9 Journal of Advances in Computer Research Quarterly pISSN: 2345-606x eISSN: 2345-6078 Sari Branch, Islamic Azad University, Sari, I.R.Iran (Vol. 5, No. 4, November 2014), Pages: 9-21 www.jacr.iausari.ac.ir New Strategy for Stopping Sifting Process during Bio- signals De-nosing By EMD: In Case of Low Frequency Artifact Reduction from ECG and EMG Mohammad Shahbakhti * , Elnaz Heydari, Mohsen Naji Department of Bio-medical engineering, Dezful branch, Islamic Azad University, Dezful, Iran shahbakhti_m@yahoo.com; elnaz.heydari@gmail.com; m.naji@srbiau.ac.ir Received: 2014/01/19; Accepted: 2014/03/04 Abstract Removal of artifacts from bio-signals is a necessary step before automatic processing and obtaining clinical information. Recently, many applications of Empirical Mode Decomposition (EMD) on biomedical researches have been presented that artifact reduction from bio-signals is one of them. EMD separates a time series into finite numbers of its individual oscillations, which are called intrinsic mode function (IMF). The process of the IMF extraction from a signal is known as the sifting process. The main issue during sifting process is to select an appropriate criterion for stopping IMF extraction when the process reaches the artifact components. In this paper, we try to investigate mean power frequency (MPF) for stopping sifting process, in case of low frequency artifact reduction from ECG and EMG signals. In order to evaluate effectiveness of the proposed index during sifting process, reduction of baseline noise from electrocardiogram signals (ECG) and ECG artifact from electromyogram signals (EMG) have been investigated. The obtained results indicate MPF can be considered as an acceptable criterion to stop sifting process during low frequency artifact elimination. Keywords: Artifact reduction, EMD, Sifting process, ECG, EMG, MPF 1. Introduction Bio-medical signals are often affected by artifacts which have external and biological sources [1-3]. Removal of artifacts from bio-signals is the primary step before classification or even visual diagnosis. In the last two decades, to correct or remove artifacts from bio-signals, various methods have been proposed, including wavelet, ICA, PCA, adaptive filtering and etc [4-8]. In this paper, the application of empirical mode decomposition (EMD) is investigated for elimination of baseline wander from ECG and heart muscle electrical activity from EMG. EMD is a new non-linear technique that has been introduced by Haung [9] for adaptively representation of signals as sum of zero-mean AM and FM components. The basis of this technique consists of decomposing any signal into finite number of intrinsic mode functions (IMF). IMFs are extracted from a signal during a process called "sifting process". The main issue during sifting process is to determine an index for stopping IMF extraction when the process reaches the artifact components. For this aim, we apply mean power frequency (MPF) as the desired index.