Signal Denoising

The heart is considered the most important organ of our body that controls the circulation of blood throughout the body. Measured heartbeat signals can be further analyzed in order to know the health condition of a person. The challenge of ECG signal measurement and analysis is how to remove the noises imposed on the signal that is interfered from many different sources, such as internal noise in sensor devices, power line interference, muscle activity, and body movements. This paper implemented wavelet transform to reduce the noise imposed on the ECG signal to get a closely actual heart signal. ECG data used in this research are three digitized recorded ECG data obtained from MIT-BIH Arrhythmia Database. The first step is generating the noisy ECG signal as the input system by adding 1W WGN signal into the original ECG signal. Then DWT is applied to extract the noisy ECG signal. Some DWT's parameters, threshold selection (rule, type, rescaling), decomposition level, and desired wavelet family are varied to get the best denoised output signal. All results are recorded to be compared. Based on the results, the best DWT parameter for ECG signal denoising is obtained by Symlet wavelet when the decomposition level is set to 3, with soft thresholding, in rigrsure thresholding rule. Abstrak Organ jantung adalah bagian dari tubuh yang memiliki peran penting yang berfungsi untuk mengendalikan peredaran darah keseluruh tubuh. Pengamatan terhadap cara kerjanya adalah bidang studi yang sangat menarik untuk dipelajari sejak bertahun-tahun yang lalu. Seiring perkembangan teknologi, pola sinyal detak jantung dapat diukur dan dianalisis lebih lanjut untuk mengetahui kondisi kesehatan seseorang. Adapun tantangan yang ditemui dalam pengukuran dan analisis sinyal EKG adalah bagaimana menghilangkan atau meminimalkan derau yang mengganggu sinyal EKG yang berasal dari berbagai sumber, seperti; derau internal perangkat sensor, interferensi pada jalur listrik, aktivitas otot, dan derau akibat gerakan tubuh/organ lainnya. Makalah ini menerapkan transformasi wavelet untuk mengurangi derau yang mengganggu sinyal EKG untuk mendapatkan sinyal detak jantung yang akurat sesuai dengan kondisi sebenarnya. Data EKG yang digunakan dalam penelitian ini sebanyak tiga data EKG yang telah direkam secara digital yang terdapat di database Aritmia MIT-BIH. Metodologi yang dilakukan pertama kali adalah menerapkan derau pada sinyal EKG sebagai input system dengan cara menambahkan sinyal derau WGN dengan power 1W ke sinyal EKG asli. Kemudian DWT diterapkan untuk mengekstrak sinyal EKG yang telah dikenai derau. Kemudian melakukan seting pada beberapa parameter DWT, seperti misalnya; pemilihan threshold (rule, type, rescaling), tingkat dekomposisi, dan jenis wavelet, untuk mendapatkan sinyal keluaran berupa sinyal denoised dari EKG yang paling akurat. Langkah terakhir adalah membandingkan semua hasil berdasarkan perubahan parameter-parameter DWT terhadap seluruh data input. Hasil terbaik dari proses denoising sinyal EKG menggunakan DWT diperoleh dari penggunaan wavelet Symlet, decomposition level sebesar 3, soft thresholding, pada rule thresholding rigrsure.

Regularization is often utilized to elicit the desired physical results from experimental data. The recent development of a denoising procedure yielding about 2 orders of magnitude in improvement in SNR obviates the need for regularization, which achieves a compromise between canceling effects of noise and obtaining an estimate of the desired physical results. We show how singular value decomposition (SVD) can be employed directly on the denoised data, using pulse dipolar electron spin resonance experiments as an example. Such experiments are useful in measuring distances and their distributions, P(r) between spin labels on proteins. In noise-free model cases exact results are obtained, but even a small amount of noise (e.g., SNR = 850 after denoising) corrupts the solution. We develop criteria that precisely determine an optimum approximate solution, which can readily be automated. This method is applicable to any signal that is currently processed with regularization of its SVD analysis.

The heart is considered the most important organ of our body that controls the circulation of blood throughout the body. Measured heartbeat signals can be further analyzed in order to know the health condition of a person. The challenge of ECG signal measurement and analysis is how to remove the noises imposed on the signal that is interfered from many different sources, such as internal noise in sensor devices, power line interference, muscle activity, and body movements. This paper implemented wavelet transform to reduce the noise imposed on the ECG signal to get a closely actual heart signal. ECG data used in this research are three digitized recorded ECG data obtained from MIT-BIH Arrhythmia Database. The first step is generating the noisy ECG signal as the input system by adding 1W WGN signal into the original ECG signal. Then DWT is applied to extract the noisy ECG signal. Some DWT’s parameters, threshold selection (rule, type, rescaling), decomposition level, and desired wave...