Review on Power Line Interference Removal from ECG Signal Using Adaptive and Error Filter

An ECG signal is basically an index of the functionality of the heart. For example, a physician can detect arrhythmia by studying abnormalities in the ECG signal. Since very fine features present in an ECG signal may convey important information, it is important to have the signal as clean as possible. Power line interference may be significant in electrocardiography. Often, a proper recording environment is not sufficient to avoid this interference. ECG signals polluted by power line noise of relatively large amplitude were the frequency of power line interference accurately at 50 Hz or 60 Hz, a sharp notch filter would be able to separate and eliminate the noise. The major difficulty is that the frequency can vary about fractions of a Hertz, or even a few Hertz. Two different approaches have been proposed in literature for this purpose notch filters and adaptive interference cancellers. Notch filters reduce the power line interference by suppressing predetermined frequencies. One of the possible alternatives to take frequency variations into account is the use of an external reference power line signal. An ideal EMI filter for ECG should act as a sharp notch filter to eliminate only the undesirable power line interference while automatically adapting itself to variations in the frequency and level of the noise. This technique, available by the use of adaptive filters only, is reported in literature and present serious practical difficulties and is difficult to implement

Magnetocardiography in unshielded environment based on optical magnetometry and adaptive noise cancellation

This thesis proposes and demonstrates the concept of a magnetocardiographic system employing an array of optically-pumped quantum magnetometers and an adaptive noise cancellation for heart magnetic field measurement within a magnetically-unshielded environment. Optically-pumped quantum magnetometers are based on the use of the atomic-spin-dependent optical properties of an atomic medium. An Mxconfiguration- based optically-pumped quantum magnetometer employing two sensing cells containing caesium vapour is theoretically described and experimentally developed, and the dependence of its sensitivity and frequency bandwidth upon the light power and the alkali vapour temperature is experimentally demonstrated. Furthermore, the capability of the developed magnetometer of measuring very weak magnetic fields is experimentally demonstrated in a magnetically-unshielded environment. The adaptive noise canceller is based on standard Least-Mean-Squares (LMS) algorithms and on two heuristic optimization techniques, namely, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The use of these algorithms is investigated for suppressing the power line generated 50Hz interference and recovering of the weak magnetic heart signals from a much higher electromagnetic environmental noise. Experimental results show that all the algorithms can extract a weak heart signal from a much-stronger magnetic noise, detect the P, QRS, and T heart features and highly suppress the common power line noise component at 50 Hz. Moreover, adaptive noise cancellation based on heuristic algorithms is shown to be more efficient than adaptive noise canceller based on standard or normalised LMS algorithm in heart features detection

A novel fixed-point leaky sign regressor algorithm based adaptive noise canceller for PLI cancellation in ECG signals

In this paper, a novel fixed-point Leaky Sign Regressor Algorithm (LSRA) based adaptive noise canceller has been employed for the cancellation of 60 Hz Power Line Interference (PLI) from the ElectroCardioGram (ECG) signal. A sufficient condition for the convergence in the mean of the LSRA algorithm is also derived. The fixed-point LSRA-based adaptive noise canceller employed in this work is fully quantized using an in-house quantize function. The most effective number of quantization bits required for the various parameters are found to be 6-bits and are determined through rigorous simulations. The filtered ECG signal free from 60 Hz PLI is successfully recovered using a novel 6-bit fixed-point LSRA-based adaptive noise canceller

Modified Variational Mode Decomposition for Power Line Interference Removal in ECG Signals

Power line interferences (PLI) occurring at 50/60 Hz can corrupt the biomedical recordings like ECG signals and which leads to an improper diagnosis of disease conditions. Proper interference cancellation techniques are therefore required for the removal of these power line disturbances from biomedical recordings. The non-linear time varying characteristics of biomedical signals make the interference removal a difficult task without compromising the actual signal characteristics. In this paper, a modified variational mode decomposition based approach is proposed for PLI removal from the ECG signals. In this approach, the central frequency of an intrinsic mode function is fixed corresponding to the normalized power line disturbance frequency. The experimental results show that the PLI interference is exactly captured both in magnitude and phase and are removed. The proposed approach is experimented with ECG signal records from MIT-BIH Arrhythmia database and compared with traditional notch filtering

Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers

Although cascaded multistage adaptive noise cancellers have been employed before by researchers for multiple artifact removal from the ElectroCardioGram (ECG) signal, they all used the same adaptive algorithm in all the cascaded multi-stages for adjusting the adaptive filter weights. In this paper, we propose a cascaded 4-stage adaptive noise canceller for the removal of four artifacts present in the ECG signal, viz. baseline wander, motion artifacts, muscle artifacts, and 60 Hz Power Line Interference (PLI). We have investigated the performance of eight adaptive algorithms, viz. Least Mean Square (LMS), Least Mean Fourth (LMF), Least Mean Mixed-Norm (LMMN), Sign Regressor Least Mean Square (SRLMS), Sign Error Least Mean Square (SELMS), Sign-Sign Least Mean Square (SSLMS), Sign Regressor Least Mean Fourth (SRLMF), and Sign Regressor Least Mean Mixed-Norm (SRLMMN) in terms of Signal-to-Noise Ratio (SNR) improvement for removing the aforementioned four artifacts from the ECG signal. We employed the LMMN, LMF, LMMN, LMF algorithms in the proposed cascaded 4-stage adaptive noise canceller to remove the respective ECG artifacts as mentioned above. We succeeded in achieving an SNR improvement of 12.7319 dBs. The proposed cascaded 4-stage adaptive noise canceller employing the LMMN, LMF, LMMN, LMF algorithms outperforms those that employ the same algorithm in the four stages. One unique and powerful feature of our proposed cascaded 4-stage adaptive noise canceller is that it employs only those adaptive algorithms in the four stages, which are shown to be effective in removing the respective ECG artifacts as mentioned above. Such a scheme has not been investigated before in the literature

New algorithm for fetal QRS detection in surface abdominal records

The proposed method detects fetal R waves on abdominal non-invasive records. An exponentially averaged pattern of the mother PQRST segment is obtained and subtracted. Subsequently the fetal R detector based on a Smoothed Nonlinear Energy Operator (SNEO) is applied to the residual signal. Finally, criteria about amplitude, heart rate and backward search are settled to correct false detections. To evaluate the fetal R detector, 10 multichannel records were used, acquired between gestation week 22 and 40. The position of the fetal R waves were manually marked (N=1490), and these reference marks were compared with the ones from the detector. It was obtained a 88.83% of sensitivity and a 91.32% of positive prediction value. The application of the detector to all the abdominal channels will probably allow improving the obtained results

Relationship between decomposition level and induced solidification of peat based on laboratory investigation

Over 60 % of Pontian district is covered by peat. Peat is considered as a poor quality soil for construction due to the high moisture content and low bearing capacity. Solidification of peat is important in this area before any construction work could start thus, will increase the population rate in the district. The degree of decomposition affects the porosity of peat while the porosity is affected by both particle size and structure of the peat. The pores between the decomposed materials in peat can be filled and bound using ordinary portland cement (OPC) and coal ash (fly ash, FA and bottom ash, BA). Different decomposition levels of peat require different amounts of filler and binder to achieve the optimum strength. The peats are categorized as fabric for the less decomposed peat, hemic for the moderately decomposed and sapric for the mostly decomposed peat. The Pontian peat has high moisture content with fabric peat having 970 %, hemic peat, 417 % and sapric peat, 720 %. All peat was found acidic with pH 3-4.5 while the binders and filler are in alkaline state. The physico-chemical and mechanical properties of peat were identified according to British (BS 1377, 1990) and US (ASTM, 2000) standards. Chemical tests were adopted from previous researchers to identify the chemical properties. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bender element (BE) test and the same chemical tests as applied for the original sample. The mix ratios examined were of four types being 100 % OPC, 50 % OPC 50 % BA, 50 % OPC 25 % BA 25 % FA and 25 % OPC 50 % BA 25 % FA. Two water-binder ratios were used, i.e. 1 and 3. Curing periods of 7, 14, 28 and 56 days were applied for all samples. The moisture content of the peat was controlled at 300 % before mixing. The scanning electron microscope (SEM) result shows that over time, the peat was filled with calcium silicate hydrate (CSH) and calcium aluminate hydrate (CAH) which were products of cement hydration. The strength gain for fabric peat is 157 kPa, while hemic peat, 737 kPa and sapric peat, 121 kPa. It is concluded that regardless the peat decomposition level, the optimum for a peat-binder-filler mixture to get the significant strength, should consist of i) 23 - 34 % of particles, being combination of peat fiber and BA with size ranging from 2 mm to 0.15 mm, ii) OPC with equal amount of dry mass of the peat and iii) 25 % of FA by the total mass of binder. This combination was found to be effective for the peat-binder-filler mixture. Keywords: Peat decomposition level, bottom ash, fly ash, OPC, solidification

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator

PubMed ID: 26799770Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Web of Science37225623