Motion Artifact Cancellation in Ambulatory Ecg Measurement System for the Detection of Cardiac Diseases

In this work, a simple and efficient artifact cancellation in ambulatory ECG using adaptive filter is designed for the detection of different cardiac diseases like bradycardia, tachycardia, left ventricular hypertrophy and right ventricular hypertrophy. Our work is focused on extraction of noise free ECG signal and the real-time implementation of artifacts removal techniques. As ECG signal is very sensitive in nature, and even if small noise mixed with original signal the various characteristics of the signal changes, data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real-time ECG measurement systems. Here we have implemented different adaptive filtering algorithms (LMS-Least Mean Square, RLS-Recursive Least Squares) using virtual instrumentation technique to minimize the noisy components and to analyze different cardiac diseases like bradycardia, tachycardia, left ventricular hypertrophy and right ventricular hypertrophy. Finally the overall performance of LMS and RLS algorithm is also compared according to the error signal generated by the techniques. Keywords-Ambulatory ECG; Adaptive filter; Virtual Instrumentation; Artifacts; Cardiac Disease; Arrhythmia; Bradycardia; Tachycardia; Left and Right Hypertrophy. INTRODUCTION Electrocardiograph (ECG) is a transthoracic interpretation of the electrical activity of the heart over time captured and externally recorded by skin electrodes. It is a noninvasive recording produced by an electrocardiography device. ECG is very significant to diagnose the heart disease such as myocardial ischemia, arrhythmia and cardiac infarction. Recently, ECG is used on purpose to keep good health as well as to diagnose the heart disease [1]. The oxygen demand in the cardiac muscle is different according to the body condition. The ECG works mostly by detecting and amplifying the tiny electrical changes on the skin that are caused when the heart muscle "depolarizes" during each heart beat [9]. At rest, each heart muscle cell has a charge across its outer wall, or cell membrane. Reducing this charge towards zero is called de-polarization, which activates the mechanisms in the cell that cause it to contract [2]. During each heartbeat a healthy heart will have an orderly progression of a wave of depolarization that is triggered by the cells in the sinoatrial node, spreads out through the atrium, passes through "intrinsic conduction pathways" and then spreads all over the ventricles. This is detected as tiny rises and falls in the voltage between two electrodes placed either side of the heart which is displayed as a wavy line either on a screen or on paper. This display indicates the overall rhythm of the heart and weaknesses in different parts of the heart muscle [6]. The purpose of the study discussed herein was to develop a continuous healthcare system offering greater convenience in vital signal monitoring in our daily life. To achieve this purpose, we have developed a system that monitors ECG, a data rich signal with comprehensive use in monitoring a person’s health. Further, this system was applied with adaptive signal processing to enable continued signal measuring while the subject carries on their normal daily routine, freeing the subject from the static constraints of conventional vital signal measuring processes undergone in medical institutions [3]. Fig. 1Normal QRS complex and intervals in two ECG pulses Ambarish G. Mohapatra and Saroj Kumar Lenka 43 Advances in Computational Research ISSN: 0975–3273 & E-ISSN: 0975–9085, Volume 3, Issue 1, 2011 ECG signal is a type of electrical signal generated as myocardial tissues making up the heart constrict and relax under the regulation of the heart’s impulse conduction system. Specifically, the waveform derived from measuring these types of electrical and biological electric generation using external leads is ECG [5]. Generally, ECG consists of a P wave, a QRS complex, and a T wave. P wave is formed as the atria constrict QRS complex forms as the ventricles constrict, and T wave is formed as the ventricles relax. The wave that forms as the astria relax virtually overlaps entirely with the wave generated with the constriction of the ventricles and is therefore ignored. “Figure 1” shows an example of ECG waveform and the various parameters that can be derived from ECG waveform for use in diagnosis and health monitoring. Normal ECG signals carry a virtually consistent cycle and generate a regular rhythm. The heart signals from the electrodes are very low level signals. To collect correct samples of the raw electrode signals a highly sensitive, high CMRR and high slew rate amplifier is required. In this work, we have designed a high sensitive differential amplifier and a high gain amplifier as shown in figure 1. The output of the amplifier stage is directly connected to a National Instruments DAQ card for the acquisition and adaptive signal processing of the raw data. Fig. 2Basic circuit connections required for data acquisition. ECG bandwidth between 0.05Hz and 100Hz is used for general diagnosis applications, and ECG bandwidth between 0.05Hz and 35Hz is used for patient monitoring or healthcare purposes. These ECG bandwidths, however, can overlap with other elements such as the 60Hz power supply noise, baseline wandering due to respiration, high frequency noises originating from various electronic devices and equipments, motion artifact from changes in skin-to-lead impedance brought on subject movement, and EMG signal of muscle tissue movements [4]. Filter set comprising of a high pass filter, a low pass filter, and a notch filter is the most commonly used method of canceling noise elements embedded in the ECG signal. An adaptive filter is required when either the fixed specifications are unknown or the specifications cannot be satisfied by time-invariant filters [1]. Strictly speaking an adaptive filter is a nonlinear filter since its characteristics are dependent on the input signal and consequently the homogeneity and additivity conditions are not satisfied. In an ECG signal the motion artifacts are usually not fixed specifications. That’s why adaptive filters are usually implemented for the reduction of motion artifacts and other undesired noisy components in the usual ECG signal. ADAPTIVE FILTER Discrete-time (or digital) filters are ubiquitous in today’s signal processing applications. Filters are used to achieve desired spectral characteristics of a signal, to reject unwanted signals, like noise or interferers, to reduce the bit rate in signal transmission, etc. The notion of making filters adaptive, i.e., to alter parameters (coefficients) of a filter according to some algorithm, tackles the problems that we might not in advance know, e.g., the characteristics of the signal, or of the unwanted signal, or of a systems influence on the signal that we like to compensate. Adaptive filters can adjust to unknown environment and even track signal or system characteristics varying over time [3]. In a transversal filter of length N, as depicted in figure 3, at each time n the output sample y[n] is computed by a weighted sum of the current and delayed input samples x[n], x[n − 1], . . ... Fig. 3General Block Diagram of Adaptive Filter Here the output signal y[n] is expressed as the weighted sum of input signal.