Projects - User contributions [en]

Projects:2015s2-203 Analysis of Heart Sound Signals using the Wavelet Transform

2016-06-28T08:32:26Z

A1651096:

The PCG would be a very useful tool in terms of diagnose. When the PCG is contaminated by unwanted noise, the diagnose cannot be performed. The current study examines methods of eliminating the noise from PCG with wavelet analysis and averaging process. There is no way of determining what is the noise component from environment once the measurement has been recorded. Thus in every case and situation, the noise is different. Figure illustrates the principle of de-noising PCG signal

[[File:图片 1.png]]

The character of noise is recognized as coloured instead of only white. Figure 3.1.2 proves that the power spectral densities of pink, red and white and a real PCG signal measurements are presents.

[[File:图片 2.png]]

Using wavelet can perform de-noising process of a one-dimensional signals. Because the wavelet de-noising algorithm, performs similarly so that the colour of noise is regardless and pink noise is only considered in further part of this paper because de-noising pink noise has better performance in the SNR evaluation (shown in figure)

[[File:图片 3.png]]

Wavelet basis function is decided to remove the noise accurately. Figure 3.1.9 presents the comparison of algorithm achievement for wavelet basis function that is Coif (4 and 5), Daubechies (11, 14 and 20), and Symlet (9, 11 and 14) wavelets. The level of decomposition is 10 and the threshold is the application of minimaxi threshold algorithm and mln rescaling function.

[[File:图片 4.png]]

The next step is decision of the decomposition level. The signal-to-noise ratio is calculated to evaluate the signal contaminated by white noise of 1dB, 5dB, 10dB, 15dB and 20dB. Coif5 is performed for the DWT decomposition. Minimaxi threshold algorithm is selected for the threshold selection algorithm. Mln rescaling function is peaked for the rescaling function. Therefore, the suitable decomposition level is at 7th.

[[File:图片 5.png]]

Figure shows the comparison of threshold selection algorithm combined with averaging process for wavelet de-noising.

[[File:图片 6.png]]

To sum up, the optimal parameters of wavelet denoising on PCG signal with pink noise are sqtwology threshold selection algorithm, mln threshold rescaling function at level 7th. Figure 3.1.15 indicates the comparison between optimal wavelet denosing and wavelet denoising. Optimal wavelet denoising using the optimal parameters compared with the wavelet denoising using Rigrsure threshold selection algorithm and sln rescaling threshold function at level 4th. The mother wavelet is coif5 and using soft threshold. It can be observed that the performance of optimal wavelet denoising (blue), mean SNR = 19.3279dB, is more outstanding than the wavelet denoisng (red), mean SNR = 17.3488dB in SNR evaluation.

[[File:图片 9.png]]
[[File:图片 10.png]]

Team member
Zishuo Li

Projects:2015s2-203 Analysis of Heart Sound Signals using the Wavelet Transform

2016-06-28T08:31:47Z

A1651096:

The PCG would be a very useful tool in terms of diagnose. When the PCG is contaminated by unwanted noise, the diagnose cannot be performed. The current study examines methods of eliminating the noise from PCG with wavelet analysis and averaging process. There is no way of determining what is the noise component from environment once the measurement has been recorded. Thus in every case and situation, the noise is different. Figure illustrates the principle of de-noising PCG signal
[[File:图片 1.png]]

The character of noise is recognized as coloured instead of only white. Figure 3.1.2 proves that the power spectral densities of pink, red and white and a real PCG signal measurements are presents.
[[File:图片 2.png]]

Using wavelet can perform de-noising process of a one-dimensional signals. Because the wavelet de-noising algorithm, performs similarly so that the colour of noise is regardless and pink noise is only considered in further part of this paper because de-noising pink noise has better performance in the SNR evaluation (shown in figure)
[[File:图片 3.png]]

Wavelet basis function is decided to remove the noise accurately. Figure 3.1.9 presents the comparison of algorithm achievement for wavelet basis function that is Coif (4 and 5), Daubechies (11, 14 and 20), and Symlet (9, 11 and 14) wavelets. The level of decomposition is 10 and the threshold is the application of minimaxi threshold algorithm and mln rescaling function.
[[File:图片 4.png]]

The next step is decision of the decomposition level. The signal-to-noise ratio is calculated to evaluate the signal contaminated by white noise of 1dB, 5dB, 10dB, 15dB and 20dB. Coif5 is performed for the DWT decomposition. Minimaxi threshold algorithm is selected for the threshold selection algorithm. Mln rescaling function is peaked for the rescaling function. Therefore, the suitable decomposition level is at 7th.
[[File:图片 5.png]]

Figure shows the comparison of threshold selection algorithm combined with averaging process for wavelet de-noising.
[[File:图片 6.png]]

To sum up, the optimal parameters of wavelet denoising on PCG signal with pink noise are sqtwology threshold selection algorithm, mln threshold rescaling function at level 7th. Figure 3.1.15 indicates the comparison between optimal wavelet denosing and wavelet denoising. Optimal wavelet denoising using the optimal parameters compared with the wavelet denoising using Rigrsure threshold selection algorithm and sln rescaling threshold function at level 4th. The mother wavelet is coif5 and using soft threshold. It can be observed that the performance of optimal wavelet denoising (blue), mean SNR = 19.3279dB, is more outstanding than the wavelet denoisng (red), mean SNR = 17.3488dB in SNR evaluation.

[[File:图片 9.png]]
[[File:图片 10.png]]

Team member
Zishuo Li

File:图片 10.png

2016-06-28T08:29:59Z

A1651096:

File:图片 9.png

2016-06-28T08:29:43Z

A1651096:

File:图片 6.png

2016-06-28T08:29:27Z

A1651096:

File:图片 5.png

2016-06-28T08:29:09Z

A1651096:

File:图片 4.png

2016-06-28T08:28:49Z

A1651096:

File:图片 3.png

2016-06-28T08:27:40Z

A1651096:

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2016-06-28T08:27:21Z

A1651096:

Projects:2015s2-203 Analysis of Heart Sound Signals using the Wavelet Transform

2016-06-28T08:26:04Z

A1651096:

The PCG would be a very useful tool in terms of diagnose. When the PCG is contaminated by unwanted noise, the diagnose cannot be performed. The current study examines methods of eliminating the noise from PCG with wavelet analysis and averaging process. There is no way of determining what is the noise component from environment once the measurement has been recorded. Thus in every case and situation, the noise is different. Figure illustrates the principle of de-noising PCG signal
[[File:图片 1.png]]

The character of noise is recognized as coloured instead of only white. Figure 3.1.2 proves that the power spectral densities of pink, red and white and a real PCG signal measurements are presents.
[[File:图片 2.png]]

Using wavelet can perform de-noising process of a one-dimensional signals. Because the wavelet de-noising algorithm, performs similarly so that the colour of noise is regardless and pink noise is only considered in further part of this paper because de-noising pink noise has better performance in the SNR evaluation (shown in figure)
[[File:图片 3.png]]

Wavelet basis function is decided to remove the noise accurately. Figure 3.1.9 presents the comparison of algorithm achievement for wavelet basis function that is Coif (4 and 5), Daubechies (11, 14 and 20), and Symlet (9, 11 and 14) wavelets. The level of decomposition is 10 and the threshold is the application of minimaxi threshold algorithm and mln rescaling function.
[[File:图片 4.png]]

The next step is decision of the decomposition level. The signal-to-noise ratio is calculated to evaluate the signal contaminated by white noise of 1dB, 5dB, 10dB, 15dB and 20dB. Coif5 is performed for the DWT decomposition. Minimaxi threshold algorithm is selected for the threshold selection algorithm. Mln rescaling function is peaked for the rescaling function. Therefore, the suitable decomposition level is at 7th.
[[File:图片 5.png]]

Figure shows the comparison of threshold selection algorithm combined with averaging process for wavelet de-noising.
[[File:图片 6.png]]

[[File:图片 9.png]]
[[File:图片 10.png]]

Team member
Zishuo Li

Projects:2015s2-203 Analysis of Heart Sound Signals using the Wavelet Transform

2016-06-28T08:18:35Z

A1651096:

[[File:图片 1.png]]

Projects:2015s2-203 Analysis of Heart Sound Signals using the Wavelet Transform

2016-06-28T08:16:53Z

A1651096: Created page with "sasdsd"

sasdsd

File:图片 1.png

2016-06-28T08:14:05Z

A1651096: