The sound of the heart can provide useful information for heart diagnosis. Cardiologists can for example detect heart murmurs or the localization of the heart damage by listening to the heart sound. Therefore, the inclusion of the analysis of heart sounds is becoming more and more popular when monitoring the well-being of the fetus. However, frequency overlapping of the fetal heart sound with inter-nal interference signals such as the maternal heart sound occurs.

The aim of this thesis is to develop a statistical “tool” and to test whether it is feasible to achieve a clear separation of the two dif-ferent heart signals using the Dictaphone (Model: Olympus VN- 8600PC) and two stereo stethoscopes with integrated micropho-nes (Model: omni solder pad 6mm, Stock nr. 724-3131, brand: RS).Furthermore, it is of particular interest, how accurate the obtained results are, when using the FastICA algorithm by Hyvärinen.

Materials & MethodsIn this section a short introduction to the measurement set-up and the methods used in this feasibility study is given.In Fig.1 the components of the measu-rement chain can be seen: test subject, two stethoscopes, two microphones and a Dictaphone.Real-time recordings of the maternal and fetal heart sounds from 8 probands bet-ween the 25th and 38th gestational week are used for the analysis in MATLAB.To achieve a clear separation of the two heart sounds Independent Component Analysis (ICA) is performed with the Fa-stICA algorithm by Hyvärinen et al. As two stethoscopes are provided a multi -channel Independent Component Analy-sis (ICA) is performed.

Independent Component Analysis (ICA)The ICA is based on the idea that a sepa-ration of a mixed signal can be achieved, when the original sources are independent and have non-gaussian distribution.

Several statistical methods (envelope de-tection (see Fig.2), Fast Fourier Transform (FFT) (see Fig.3), autocorrelation, corre-lation and Wavelet Transform) are used to segment and analyze the outcome of ICA.

DiscussionThe detection of the fetal heart sounds could not be assumed in all probands. Only in one proband out of 8 this assump-tion could be made. The analysis of the features of the probands shows that the fat tissue on the abdomen of the proband is probably one main feature that influen-ces the detection of the fetal heart sounds. Additionally, the postioning of the stethos-copes might represent another important factor with regard to the detection of the fetal heart sounds and should be investi-gated in more detail.

Other aspects, which could be improved in further studies with the aim of separa-ting the fetal and maternal heart sounds with a low-cost phonocardiogram device, are as follows:* inclusion of more probands to enable to design an own mother wavelet* recording of addtional bio-signals such as fECG or ultrasound Dopler as a refe- rence signal

ResultsThe main finding of this work is that the-re are indications for the detection of fetal heart sounds with the low-cost phonocar-diogram device; distinctive peaks were observed in the relevant frequency bands of the FFT-analysis (see Fig.3). However, the results also show that the signals are still contaminated with a lot of noise and overlapping with maternal heart sounds might still exist.

Separation between fetal and maternal heart sound Jean-Michelle ENTENA - University of Applied Sciences

Supervisors: FH-Prof. DI Dr. Lars MEHNEN, FH-Prof. DI Dr. Johannes MARTINEK

Introduction

ReferencesA. Hyvärinen et al. Independent Component Analysis: Algorithms and Applications. Neural Networks, 13(4-5):411-430, 2000.

Fig.1: Measurement chain: test subject, two stethoscopes, integrated microphones, Dictaphone

Fig.2: Extraction of envelope curves from proband4; title - number of channel, x-axis - time in sec, y-axis - amplitude.The purpose of the envelope detection was to eliminate the strong variances of the audio signals and to capture just the shape of the signals recorded.

Fig.3: Extraction of FFT-results of envelope curves from proband4; title- number of channel, x-axis - frequency in Hz, y-axis - amplitude in power. The relevant fre-quency band lies approx. between 0.5 and 3.5Hz. The frequency 0 is excluded in the graphs as noise accumulates at this frequency.