Nidhi Rastogi

Bio: Nidhi Rastogi is an academic researcher. The author has contributed to research in topics: Signal processing & Filter (signal processing). The author has an hindex of 1, co-authored 1 publications receiving 15 citations.

Analysis of Butterworth and Chebyshev Filters for ECG Denoising Using Wavelets

Abstract: A wide area of research has been done in the field of noise removal in Electrocardiogram signals.. Electrocardiograms (ECG) play an important role in diagnosis process and providing information regarding heart diseases. In this paper, we propose a new method for removing the baseline wander interferences, based on discrete wavelet transform and Butterworth/Chebyshev filtering. The ECG data is taken from non-invasive fetal electrocardiogram database, while noise signal is generated and added to the original signal using instructions in MATLAB environment. Our proposed method is a hybrid technique, which combines Daubechies wavelet decomposition and different thresholding techniques with Butterworth or Chebyshev filter. DWT has good ability to decompose the signal and wavelet thresholding is good in removing noise from decomposed signal. Filtering is done for improved denoising performence. Here quantitative study of result evaluation has been done between Butterworth and Chebyshev filters based on minimum mean squared error (MSE), higher values of signal to interference ratio and peak signal to noise ratio in MATLAB environment using wavelet and signal processing toolbox. The results proved that the denoised signal using Butterworth filter has a better balance between smoothness and accuracy than the Chebvshev filter.

Bias Impact Analysis of AI in Consumer Mobile Health Technologies: Legal, Technical, and Policy

Abstract: Today’s large-scale algorithmic and automated deployment of decision-making systems threatens to exclude marginalized communities. Thus, the emergent danger comes from the effectiveness and the propensity of such systems to replicate, reinforce, or amplify harmful existing discriminatory acts. Algorithmic bias exposes a deeply entrenched encoding of a range of unwanted biases that can have profound real-world effects that manifest in domains from employment, to housing, to healthcare. The last decade of research and examples on these effects further underscores the need to examine any claim of a value-neutral technology. This work examines the intersection of algorithmic bias in consumer mobile health technologies (mHealth). We include mHealth, a term used to describe mobile technology and associated sensors to provide healthcare solutions through patient journeys. We also include mental and behavioral health (mental and physiological) as part of our study. Furthermore, we explore to what extent current mechanisms - legal, technical, and or normative - help mitigate potential risks associated with unwanted bias in intelligent systems that make up the mHealth domain. We provide additional guidance on the role, and responsibilities technologists and policymakers have to ensure that such systems empower patients equitably.

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

Abstract: Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

ECG denoising using weiner filter and adaptive least mean square algorithm

Abstract: Electrocardiogram (ECG) is needed for health issues related to heart disease. But sometimes due to mismatches in electrodes signal becomes noisy hence, removal of these interference like noise artifacts, baseline wandering and power line interference different filter approaches has been proposed. Various filter approaches are available for removal of noise artifacts from Electrocardiogram (ECG) signal. Filtering methods like Wiener filter and Adaptive Least Mean Square (LMS) algorithm are utilized for denoisingnoise interference from Electrocardiogram (ECG) signal. The main goal is to implement different filters and to compare based on performance parameters of the respective filter like Signal to Noise Ratio (SNR) and power spectral density (PSD). Testing was implemented on artificially noisy Electrocardiogram (ECG) signal which has taken from standard Physio.net database sampled at 50 Hz. For better utilization testing results are compared in term of their performance parameter such as SNR and PSD.

Signal Processing Methods for Heart Rate Detection Using the Seismocardiogram

Abstract: Cardiac diseases are one of the major causes of death. Heart monitoring and diagnostic techniques have been developed over decades to address this concern. Monitoring a vital sign such as heart rate is a powerful technique for detecting heart abnormalities (e.g., arrhythmia). This work presents novel heart rate detection methods, which are both robust and adaptive compared to existing heart rate detection methods. Two different experimental data sets, with varying operating conditions, were used in validating the proposed methods. In this work, utilized methods for heart rate detection include Signal Energy Thresholding (SET), Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). To the best of the author’s knowledge, this work is the first to use EMD and EWT for heart rate detection from Seismocardiogram (SCG) signal. Obtained result from applying SET to ECG signal is selected as our ground truth. Then, all three methods are used for heart rate detection from the SCG signal. The average error of SET method, EWT and EMD respectively 13.9 ms, 13.8 ms and 16 ms. Based on the obtained results, EMD and EWT are promising techniques for heart rate detection and interpretation from the SCG signal. Another contribution of this work is arrhythmia detection using EWT. EWT provides us with the instantaneous frequency changes of the corresponding modes to ECG signal. Based on the estimated power spectral density of each mode, power spectral density of arrhythmia affected ECG is higher (more than 50dB) compared to the power spectral density of a normal ECG which is less than 20dB. This provides the potential for arrhythmia detection using EWT.

Analysis of various window techniques used for denoising ECG signal

Abstract: Electrocardiogram (ECG) is an electric device of measuring the electrical activity of heart. Removal of noise artifacts, baseline wandering and power interference plays a major role in diagnosing most of the heart diseases. ECG signal is confined of P wave, QRS complex and T wave [1]. Various approaches are available for removal of noise artifacts from Electrocardiogram (ECG) signal. ECG signal is low frequency signal. In this paper, FIR low pass filter have been implemented with the help of window techniques at cut off frequency 50 Hz to remove noise artifacts from ECG signal. External noise signal is added artificially to the ECG wave recorded from Physio.net database. Comparison of Electrocardiogram (ECG) signal before and after filtering is completed on the basis of two physical parameters i.e. signal to noise ratio (SNR) and power spectrum density (PSD). The results are calculated using Kaiser and Bartlett window based on FIR filter.

Noise Removal from ECG Signal Based on Filtering Techniques

Abstract: The Electrocardiogram (EKG or ECG) is a semi-cyclic, rhythmically, and synchronous signal with a cardiac function through the passive sensory apparatus in which the apparatus is performing as generator of bioelectric signal mimicking the function of the heart. The EKG signals are inherently weak, and noisy signals built of many variable components due to many environmental factors in which it may include but is not limited to changes in body temperature, body movement, and line frequency 50/60 Hz. The ECG signal cannot be conditioned, amplified, nor reproduced directly and therefore, digital filtering techniques with adjustable window are used in this paper. The paper analyses several models of Finite Impulse Response (FIR) filters of low-pass and high-pass and their aspects in term of response time, gain, and harmonic distortion, and rejection to determine the best band-pass filtering model to reproduce an ECG signal that closely resembles the actual Heart function of a patient. A hybrid filtering model is proposed and experimentally tested. Mean square error (MSE) is used to estimate a signal goodness. MATLAB environment has been used for the experimental part to simulate the signals. This research work has been considered in the context of a larger project that consists of a complex wearable health monitoring system comprising biosensors, wireless communication modules and links, control and processing units, medical shields, wearable materials and advanced algorithms used for decision making and data extracting. The proposed filtering technique is useful in the medical data preprocessing phase.