by descent method

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS (cont.) The update of the parameters in the th node in layer L can be written as The update of the parameters in the th node in layer L can be written as

PROPOSED SOLUTION FOR FECG EXTRACTION To account for the possibility that the nonlinear transformation T might be time- variant, we structure our algorithm to be frame-based. To account for the possibility that the nonlinear transformation T might be time- variant, we structure our algorithm to be frame-based.

PROPOSED SOLUTION FOR FECG EXTRACTION (cont.) Training data is constructed from the thoracic and abdominal data frames such that Training data is constructed from the thoracic and abdominal data frames such that

PROPOSED SOLUTION FOR FECG EXTRACTION (cont.) Use of ANFIS for FECG extraction for frame i.

ECG DATA USED IN THIS STUDY A. Synthetic ECG Data Generation A. Synthetic ECG Data Generation –A model for generating the abdominal signal

ECG DATA USED IN THIS STUDY (cont.) The effect of the nonlinearity that the model imposes can be best shown if we plot a portion of one beat of the MECG, and its transformation into The effect of the nonlinearity that the model imposes can be best shown if we plot a portion of one beat of the MECG, and its transformation into

ECG DATA USED IN THIS STUDY (cont.) The MECG signal, is assumed to have a heartbeat rate of 60 beats/min and the FECG signal is assumed to have a heartbeat rate of 170 beats/min. The MECG signal, is assumed to have a heartbeat rate of 60 beats/min and the FECG signal is assumed to have a heartbeat rate of 170 beats/min.

ECG DATA USED IN THIS STUDY (cont.) B. Real ECG Data B. Real ECG Data

EXPERIMENTAL RESULTS A. Results On Synthetic ECG Data A. Results On Synthetic ECG Data FECG extraction from synthetic ECG

EXPERIMENTAL RESULTS (cont.) Effect of the fetal to maternal signal to noise ratio (fmSNR) on the quality of FECG extraction represented by the qSNR using three different FECG extraction techniques. Effect of the fetal to maternal signal to noise ratio (fmSNR) on the quality of FECG extraction represented by the qSNR using three different FECG extraction techniques.

EXPERIMENTAL RESULTS (cont.) B. Results on Real ECG Data B. Results on Real ECG Data FECG extraction from real ECG data

EXPERIMENTAL RESULTS (cont.) One frame (400 samples) of the abdominal signal and the extracted fetal component. One frame (400 samples) of the abdominal signal and the extracted fetal component.

EXPERIMENTAL RESULTS (cont.) (a) One frame of abdominal ECG with temporally overlapping maternal and fetal components, and (b) extracted FECG signal

EXPERIMENTAL RESULTS (cont.) Extracted FECG signals from the same data of Fig. 14 using the proposed ANFIS technique, the polynomial networks technique, and the NLMS technique as labeled on the figure.

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