Automatic ECG artifact removal in the real-time SEMG recording system

The contaminated electrocardiography (ECG) is a big problem in the surface electromyography (SEMG) signal detection and analysis. The objective of the current study is to propose and validate an algorithm for the automated feature cognition and identification for eliminating ECG artifact from the raw SEMG signals. The utilization of Independent Component Analysis (ICA) method is to decompose the raw SEMG signals into individual independent source components. After that, some of the independent source components with the characteristics of ECG artifact were detected by the automated identification algorithm and thereafter eliminated. The sensitivity and specificity of the algorithm for distinguishing ECG source components from independent source components are 100% and 99% respectively. The automated identification algorithm exhibits the prominent performance of recognition for ECG artifact and can be considered reliable and effective.published_or_final_versio

Surface electromyography low-frequency content: Assessment in isometric conditions after electrocardiogram cancellation by the Segmented-Beat Modulation Method

Background: Surface electromyography (SEMG) is widely used in clinics for assessing muscle functionality. All procedures proposed for noise reduction alter SEMG spectrum, especially in the low-frequency band (below 30 Hz). Indeed, low-frequency band is generally addressed to motion artifacts and electrocardiogram (ECG) interference without any further investigation on the possibility of SEMG having significant spectral content. The aim of the present study was evaluating SEMG frequency content to understand if low-frequency spectral content is negligible or, on the contrary, represents a significant SEMG portion potentially providing relevant clinical information. Method: Isometric recordings of five muscles (sternocleidomastoideus, erectores spinae at L4, rectus abdominis, rectus femoris and tibialis anterior) were acquired in 10 young healthy voluntary subjects. These recordings were not affected by motion artifacts by construction and were pre-processed by the Segmented-Beat Modulation Method for ECG deletion before performing spectral analysis. Results: Results indicated that SEMG frequency content is muscle and subject dependent. Overall, the 50th[25th;75th] percentiles spectrum median frequency and spectral power below 30 Hz were 74[54; 87] Hz and 18[10; 31] % of total (0–450 Hz) spectral power. Conclusions: Low-frequency spectral content represents a significant SEMG portion and should not be neglected. Keywords: Surface electromyographic signal, Electromyographic spectrum, Segmented-Beat Modulation Method, Non-linear filtering, Spectral analysi

Development of ECG and EMG platform with IMU to eliminate the motion artifacts found in measurements

The long term measurement and analysis of electrophysiological parameters is crucial for diagnosis of chronic diseases, and to monitor critical health parameters. It is also very important to monitor physical fitness improvement, or degradation level, of human beings where physical fitness is entirely critical for their work, or of more vulnerable members of society such as senior citizens and the sick. The state-of-the-art technological developments are leading to the use of artificial intelligence in the continuous monitoring and identification of life-threatening events in the daily life of ordinary people. However, these ambulatory measurements of electrophysiological parameters leads to drastic motion artifacts because of the test subject’s movements. Therefore, there is a dire need for the development of both hardware and software solutions to address this challenge. The scope of this thesis is to develop a hardware platform, by using off-the-shelf discrete and IC electronic components, to measure two electrophysiological parameters, electrocardiogram (ECG) and electromyogram (EMG), with an additional motion sensor inertial measurement unit (IMU) comprising nine degrees of freedom. The ECG, EMG and IMU data will be collected using the developed measurement platform from various predefined day-to-day routine activity events. A Bluetooth interface will be developed to transmit the data wirelessly, and record it on a laptop for further real-time processing. The resources of the electrical workshop and measurement lab at Aalto University will be used for the development, assembly, testing and finally for research of the measurement platform. The second aspect of the study is to prepare, process and analyze the recorded ECG and EMG data by using MATLAB. Various filtering, denoising, processing and analysis algorithms will be developed and executed to extract the features of the ECG and EMG waveform structures. Finally, graphical representations will be made for the resulting outputs of the aforementioned techniques

Extended segmented beat modulation method for cardiac beat classification and electrocardiogram denoising

none4noBeat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.openNasim A.; Sbrollini A.; Morettini M.; Burattini L.Nasim, A.; Sbrollini, A.; Morettini, M.; Burattini, L

Efficient procedure to remove ECG from sEMG with limited deteriorations: Extraction, quasi-periodic detection and cancellation

An efficient method is presented to remove ECG from EMG with limited deterioration. The ECG pulses are first localized and then remove only where they have been detected. A combination of ICA and DWT is first used to extract ECG information. Then, the pulses positions are detected with an original algorithm based on FFT which takes advantage of the quasi-periodic nature of the ECG. The proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals even when both signals are strongly overlapped. The interpretations of the surface electromyography (sEMG) signals from the trunk region are strongly distorted by the heart activity (ECG), especially in case of low-amplitude EMG responses analyses. Many methods have been investigated to resolve this nontrivial problem, by using advanced data processing on the overall sEMG recorded signal. However, if they reduce ECG artifacts, those cancellation methods also deteriorate noiseless parts of the signal. This work proposes an original ECG cancellation method designed to limit the deterioration of sEMG information. To do that, the proposed techniques does not directly attempt to remove the ECG, but is based on two main steps: the localization of ECG and the cancellation of ECG but only where heart pulses have been detected. The phase of localization efficiently extracts the ECG contribution by combining the discrete wavelet transforms (DWT) and the method of independent component analysis (ICA). And finally, this phase takes advantage of quasi-periodic properties of ECG signals to accurately detect pulses localization with an original algorithm based on the fast Fourier transform (FFT). Intensive simulations were achieved in terms of relative errors, coherence and accuracy for different levels of ECG interference. And the correlation coefficients computed from the paraspinal muscles EMG signals of 12 healthy participants were also used to evaluate the developed method. The results from simulation and real data demonstrate that the proposed method accurately detects pulses positions and efficiently removes the ECG from EMG signals, even when both signals are strongly overlapped, and greatly limits the deterioration of the EMG