Shannon Energy Envelope Research Articles

Detection of myocardial infarction using Shannon energy envelope, FA-MVEMD and deterministic learning

Myocardial infarction (MI) poses a significant clinical challenge, necessitating expeditious and precise detection to mitigate potentially fatal outcomes. Current MI diagnosis predominantly relies on electrocardiography (ECG); however, it is fraught with limitations, including inter-observer variability and a reliance on expert interpretation. This study introduces an automated MI detection framework that capitalizes on hybrid signal processing methodologies and deterministic learning theory. The initial step involves the extraction of the Shannon energy envelope (SEE) and its derivative from a single-lead ECG. Integration of the SEE into the ECG’s phase portrait provides a means to capture the underlying nonlinear system dynamics. Subsequently, the application of fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) yields discriminative features originating from the most energetically dominant intrinsic mode components (IMFs) within the SEE. Profound dissimilarities are discernible between ECG signals recorded from healthy subjects and those afflicted with MI. In the subsequent phase, deterministic learning theory, implemented through neural networks, is employed to facilitate the classification of ECG signals into two distinct groups. The method’s efficacy is meticulously evaluated using the PTB diagnostic ECG database, resulting in a noteworthy average classification accuracy of 99.21%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} within a tenfold cross-validation framework. In summation, the findings affirm that the proposed features not only complement conventional ECG attributes but also align closely with the underlying dynamics of the ECG system, ultimately fortifying the automatic detection of MI. The imperative requirement for early and accurate MI diagnosis is addressed through our approach, offering a robust and dependable means to fulfill this pivotal clinical need.

An Approach to Identify “ R Peak” of an Ecg Signal Using Shannon Energy to Improve Clinical Diagnosis

A novel method for processing Electrocardiogram (ECG) signals, with a focus on interpreting the QRS complex and extracting its characteristics. The algorithm incorporates a variety of techniques, including median filtering, the Shannon energy transform, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). These filters play a crucial role in the preprocessing phase, reducing undesired background noise and interference. With in the algorithm, the Shannon energy (SE) is computed, and an SE envelope is generated by applying a predefined threshold. This envelope serves the purpose of pinpointing R waves in the time domain. False peaks, stemming from residual noise, are eliminated by averaging peak-to-peak intervals. The algorithm's performance is evaluated using MIT-BIH arrhythmia database, demonstrating significant improvements in signal quality, reduced noise, and closer alignment to the original signal. This underscores the efficiency of the proposed signal processing technique.

Signal Pre-processing Method for Condition Monitoring based on Vibration Signals from On-Site Manipulators

Handling irregular and noisy field data is challenging in condition monitoring. In contrast to refined lab data, where external influences are kept to a minimum, acquired signal from accelerometer attached to mechanical devices involves a great deal of uncontrollable variables. Especially, irregular operation cycles of the process make difficult to specify significant vibration signals for monitoring without mechanical expertise and information of the manipulators' motion. In this study, we distinguish motion signals from noisy raw signal using Shannon Energy Envelope (SEE). The extracted individual motion signals are algorithmically clustered through the signal graph characteristics for each robot motion. Clusters are evaluated for the effectiveness of monitoring, and it enables users to obtain a reference whose signal can perform the same accuracy for condition monitoring with expert knowledge.

4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction

4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction

Envelope Detection by Shannon Energy Calculation in DCT Domain and DFS-Based Notch Filter for Interference Mitigation in GNSS Receivers

A new pre-correlation technique to enhance the Global Navigation Satellite Systems (GNSS) receivers, against the continuous wave interferences (CWI), is presented. Accordingly, the detection and the localisation procedure are constituted of many steps. First, the discrete cosine transform (DCT) is applied on the contaminated signal. Next, the Shannon-energy envelope detector, in the DCT-domain, is accomplished. Then, all envelope magnitudes above a predefined threshold, representing the interference components, are localized in the frequency domain. The following step consists in the use of the discrete Fourier series (DFS) technique to calculate the corresponding contributing harmonics of the CW interferences. Finally, the interference is reduced efficiently by a subtraction of its approximated version from the original contaminated signal. The results provided from the simulation prove that the DFS-based notch filter in terms of signal quality restoration, for both single-tone and multi-tone, is of superior performance compared to the classical notch filtering.

A ROBUST QRS COMPLEX DETECTION METHOD BASED ON SHANNON ENERGY ENVELOPE AND HILBERT TRANSFORM

QRS complex detection plays an important role in electrocardiogram (ECG) automatic analysis. The accuracy and robustness of the detection algorithm greatly affect its practicability. However, the existing detection algorithms are greatly affected by ECG signal quality, and some detection algorithms cannot even work properly due to the poor signal quality. In this paper, a robust QRS complex detection algorithm is proposed based on Shannon energy envelope and Hilbert transform. The detection algorithm extracts the Shannon energy envelope of the preprocessed ECG signal, performs Hilbert transform on the envelope signal, then detects the suspected [Formula: see text]-peaks on the envelope by detecting the position of zero pass and screens the real [Formula: see text]-peaks by using a combination of ECG refractory period and backtracking mechanism. The proposed detection algorithm is validated using MIT-BIH Arrhythmia Database, and achieves the average detection accuracy of 99.69%, sensitivity of 99.81% and positive predictivity of 99.88%. Experimental results show that the proposed detection algorithm can still detect QRS complex correctly under complex interference, and the performance of the algorithm is hardly affected.

ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope and deterministic learning

ECG arrhythmia classification based on variational mode decomposition, Shannon energy envelope and deterministic learning

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm.

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15-30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning

Heart valve disorders (HVDs) are the major causes of cardiovascular diseases (CVD), which may be detected at the early stage using routine auscultation examination. The phonocardiogram (PCG) is a graphical representation of the physiological condition of the heart, which differs with respect to heart diseases. It is closely related to valve functionality which provides vital information for the diagnosis of CVD. However, visual inspection of PCG is tedious and error-prone, which makes it necessary and urgent to develop an automated system for the detection of HVDs with PCG recordings. In the present study we propose a novel method for the identification and classification of normal and abnormal non-segmented PCG recordings based on hybrid signal processing tools and deterministic learning theory. First, PCG signal and its first derivative are decomposed into a set of frequency subbands with a number of decomposition levels by using the tunable Q-factor wavelet transform method. Second, fast and adaptive multivariate empirical mode decomposition decomposes the subbands of the PCG signal and its derivative into scale-aligned intrinsic mode components (IMFs). The first two IMFs are extracted, which contain most of the energy of the PCG signal and its derivative and are considered to be the predominant IMFs. Third, Shannon energy is used to extract the characteristic envelope of predominant IMFs. The properties associated with the nonlinear PCG system dynamics are preserved. They are utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify PCG system dynamics between normal and abnormal PCG signals based on deterministic learning theory. Finally, experiments have been carried out on a publicly available PCG database to verify the effectiveness of the proposed method, which include two types of classification, one for binary classification (normal vs. abnormal) and the other for multi-class classification (normal vs. aortic stenosis vs. mitral regurgitation vs. mitral stenosis vs. mitral valve prolapse). The overall average accuracy for binary, four-class and five-class classification are reported to be 97.75, 98.69 and 98.48%, respectively. The proposed method has obtained the highest overall accuracy in comparison to other state-of-the-art approaches using the same database, which can serve as an assistant diagnostic tool for the automated detection of HVDs in clinical applications.

An innovative method based on Shannon energy envelope and summit navigation for detecting R peaks of noise stress test signals

In recent decades, there has been an increased demand for the processing of electrocardiogram (ECG) signals because of its significant role in diagnosing cardiac diseases. The QRS complex is the dominant feature of the ECG signal. The detection of QRS complexes is thus an essential part of almost any ECG signal processing systems. This paper presents a developed QRS complex detection method using dominant peak extraction and Shannon energy envelope for useful ECG signal analysis. The algorithm is divided into three main stages: pre-processing, searching for dominant peaks, and removing false R peaks. The proposed algorithm is validated in static ECG recordings from the MIT-BIH Arrhythmia Database (MITDB) and noise-contaminated ECG stress tests from the Glasgow University Database (GUDB), separately. The method compares favorably with conventional and recently published results of many QRS detection algorithms on the same MITDB. Subsequently, valuable performance coefficients are also found on the GUDB. The average detection accuracy of finding R peaks exceed 99% on both the databases, especially for cardiac stress test records with high interference levels. The method enables a highly effective ECG signal processing tool under various noises, artifacts, abnormalities, and morphologies.

Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals

Phonocardigraphy (PCG) is the graphical representation of heart sounds. The PCG signal contains useful information about the functionality and the condition of the heart. It also provides an early indication of potential cardiac abnormalities. Extracting cardiac information from heart sounds and detecting abnormal heart sounds to diagnose heart diseases using the PCG signal can play a vital role in remote patient monitoring. In this paper, we have combined different signal processing techniques and a deep learning method to denoise, compress, segment, and classify PCG signals effectively and accurately. First, the PCG signal is denoised and compressed by using a multi-resolution analysis based on the Discrete Wavelet Transform (DWT). Then, a segmentation algorithm, based on the Shannon energy envelope and zero-crossing, is applied to segment the PCG signal into four major parts: the first heart sound (S1), the systole interval, the second heart sound (S2), and the diastole interval. Finally, Mel-scaled power spectrogram and Mel-frequency cepstral coefficients (MFCC) are employed to extract informative features from the PCG signal, which are then fed into a classifier to classify each PCG signal into a normal or an abnormal signal by using a deep learning approach. For the classification, a 5-layer feed-forward Deep Neural Network (DNN) model is used, and overall testing accuracy of around 97.10% is achieved. Besides providing valuable information regarding heart condition, this signal processing approach can help cardiologists take appropriate and reliable steps toward diagnosis if any cardiovascular disorder is found in the initial stage.

Classification and evaluation of the severity of tricuspid regurgitation using phonocardiogram

Cardiovascular disorders (CVDs) have attracted a great deal of attention given that they are one of the most common causes of mortality every year. With early diagnoses, CVDs can be controlled before reaching the critical stage. Listening to heart sounds has been a common way of evaluating the cardiovascular system. The sound signal generated by the heart’s mechanical activity provides useful information on the operation of the heart valves. Due to human hearing limitations as well as transient and non-stationary nature of heart sounds, diagnosis based on auscultation is difficult and requires training, repetition, and expertise. The purpose of this study is to diagnose and assess the severity of tricuspid regurgitation (TR), a disease that most adults suffer from without being aware of it, by using phonocardiogram (PCG) and without the employment of auxiliary tools such as the electrocardiogram (ECG). To this end, PCG signal is first preprocessed using Shannon energy envelope (SEE) and Hilbert-transform (HT). The Mel Frequency Cepstral Coefficient (MFCC) is used along with the wavelet transform method for feature extraction; superior features are then selected with the genetic algorithm (GA). The accuracy of the proposed method was 98.78 ± 0.95% and kappa was 98.12 ± 1.48% with k-nearest neighbors (KNN) classifier.

Efficient FPGA‐based VLSI architecture for detecting R‐peaks in electrocardiogram signal by combining Shannon energy with Hilbert transform

Electrocardiogram (ECG) is a critical application in light of R-peak detection. The R-peaks are impacted by some QRS complex and noises in the current method. Basic testability, snappier execution, and confirmation choices are accomplished by the utilization of the field programmable gate array (FPGA). But, FPGA execution gives less accuracy. To battle execution trouble, another R-peak detector is proposed and relying upon the propelled peak finding logic, which incorporates a Bandpass filter and first-order differentiation process, which are done in the primary phase of the strategy. The noises in the input ECG signal are decreased in the first stage. In the subsequent stage, the smooth Shannon energy envelope (SEE) is acquired by utilizing SEE extraction and the zero phases filtering process. The false R peak is stifled by proposing a Hilbert transform (HT) in the third stage. The HT requires more hardware space and high-power utilization in the current technique. Due to this reason, Real valued Fast Fourier Transform (RFFT) and Inverse RFFT (IRFFT) techniques are proposed in the HT. In the fourth stage, R-peak acknowledgment is evaluated using Massachusetts Institute of Technology, Beth Israel Hospital (MIT-BIH) arrhythmia database and produced the average accuracy of 99.86%, sensitivity of 99.95% and the positive predictivity of 99.90%.

A Novel R Peak Detection Method for Mobile Environments

With the development of the portable electrocardiogram (ECG) sensor, R peaks can be monitored during various physical activities in mobile environment. However, such ECG signals contain real-world noise, complicating the accurate detection of R peak. In this paper, we propose a novel approach for R peak detection in ECG signals with real-world noise by using signal envelop filtering (SEF) and Shannon energy envelope with the Savitzky–Golay filter (SEE-SG). SEF was designed to diminish abrupt peaks due to movement by adjusting its amplitude based on an automatically determined threshold. SEE-SG was used to enhance the QRS-complex. We first designed the experimental protocol for physical activity (sit, walk, and ascend), namely, normal ECG in a physical activity database, and validated our method by using the ECG data of 23 subjects. Moreover, to avoid overtuning on our database, we validated the proposed method using two public databases: the MIT-BIH QT database (QTDB) and Noise Stress Test Database (NSTDB). The experimental results show promising performance on QTDB (SE and P+: 99.96% and ER: 0.08%), NSTDB (SE: 94.07%, P+: 93.45, and ER: 12.57%), and NPADB (SE: 99.95%, P+: 99.78%, and ER: 0.27%). We also assessed our method by comparing the absolute differences of R–R intervals and heart rates between actual and detected R peaks. In conclusion, as the proposed method achieved noise robustness and reliable performance on R peak detection during physical activities, it can be used to monitor a user’s ECG and cardiac health information accurately in daily life.

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

An efficient wavelet-based automated R-peaks detection method using Hilbert transform

Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.

Heart sound classification based on scaled spectrogram and partial least squares regression

Abstract Phonocardiogram (PCG) signal analysis is an effective and convenient method for the preliminary diagnosis of heart disease. In this study, a scaled spectrogram and partial least squares regression (PLSR) based method was proposed for the classification of PCG signals. Proposed method is mainly comprised of four stages, namely as being heart cycle estimation, spectrogram scaling, dimension reduction and classification. At the heart cycle estimation stage, the short time average magnitude difference of the Shannon energy envelope is applied. Then the spectrogram of the obtained heart cycle is calculated for feature extraction. However, the sizes of the spectrograms between different PCG signals are usually not the same. In order to overcome the difficulty of direct comparison, the bilinear interpolation is used for the spectrogram to get the scaled spectrogram with a fixed size. Nevertheless, the scaled spectrogram contains a large quantity of redundant and irrelevant information. To extract the most relevant features from the scaled spectrogram, we adopt the PLSR to reduce the dimension of the scaled spectrograms. Since PLSR has the advantage of using the category information during the dimension reduction process, the extracted features are more discriminative. Then the classification results are obtained via support vector machine (SVM). The proposed method is evaluated on two public datasets offered by the PASCAL classifying heart sounds challenge, and the results are compared to those obtained using the best methods in the challenge, thereby proving the effectiveness of our method.

An improved method for R-peak detection by using Shannon energy envelope

R-peaks in electrocardiogram (ECG) play a vital role in diagnosis of heart rhythm irregularities and also estimating heart rate variability. However, almost all existing R-peak detectors suffer from the non-stationary of both QRS morphology and noise. To overcome these difficulties, we propose a four-stage improved method to detect R-peak using Shannon energy envelope. In the first stage, noise is suppressed and QRS complex is enhanced by using band pass filter, first order differentiation, and amplitude normalization. In the second stage, Shannon energy envelope is extracted. In the third stage, peak is estimated without considering any threshold amplitude. In the final stage, true R-peaks are detected. Our proposed R-peak detection method is validated using 48 first channel ECG records of the MIT-BIH arrhythmia database with the accuracy of 99.84%, sensitivity of 99.95% and positive predictability of 99.88%. Our proposed method outperforms other well-known methods in case of pathological ECG signals.

Automated detection of the onset and systolic peak in the pulse wave using Hilbert transform

Abstract Pulse transit time (PTT) and pulse wave velocity (PWV) are the markers most widely used to evaluate the vascular effects of aging, hypertension, arterial stiffness and atherosclerosis. To calculate these markers it is necessary to determine the location of the onset and systolic peak of the arterial pulse wave (APW). In this paper, a method employed for electrocardiography (ECG) R peak detection, with a slight modification, is applied for both the onset and systolic peak detections in APW. The method employs Shannon energy envelope (SEE) estimator, Hilbert transform (HT) and moving average (MA) filter. The minimum value and the positive zero-crossing points of the odd-symmetry function of the HT correspond to the locations of the onset and systolic peak respectively. The algorithm was evaluated using expert's annotations, with 10 records of 5 min length and different signal-to-noise ratios (15, 12 and 9 dB) and achieved a good performance and precision. When compared to, expert's annotation, the algorithm detected these fiducial points with average sensitivity, positive predictivity and accuracy of 100% and presented errors less than 10 ms. In APW signals contaminated with noise in both cases the relative error is less than 2% respect to pulse wave periods of 800 ms. The performance of algorithm was compared with both foot approximation and adaptive threshold methods and the results show that the algorithm outperforms theses reported methods with respect to manuals annotation. The results are promising, suggesting that the method provides a simple but accurate onset and systolic peak detection and can be used in the measurement of pulse transit time, pulse wave velocity and pulse rate variability.

Automated Assessment of Cardiac Autonomic Function by Means of Deceleration Capacity from Noisy, Nonstationary ECG Signals: Validation Study

Assessment of heart rate variability by means of deceleration capacity (DC) provides a noninvasive probe of cardiac autonomic activity. However, clinical use of DC is limited by the need of manual review of the ECG signals to eliminate artifacts, noise, and nonstationarities. To validate a novel approach to fully automatically assess DC from noisy, nonstationary signals We analyzed 100 randomly selected ECG tracings recorded for 10 minutes by routine monitor devices (GE DASH 4000, sample size 100 Hz) in a medical emergency department. We used a novel automated R-peak detection algorithm, which is mainly based on a Shannon energy envelope estimator and a Hilbert transformation. We transformed the automatically generated RR interval time series by phase-rectified signal averaging (PRSA) to assess DC of heart rate (DCauto ). DCauto was compared to DCmanual , which was obtained from the same manually preprocessed ECG signals. DCauto and DCmanual showed good correlation and agreement, particularly if a low-pass filter was implemented into the PRSA algorithm. Correlation coefficient between DCauto and DCmanual was 0.983 (P < 0.0001). Average difference between DCauto and DCmanual was -0.23±0.49 ms with limits of agreement ranging from -1.19 to 0.73 ms. Significantly lower correlations were observed when a different R-peak detection algorithm or conventional heart rate variability (HRV) measures were tested. DC can be fully automatically assessed from noisy, nonstationary ECG signals.