Heart sounds are highly non-stationary – AR model is very much inaccurate.ġ6 Suggested Methods Waveshape analysis – Homomorphic Filtering. In addition, there is no model for the timing mechanism of the events. In case of AR model, there is still question of optimality : window size, order etc. Not suitable for abnormal\irregular cardiac activity. In most cases there is no parametric model of the waveshape and\or timing mechanism. Most of the methods are based on rules of thumb – no physical basis. Detected S1, S2 and murmurs.ġ5 Segmentation and Event Detection - Cons Features used : dominant poles (below 80Hz) and bandwidth.
HEARTBEAT SOUND CLASSIFICATION WAVELET MATLAB CODE WINDOWS
Used narrow sliding windows (25ms) to compute 8th order AR model. The choosing criterion : more identified S1s and S2s and less discarded peaks.ġ3 Segmentation Using Wavelet Decomposition and Reconstructionġ4 AR modeling of PCG (Iwata et al. Compare the segmentation results of d4, d5 and a4. Identify S1 and S2 according to set of rules similar to those used in segmentation with envelograms. Use Shannon energy to pick up the peaks above certain threshold. Daubechies filters at frequency bands : a4 : 0-69Hz d4 : Hz d5 : 34-69Hzġ2 Segmentation Using Wavelet Decomposition and Reconstruction Use the frequency bands that contain the majority power of S1 and S2. Identify S1 and S2 according the intervals between adjacent peaks.ġ1 Segmentation Using Wavelet Decomposition and Reconstruction (Liang et al. Recover lost peaks by lowering the threshold Reject extra peaks and recover weak peaks according to the intervals statistics.
The Shannon energy eliminates the effect of noise. Use Shannon energy to emphasize the medium intensity signal. In order to detect\segment\classify cardiac events we might need temporal information.ħ Segmentation Using Envelogram (S. Suggestion for parametric modeling.Ħ Features of PCG The envelope of PCG signals might convey useful information. Most of the methods are non-parametric or semi-parametric (parametric models for the waveshape but non-parametric in the temporal behavior). We will discuss only methods which do not use external references (ECG, CP or other channels). Wavelet decomposition and reconstruction - AR modeling - Envelogram estimation using Hilbert transform Suggested method : Homomorphic analysis Suggested temporal modeling : Hidden Markov ModelsĤ Heart beat, why do you miss when my baby kisses me ?ĥ PCG Analysis We will concentrate mainly on S1 and S2. The classifier.ģ Outline Methods based on waveshape : - Envelogram Classification problem: Feature extraction – waveshape & temporal information. Problems : Pre-processing and noise treatment. 1 Detection, segmentation and classification of heart soundsĭaniel Gill Advanced Research Seminar May 2004
0 kommentar(er)
