We propose a novel system for the compression and analysis of Abdominal Fetal Electrocardiogram using Compressive Sensing (CS) and Independent Component Analysis (ICA) applied in the compressed domain and sparse representations in a specific dictionary [2].

Non-invasive fetal ECG (fECG) extraction from abdominal recordings is a difficult task since the fECG is unfortunately contaminated by the maternal ECG, maternal electromyogram (EMG), and noise [1].

Fig. 2 Compressed Measurements yS.

  Shape parameter s can discriminate mother’s or fetal

  Shift parameter p gives information about time location

Compressive Sensing theory [3] is based on the principle that a small number of random measurements (m) are sufficient to capture all the information in a signal having a sparse representation and enable its exact reconstruction.

  Compressive Sensing using sparse binary sensing matrix allows a low power implementation of the compression algorithm (one channel, column vectors)

  Reconstruction is based on l0 minimization:

  To process P channels, we use ICA in the compressed domain (see next): reconstruction of the independent components is done using a modified version of the SL0 reconstruction algorithm, introducing regularization for better immunity against noise.

  We propose to use a dictionary of Gaussian-like functions for sparsification and separation of mother’s and fetal beats. The dictionary is composed by three sub-dictionaries: Dm, Df, Dn, with different shape parameter s𝑖 in each one [4]. in each one [4].

Validation of the proposed compression and detection framework has been done on two datasets: set A and set B of the Physionet Challenge datasets*.

Fig. Reconstruction SNR versus CR obtained from 100 trials for simulated fECG signals.

Tab. 1 Results for set B of Physionet Challenge dataset. *Available online: http://www.physionet.org/challenge/2013/

Fig. 7 Effects of the number of non-zero entries in each column of the sensing matrix at CR=75%. Average PRD for the whole dataset A.

Fig. 8 Effects of CR on the average Sensitivity of the detection algorithm with sparse binary sensing matrices with d=2 for the whole dataset A.

Fig. 9 Comparison of average PRD when using a sparse binary sensing matrices with d=2 and Gaussian sensing matrices at different CR (λ-SL0 algorithm).

Method HRmes[bpm2]

RRmeas[ms] Real3me Compression Execu3onTime

[s]

ICA- Template Adaption 20.4 4.6 -

FUSE-smooth 29.6 4.7 1.8

ICA-TS-ICA 34 5.1 1.55

WienerFilter 124.8 14.4 4.8

TS-ICA 124.5 12.4 200

ProposedMethod-smoothed 136 17.2 0.64

ProposedMethod 188 24.5 0.64

ICA-ExtendedKalman 219 7.7 -

TSandPCA 205.7 23.1 -

Waveletbased 513.1 35.3 -

TSPCA 759.4 21.86 1.09

[1] G. D. Clifford et al., “Non-invasive fetal ECG analysis,” Physiol. Meas., vol. 35, no. 8, pp.1521, 2014

[2] G. Da Poian, R. Bernardini, R. Rinaldo, "Separation and Analysis of Fetal-ECG Signals from Compressed Sensed Abdominal ECG Recordings," accepted in IEEE Transactions on Biomedical Engineering.

[3] D.L. Donoho, ”Compressed sensing,” Information Theory, IEEE Transactions on 52.4 (2006): 1289-1306.

[4] G. Da Poian, R. Bernardini, R. Rinaldo, “ Sparse Representation for Fetal QRS Detection in Abdominal ECG Recordings,” in IEEE E-Health and Bioengineering Conference (EHB), 2015.

Our work proposes a novel framework for the compression of abdominal fECG recordings jointly with real time beat detection and classification. Results allow us to conclude that the proposed framework may be used for compression of abdominal fECG and to obtain real time information of the fetal heart rate, providing a suitable solution for low-power telemonitoring applications.

Regularization of the SL0 algorithm allows a robust detection and reconstruction of signals even in the presence of noise.

Use of a sparse sensing matrix with a small number of non-zero elements in each column allows a low power implementation of the scheme without performance loss with respect to the use of a more common Gaussian sensing matrix (when compressing 250 sample signal blocks to 62 samples, CR=75%, Fc=1 kHz, a sparse binary sensing matrix with d=2 only needs about 375 additions, while using a random Gaussian sensing matrix requires 15438 additions and 15500 multiplications.)

We propose to apply ICA directly in the compressed domain to extract the source components from the multi-channel abdominal fECG. With P channels, assume

Fig3. Independent Components in the Compressed Domain.

Source signalsMixing Matrix

•  Find Amix and yS with ICA from y •  From yS find s by exploiting sparsity •  For the reconstruction of the independent

components s we still exploit the signal sparsity using a dictionary of Gaussian like functions

•  Fetal beats detection based on the atoms activated during reconstruction

•  Recovery of original signals x from s using the Amix

Fig.4 Reconstructed Independent Components.

Compressive Sensing

Fetal QRS Detection !

Results !

References

Fig.6 Reconstructed signals (blu) and original signals (red).

Fig.5 Atoms in the Fetal and Maternal Dictionary activated during reconstruction.

Contacts PhD Student: Da Poian Giulia e-mail: dapoian.giulia@spes.uniud.it

Supervisor: Rinaldo Roberto e-mail: rinaldo@uniud.it

Energy Efficient Fetal ECG Telemonitoring Using Wearable Sensors

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Fig. 1 Original fECG.

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Fig.7 Detected fetal and maternal QRS complexes.

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