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«Detection of coronary artery disease with an electronic stethoscope Schmidt, Samuel Publication date: Document Version Publisher's PDF, also known as ...»

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1.5. Scope of the current thesis The aim of the current thesis is to develop an algorithm for detection of CAD with an

electronic stethoscope. This includes the following challenges:

 Development of a segmentation method for automatic identification of the diastolic periods without a reference signal.

 Handling the limitations of the stethoscope such as short recording duration and that the stethoscope is handheld.

 Identification of features for detection of CAD. Based on the study of prior art this includes analyses of the reported increases in high frequency energy in CAD subjects, analysis of methods for nonlinear dynamics, analyses of nonstationary method such as Hilbert Huang Transform and exploration of new feature types.

1.6. The electronic stethoscope as data collector

The advantage of the electronic stethoscope for detection of CAD is that the stethoscope is device which is familiar to medical personnel, it is fast to use and it is a low cost device. Therefore, a digital stethoscope dedicated to diagnosis of CAD is suited for use in clinics with a limited budget for equipment, such as the office of the general practitioner.

That the stethoscope is handheld implies several challenges. Because the stethoscope is handheld the potential recording time is limited since the user will be crossing the comfort zone of the patient. A second problem with the handheld stethoscope is that friction spikes occur in recordings. The friction spikes are caused by friction between the stethoscope diaphragm and the skin because it is impossible to hold the stethoscope completely still.

1.6.1. The Littmann 3M E4000 stethoscope The Littmann 3M E4000 stethoscope was chosen for the current studies due to its recording and storage capabilities. The stethoscope stores six recordings of 8 seconds and recordings can be transferred to a PC through an infrared data transmission. The signal resolution is 16 bit and the sample rate was 4000 sps. The sensor principle in the E4000 stethoscope is an air coupled microphone.

Figure 15. The Littmann 3M E4000 electronic stethoscope Due to the powerful S1 and S2 sounds and the weak systolic and diastolic sounds the dynamic range of heart sounds is large.

Therefore, the manufacture emphasized the high frequencies by a 1st order high pass filter with a cutoff frequency at 2000 Hz before digitalization. To remove this pre-emphasizing the recorded signals were subsequently filtered with a 1st order low pass filter with a cutoff frequency at 10 Hz to compensate for the 20 dB/decade slope introduced by the 1 st order high pass filter.

Figure 16 shows the average power spectrums of the diastole, systole, S1 and S2 from a normal subject before and after removal of the pre-emphasizing effect.

Power spectrum before removal of pre-emphazing Power spectrum after removal of pre-emphazing

–  –  –

Figure 16. Example of power spectrum densities from a normal subject (Subject nr.

153) before and after removal of pre-emphasizing.

The spectra in Figure 16 show that the diastolic heart sounds roll off as the frequency increases. At approximately 500-1000 Hz a plateau occurs which corresponds to the noise floor of the stethoscope. Therefore, a reasonable signal to noise ratio can be expected below 400-500 Hz.

1.7. Preliminary study

To test the potential of the stethoscope as a tool for detecting CAD an initial study was conducted. A multivariate classifier was trained using 39 subjects and tested in 59 subjects [98](Appendix 1 DaCRA Abstract). Only recordings with a low noise level were included in the preliminary study. The diastolic segments were filtered with a 200-800 Hz band-pass filter before two features were estimated: a power ratio between the 160 -350Hz band and the 350-750 Hz band and the pole magnitude of the 1 st pole in an AR-model. The features were combined using a linear discriminant function. In the test data the sensitivity was 89% and specificity was 54%. The results indicated that the electronic stethoscope was capable of detecting differences between diastolic sound from CAD and non CAD subjects.

1.8. Introduction to the studies

The thesis is based on 5 studies:

Study 1: Segmentation of heart sound recordings by a duration-dependent hidden Markov model [99].

The first study describes a method for automatic segmentation of the heart sound recordings. The focus of the study is to develop a method which is robust and useful for segmentation of heart sounds recorded in clinical settings.

Study 2: A framework for extraction of features for detection of CAD using an electronic stethoscope [62].

The focus of the second study was to develop a framework for extraction of features from the diastolic periods. The goal was to develop methods which allowed robust estimation of features even when the recording was contaminated by friction spikes.

Study 3: No evidence of nonlinear or chaotic behavior of cardiovascular murmurs [100].

The purpose of the study 3 was to examine whether cardiovascular murmurs shows nonlinear or chaotic characteristics. Therefore, murmurs from the carotid artery were analyzed to test the hypothesis that cardiovascular murmurs are different from a linear stochastic process. Murmurs from the carotid artery were analyzed because they are powerful which ensures a good signal to noise ratio.

Study 4: Noise and the detection of coronary artery disease with an electronic stethoscope [101].

The recordings obtained for the studies were taken in a clinical environment at Aalborg hospital and the recordings were often contaminated with ambient noise. Study 4 analyzes the influence of different noise types such as ambient noise, recording noise, respiration noise and abdominal noise.

Study 5: Acoustic features for the identification of coronary artery disease.

In study 5 a wide range of features was estimated from different frequency bands. The large number of features was validated using cross validation. The goal was to identify new features and compare the known features.


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