6533b82efe1ef96bd12923c3

RESEARCH PRODUCT

Analysis and recognition of vibratory signals : contribution to the treatment and analysis of cardiac signals for telemedecine

subject

description

The heart is a muscle. Its mechanical operation is like a pump charged for distributing and retrieving the blood in the lungs and cardiovascular system. Its electrical operation is regulated by the sinus node, a pacemaker or electric regulator responsible for triggering the natural heart beats that punctuate the functioning of the body.Doctors monitor the electromechanical functioning of the heart by recording an electrical signal called an electrocardiogram (ECG) or an audible signal : the phonocardiogram (PCG). The analysis and processing of these two signals are essential for diagnosis, to help detect anomalies and cardiac pathologies.The objective of this thesis is to develop signal processing tools on ECG and PCG to assist cardiologist in his analysis of these signals. The basic idea is to develop algorithms of low complexity and having inexpensive computing time. The primary interest is to ensure their easy implementation in a mobile heart monitoring system for use by the doctor or the patient. The second advantage lies in the possibility of automatic real-time analysis of signals with the mobile device, allowing control of the transmission of these signals to a removal of doubt.Numerous studies have led to significant advances in the analysis of ECG signals and the automatic recognition of cardiac conditions. Databases of real or synthetic signals annotated also assess the performance of new methods. PCG signals are much less studied, difficult to analyze and to interpret. The main methods (Fourier, wavelet and Wigner Ville) were tested : they do not allow automatic recognition of signatures, and an accurate understanding of their contents.Wavelet Transform (WT) on cardiac signals showed its effectiveness to filter and locate useful information, but it involves an external processing function (mother wavelet) whose the choice depends on the prior knowledge on the signal to be processed. This is not always suitable for cardiac signals. Moreover, the wavelet transform generally induces inaccuracies in the location due to the external function and optionally due to the sub- sampling of the signatures.The non-stationary nature of the ECG and PCG and their sensitivity to noise makes it difficult to separate an informative transition of a transition due to measurement noise. The choice of treatment tool should allow denoising and analysis of these signals without alteration or the processing tool delocalization of the singularities.In response to our objectives and considering these problems, we propose to rely primarily on empirical mode decomposition (EMD) and Hilbert Huang Transform (HHT) to develop solutions. The EMD is a non linear approach decomposing the signal in intrinsic signal (IMF), oscillations of the type FM-AM, giving a time/scale signal representation. Associated with the Hilbert transform (TH), the THH determines the instantaneous amplitude (IA) and instantaneous frequency (IF) of each mode, leading to a time/frequency representation of the ECG and PCG.Without involving an external function, EMD approach can restore (noise reduction), analyze and reconstruct the signal without relocation of its singularities. This approach allows to locate R peaks of the ECG, heart rate and study the cardiac frequency variability (CFV), locate and analyze the sound components B1 and B2 of the PCG.Among the trials and developments that we made, we present in particular a new method (EDA : empirical denoising approach) inspired by the EMD approach for denoising cardiac signals. We also set out the implementation of two approaches for locating ECG signature (QRS complex, T and P waves). The first is based on the detection of local maxima : in using Modulus Maxima and Lipschitz exponent followed by a classifier. The second uses NFLS, wich an nonlinear approach for the detection and location of unique transitions in the discrete domain.