Second-Order IIR Notch Filter Design and Implementation of Digital Signal Processing System

Wang, Chun Mei; Xiao, Wei

doi:10.4028/www.scientific.net/amm.347-350.729

Cited by 10 publications

(5 citation statements)

References 4 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The distribution of the different frequencies is given in Table 1. Then, by reconstructing the signal at level 10, irrelevant components such as BW are removed [20]. The final result of applying the preprocessing techniques is shown in Figure 2a.…”

Section: Methodsmentioning

confidence: 99%

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Hafshejani

Mehridehnavi

Hajian

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12-lead electrocardiogram (ECG) waves including T-wave, Q-wave, and ST-segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch-Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior-MI and inferior-MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation. K E Y W O R D S classification and regression tree (CART), electrocardiogram (ECG), feedforward neural network (FFNN), morphological features, myocardial infarction (MI), vectorcardiogram (VCG)This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Hafshejani

Mehridehnavi

Hajian

et al. 2021

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…Hence, it is paramount to remove such errors. Therefore, we use the IIR Notch Filters [25], and FIR Filters [26] to eliminate the unnecessary noise. These filters eliminate both high and low-frequency noises.…”

Section: Denoising the Ecg Signalsmentioning

confidence: 99%

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Kumar¹,

Mallik²,

Kumar³

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…Generally, ECG signals are contaminated with different noise sources such as power line interference (50 Hz) [31] and baseline wander [32]. A second-order infinite impulse response (IIR) notch filter [33] is utilized here to remove the power line noise from ECG signals. Furthermore, a high pass filter with a passband frequency of 0.5 Hz is also employed to remove the low-frequency baseline wander noise.…”

Section: Ecg Preprocessingmentioning

confidence: 99%

Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Arefnezhad

Eichberger

Frühwirth³

et al. 2022

Energies

View full text Add to dashboard Cite

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

show abstract

Second-Order IIR Notch Filter Design and Implementation of Digital Signal Processing System

Cited by 10 publications

References 4 publications

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals

Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals

Contact Info

Product

Resources

About