Papers by Keyword: ECG

Abstract: This paper shows the application of discrete wavelet transform in the analysis of ECG signals to detect R-peaks of the QRS complex. The proper analysis of the ECG signal is crucial to reveal the changes in the waveform to detect heart related diseases. The detection of the P-wave, T-wave, QRS complex is important and the wavelet transform offers a good possibility to recognize the abnormalities. Three different wavelets, Symlet, Coiflet and Daubechies are used for the detection and are compared to choose the most efficient one to identify the R-peaks. Multiresolution analysis (MRA) is used to determine the sufficient level of decomposition. MIT-BIH Arrhythmia Database is used as a dataset for the experiment.

Abstract: Objective: Designing an effective multiplier assists in enhancing microprocessor system performance and complex digital signal processing. The main objective of this work is to design a 16 × 16 Wallace structure multiplier with a parallel prefix adder and evaluate the design's area, power performance, and utilization of resources using three distinct architectures: pipeline, wave pipeline, and hybrid pipeline. Methods: The 16 × 16 Wallace tree multiplier is designed using a parallel prefix adder in the Verilog HDL environment. The Wallace tree multiplier is integrated with a 3-tap FIR filter, and performances are evaluated through a distinct architecture by applying an ECG signal. It is suggested to use a hybrid wave-pipeline multiplier architecture to increase the Wallace tree multiplier's speed and reduce the delay. Delay optimization: In a hybrid pipelining system, the clock duration is relative to the maximum performance difference, whereas in a standard pipeline method, it is comparative to the greatest delay. In the hybrid pipeline multiplier, the last two rows of the partial products are added by parallel prefix adders (PPAs). To lower the delay, the hybrid multiplier uses the Han-Carlson adder for addition. Findings: The hybrid multiplier is executed in the FIR filter for ECG denoising in order to validate its performance. Xilinx ISE is used to synthesize the multiplier structures, whereas Verilog HDL is used for design. Comparing the suggested hybrid design to traditional pipelined designs, the outcome demonstrates that performance is increased while resource use and power optimization are reduced. Novelty: In this work, the hybrid pipeline approach has been applied to the existing Wallace multiplier architecture, and it offers better results in terms of power, area, and delay. The results indicate that the proposed hybrid design outperforms compared to traditional pipelined designs, achieving 48.83% improved delayed performance along with reduced resource usage and power consumption.

Abstract: Fatigue is a condition experienced by a person that causes a decrease in a person's vitality and productivity. Fatigue can be characterized by slowed reaction time and fatigue. People’s condition is a significant factor in driving safety. Based on this increase in the number of accidents according to the Central Statistics Agency (BPS), experts conducted research on detecting fatigue that often occurs. In this study, a system that can detect fatigue is developed using parameters obtained from physiological indicators such as heart signals by using the Low Frequency/High Frequency ratio parameter, muscle signals using the average frequency domain of the muscle signal and oxygen saturation. The detection tool in this study uses the ECG Click Module, EMG Click Module, and Oximeter Click which will be connected to the ARM microcontroller, namely STM32F407ZG. The parameters that have been obtained are processed using the Fuzzy Logic method to determine the level of fatigue. Based on the tests results carried out on three subjects, parameter values were obtained where in the subject the three parameters entered into fuzzy logic, it was found that the three subjects were detected in a fairly tired state. The aggregated output that found from subject A was 0.6303, the aggregated output of subject B was 0.77948, and the aggregated output of subject C was 0.79188. Furthermore for future research development, the signal processing can be done more complex, besides that signal processing and fuzzy logic processing can be embedded so the process runs in realtime.

Abstract: The cardiac signal is very important for the heart disease diagnosis and evaluation. The noise cancelation represent one of the most preprocessing step in ECG signal processing, usually, this signal is very sensitive and varies with time. The ECG signal is mostly contaminated by different signals like Power line noise signal, Baseline signal and muscle signal. The power line interference signal is the most effected signal on the ECG during data recording. Several papers try to cancel the noise based on different ways and to extract the useful information. In this paper a novel approach based on stone blind source extraction is used to extract the pure ECG signal from raw ECG, the main advantage of the proposed approach compared with the classical technique is to separate all the useful information without filtering or cancelling the suitable data from the recording signal. Real ECG data from MIT-BIH databases is taken and the MATLAB program is used to evaluate the experimental results. The performance of the proposed approach is measured based on SNR and MSE. The main contribution of this paper is to use Stone blind source separation technique as a first time in ECG signal analysis and prove that this method is the best technique compared with conventional ways. The obtained result proves Stone BSS technique is very efficient to remove the power line noise.

Abstract: In order to solve the problem of ECG being complex, and the problem of abnormal ECG signal analysis standards being not uniform, this paper analyzes Lin Zetao’s clustering algorithm research achievements in the classification of abnormal heart rate. According to the characteristics of the clustering algorithm which is suitable for dealing with the rare data and the large amounts of data, this paper makes the analysis of the method of setting parameters in key process of the application of the clustering algorithm. This paper proposes an accurate clustering algorithm (LCFCM) about abnormal ECG which combines logical judgment, cluster analysis and fuzzy clustering together. Based on the algorithm design requirements, this algorithm perfects the logical judgment criteria and the key technologies of the ECG waveform vector extraction method. Ultimately, the MIT-BIH database is used as the sample to make experiment. The experiment shows that, this paper proposes LCFCM algorithm, whose accurate rate toward the classification of the abnormal heart rate reaches 93%. And at the same time, the good self-adaptive algorithm on different individuals ECG, has good practical value.

Abstract: Wet gel electrodes are widely used for ECG/EEG monitoring, their low impedance results in high-quality signals. But they have important drawbacks too, such as time-consuming electrode set-up for EEG followed by a painful removal, skin irritation by the gel and signal degradation due to gel drying. Hence various dry electrode types are investigated, such as hard metal electrodes with low impedance but limited patient comfort/safety. We focus on flexible conductive polymer-based electrodes to combine low impedance, user comfort and safety. The composition of the conductive polymers is optimized to improve various properties such as conductivity, which directly affects signal quality and sensitivity to motion artifacts, and mechanical properties of the electrodes, important with respect to patient comfort. Electrode impedance and ECG/EEG signal recordings are evaluated using various polymer compositions and compared to wet gel electrode results. Additive optimization to improve processability of the conductive formulations is performed by dedicated flow studies, and will result in a high electrode fabrication yield. Very promising results are obtained regarding impedance, EEG/ECG signal quality and user comfort.

Abstract: Adding wireless communication and smart sensing technology into telemedicine filed can enhance the abilities of detecting vital signals of the patients and significantly reduce diagnose cost. An embedded software for monitoring health status based on Zigbee network is proposed in this paper. Motion detection is also exploited to recognize current contexts. Experiments have shown the feasibility of proposed programs which can be easily transplanted into different hardware platform.

Abstract: Power line interference (PLI) may lead to the signal-to-noise ratio (SNR) decline sharply on biomedical signals, including the electrocardiogram (ECG). The proposed method employs the relationship of frequency and weights in adaptive filter to track the frequency variation of PLI. Real ECG signals from MIT-BIH database was used in the experiment, and they were corrupt by an artificial PLI signal for experiment. Correction performances of the proposed method and traditional adaptive method were compared by SNR in the paper. The results showed that the proposed method is consistently superior to the traditional one when the power line interference is vary with time, and the proposed method can track the variation of power line interference effectively.

Abstract: Electrocardiosignal feature extraction is the base of electrocard iologic automatic diagnosis. By using wavelet transform multi-resolution analysis, the noise in electrocard iosignal is removed; and by using proximity signals of wavelet transform the base linew ander is filtered. The high frequency noise is handled and eliminated with the default threshold; and the average value of the electrocardiosignals is set to zero. In detection of rpeak, because leak detection will occur when only 2³ detail signals is considered, thus the 2³ and 2⁴ detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time voltage method and amplitude method, the area method offers the highest accuracy.

Abstract: A novel ECG filtering algorithm was researched, which was suitable for the MSP430 platform. Several ECG signal wavelet denoising algorithms were simulated on matlab to compare their filtering effect. The mexican-hat wavelet denoising algorithm can get a better effect on filtering of ECG signal (No. 203 data from the MIT-BIH ECG database). The time complexity of the algorithm is O(n²), and the SNR can also be up to 66%.