Search

The ECG (Electrocardiogram) signal is the most important signal in the biomedical signal processing, because it is used to diagnose the wellness of heart's activity. Now a days the ECG signal processing devices have become more compact and smaller in size. Implementing the algorithms in devices is a complex task. Various blocks like preprocessing, extraction and diagnosis have to be implemented in hardware. The ECG signal will be corrupted by different types of noises. The filter to be implemented should be efficient in removal of the noise and extraction of medical information. In this paper, implementation of adaptive filtering using Swarm-based optimization techniques for biomedical system on chip architecture is presented. The proposed algorithm was implemented in different hardware and the performance is compared. The filters show linear phase and high rejection ratio. Field programmable gate arrays were used for implementation. These adaptive filters form the part of filter circuits for preprocessing and extraction of ECG signals. Implementation was done using Intel Altera innovative Programmable Power Technology tool Quartus. [ABSTRACT FROM AUTHOR]

Recently, photoplethysmogram (PPG) signal is widely adopted in health monitoring devices for automated assessment of different cardiovascular parameters. However, research in the area of computerized health analysis using PPG signal features is still lagging behind. In this paper, a robust, automated yet simple algorithm is proposed for accurate detection of characteristic points from the PPG signal and its derivatives. The methodology follows amplitude thresholding, slope-reversal, and an empirical formula based approach. Finally, Baseline modulation is removed from the PPG dataset and features are extracted from the amplitude normalized PPG signal and its derivatives. Performance of the proposed algorithm is evaluated over MIMIC database as well as over real PPG data acquired from both healthy volunteers and cardiac patients. The algorithm exhibits high efficiency for all detected fiducial points with an average sensitivity, positive predictivity and detection accuracy of 99.80%, 99.84%, and 99.65%, respectively. Compared to the existing methods, the proposed algorithm offers complete characterization of the PPG signal and its derivatives. [ABSTRACT FROM AUTHOR]

Single-channel electroencephalography (EEG) signals are more susceptible to electro-oculography (EOG) interference, which could be attributed to the acquisition device of the single-channel. To realize EOG artifacts separation in this paper, the blind deconvolution (BD) model was investigated based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The CEEMDAN method was firstly used to decompose the EEG data contained artifacts into several intrinsic mode functions (IMF). After that, the modal component used as the observed signal was provided to the BD model, which was formed by the source signal of the EEG signal and the EOG artifacts. Consequently, we successfully realized the separation of EEG signal and EOG artifacts by the constructing cost function iteratively, and our results demonstrated that the separation effect of this method on EOG artifacts is better than previous studies. Further, the correlation coefficient of real-life data after CEEMDAN-BD algorithm processing reaches 0.81. Moreover, the modal aliasing problem was solved with most of the original EEG signal components retained. In a word, this novel method provides theory and practice references for the processing of EEG signals and other physiological signals. [ABSTRACT FROM AUTHOR]

This paper presents a method of postural symmetry evaluation implementing the so-called follow-up posturography. The method boils down to the assessment of similarity of the phase approximation of the counter-clockwise follow-up CoP (Center of Pressure) trajectory and the mirror image against the y-axis of the phase approximation corresponding to the clockwise follow-up CoP trajectory. The usability of the presented approach was tested on the data collected in the group of 30 patients rehabilitated after total hip arthroplasty. The observed difference between the values of the proposed postural symmetry coefficient obtained at the end and at the beginning of the rehabilitation program was statistically significant (p < 0.001). These values, however, were not significantly correlated with the values of postural symmetry coefficients computed in static posturography. Lack of significant correlations between the coefficients supports the reasoning that the new postural symmetry evaluation method quantifies symmetry of posture in terms of dynamic mechanisms, which are not manifested in the case of static posturography. As a major advantage of the herein discussed approach one can distinguish its potential to evaluate postural symmetry in dynamic conditions using relatively inexpensive single-plate posturographic platform. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals are commonly used in analysing the brain activity. The EEG signals have small amplitude and hence, they are often affected by the artefacts. For the efficient processing, it is necessary to remove the artefacts from the EEG signals. This paper develops a technique through deep learning scheme for removing the artefacts present in the EEG signal. Initially, the EEG signals are pre-processed and provided to the feature extraction process, where the wavelet features are extracted from the signal by applying the wavelet transform. The extracted features are provided to the proposed classifier, namely deep-ConvLSTM, for removing the artefacts from the EEG signal. Here, the deep learner is trained based on the proposed Cat Swarm Fractional Calculus Optimisation (CSFCO) algorithm, which is the integration of Cat Swarm Optimisation (CSO) and Fractional Calculus (FC). Experimentation of the proposed technique is carried out by introducing artefacts, such as ECG, EMG, EOG and random noise on the EEG signal. Simulation results carried out on the proposed deep-ConvLSTM depict that the proposed framework has better performance than the comparative techniques with the values of 3888.362, 62.356, and 69.939 dB, for the MSE, RMSE, and SNR, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on electrocardiogram (ECG) signals is proposed for identity recognition. A unique feature called dynamics, which is fundamentally different from features used in literature, is extracted from ECG signals and used for identity recognition. Deterministic learning, a recently proposed machine learning approach, is used to model the dynamics of training ECG signals. A set of estimators employing the modelling results of training ECG signals is constructed. Through comparing the test ECG signal (measured from a subject to be recognized) with the estimators, a set of errors can be obtained and used to measure the similarity between the test and the training ECG signals. The test ECG signal is recognized in accordance with the smallest error, and then the subject can be recognized rapidly. Experimental results indicate that the proposed method is reliable and efficient for identity recognition. [ABSTRACT FROM AUTHOR]

Sigma Delta analogue-to-digital converters allow acquiring the full dynamic range of biomedical signals at the electrodes, resulting in less complex hardware and increased measurement robustness. However, the increased data size per sample (typically 24 bits) demands the transmission of extremely large volumes of data across the isolation barrier, thus increasing power consumption on the patient side. This problem is accentuated when a large number of channels is used as in current 128–256 electrodes biopotential acquisition systems, that usually opt for an optic fibre link to the computer. An analogous problem occurs for simpler low-power acquisition platforms that transmit data through a wireless link to a computing platform. In this paper, a low-complexity encoding method is presented to decrease sample data size without losses, while preserving the full DC-coupled signal. The method achieved a 2.3 average compression ratio evaluated over an ECG and EMG signal bank acquired with equipment based on Sigma-Delta converters. It demands a very low processing load: a C language implementation is presented that resulted in an 110 clock cycles average execution on an 8-bit microcontroller. [ABSTRACT FROM PUBLISHER]

In this paper, a portable and cost-effective 16-channel EEG acquisition system with multiple analog front ends cascaded was designed. Channels required were selected according to chip selecting signal to configure each in a dynamic and personalized manner. In order to test system performance, EEG signals of five subjects from spontaneous state were sent to the host computer through the high-speed wireless module. Related steady-state visual evoked experiment was collected and analyzed. It was observed that obvious alpha rhythm appears during eyes-closed in spontaneous EEG signals; and peak values appears at the corresponding stimulating frequency and harmonics in steady-state visual evoked experiment. The results show that the acquisition system can accurately collect spontaneous EEG signals and evoked EEG signals. Therefore, it is suggested this system can be used in applications of brain computer interface and have certain theoretical and practical values. [ABSTRACT FROM AUTHOR]

In general, most of the Biomedical signals such as Electrocardiogram (ECG), Electroencephalogram, and Electro-oculogram are nonstationary signals, suffers from different interferences like power line interference (PLI) and with other biomedical signals that gets added with it. Analysis of these signals means the extraction of useful information from the signal, and in this paper it is carried out using a new nonlinear and nonstationary data analysis method called Empirical Mode Decomposition (EMD). The key feature of this method is that it can decompose the signal into different IMFs and makes the analysis simple. Compared with other tools like Fourier analysis and wavelet methods, EMD is purely a data-driven and adaptive technique. Thus, it is well suited to analyze nonstationary signals like biosignals. This paper foregrounds an EMD-based, two-weight adaptive filter structure to reduce the PLI in ECG signals. Two methodologies are studied based on EMD and the simulations are carried out in a MATLAB environment. The denoised signals are visually impressive and the methodologies are well suited for real-time implementation. [ABSTRACT FROM AUTHOR]

The QRS complex is the most distinctive feature in an electrocardiogram (ECG) signal. Therefore, its detection serves as the starting point for various applications, such as detection of other waves and segments, heart-rate calculation, derivation of respiration, etc. In this paper, a novel technique for QRS detection is proposed. The technique is based on the recently proposed synchrosqueezed wavelet transform (SSWT), which is obtained by application of a post-processing technique known assynchrosqueezingto the continuous wavelet transform. Following SSWT, various other processing steps are applied, including a nonlinear mapping technique, which is novel in the context of QRS detection, to finally detect the R-peaks. The proposed algorithm is evaluated on the MIT-BIH arrhythmia database and overall sensitivity, positive predictivity and error rate obtained are 99.92%, 99.93%, and 0.15%, respectively. [ABSTRACT FROM PUBLISHER]

Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone. [ABSTRACT FROM PUBLISHER]

Traditional electroencephalogram (EEG) measurements require conductive gel to ensure good conduction between the electrodes and skin (or scalp). But this gel may make patients uncomfortable because of possible skin allergies during long-term measurement. The measurement procedure is also tedious because pasting the electrodes to the patient's scalp is time consuming. The required lead wires also constrain patient movements. This study uses conductive fabric as the capacitive electrode for EEG measurements. This paper provides the design considerations and the related theoretical analysis for this method. A long-term, null-input experiment was conducted to verify the stability of the proposed system. Experiments were conducted over the hair of patients to verify the feasibility of using the method during EEG monitoring. Various mental task experiments were also conducted. The changes in frequency domain analysis show that the proposed strategy discriminates between different mental tasks. [ABSTRACT FROM AUTHOR]