Time-Frequency Analysis.

An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique

The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.

QRS Complex Detection Using STFT, Chaos Analysis, and PCA in Standard and Real-Time ECG Databases

Biomedical Instrumentation and Measurements

An Electrocardiogram (ECG) is a primary and most prevalent non-invasive test performed on the subjects’ (i.e. patients’) with suspected heart problems. It helps in diagnosing important cardiological status of the subject’s heart i.e. normal or abnormal by investigating rhythm of the heart. This interpretation is not always possible using naked eyes, especially for minute aberrations. Therefore, advanced feature extraction methods are required for investigating these minute differences that might be a challenge to be detected by the human eye. Hence, a critical review of feature extraction techniques presented in this paper will help the researchers to make an informed choice of an appropriate technique for developing efficient methodologies for ECG signal processing.

A Critical Review of Feature Extraction Techniques for ECG Signal Analysis

A Method for QRS Delineation Based on STFT Using Adaptive Threshold

Suppression of interference from narrowband frequency signals play vital role in many signal processing and communication applications. A transform based method for suppression of narrow band interference in a biomedical signal is proposed. As a specific example Electrocardiogram (ECG) is considered for the analysis. ECG is one of the widely used biomedical signal. ECG signal is often contaminated with baseline wander noise, powerline interference (PLI) and artifacts (bioelectric signals), which complicates the processing of raw ECG signal. This work proposes an approach using Ramanujan periodic transform for reducing PLI and is tested on a subject data from MIT-BIH Arrhythmia database. A sum (E) of Euclidean error per block (e i ) is used as measure to quantify the suppression capability of RPT and notch filter based methods. The transformation is performed for different lengths (N), namely 36, 72, 108, 144, 180. Every doubling of N-points results in 50% reduction in error (E).

Removal of narrowband interference (PLI in ECG signal) using Ramanujan periodic transform (RPT)

ECG analysis is used significantly in diagnosis, and biometrics. QRS complex detection is an important step in any application involving ECG signal. In this work, a novel approach for QRS complex detection based on chirplet transform is proposed. The QRS detection algorithm proposed in this work mainly consists of four steps. A preprocessing step to remove power line interference, computation of chirplet transform, an adaptive threshold technique for detecting possible QRS complex peaks, and followed by a decision making step. The performance of proposed algorithm for QRS complex detection is evaluated on MIT-BIH database and compared with the results of different algorithms in the state of art. The performance of the algorithm is comparable with the state of art of QRS complex detection.

A novel approach for QRS delineation in ECG signal based on chirplet transform

Ramanujan Periodic Transform (RPT) is a newly emerging transformation technique in the field of signal processing. It uses an integer bases (obtained from Ramanujan sum) for transformation. A recorded ECG signal often contains artifacts (bioelectric signals) namely, baseline wander, muscle artifacts (EMG-Electromyogram), motion artifacts, powerline interference (PLI) and its harmonics. With certain precautions during signal recording we can avoid both muscle and motion artifacts. The other noises can be reduced by preprocessing of the recorded ECG signal. In this paper, RPT is used for preprocessing, to reduce baseline wander noise, PLI and its harmonics. The proposed methodology is tested on a record from MIT-BIH Arrhythmia database for different block sizes. A sum (E) of Euclidean errors per block (e i -ith block), is used as a measure to compare the results of RPT with notch filter technique. From the results, the RPT is reducing the noise with minimum error (E), when compared with notch filter technique.

Joint reduction of baseline wander, PLI and its harmonics in ECG signal using Ramanujan Periodic Transform

Ramanujan Periodic Transform (RPT) is the newly emerging transform to identify periodicities in the given data RPT represents the given finite length sequence into a weighted linear combination of signals from Ramanujan subspaces RPT has the inability of handling the frequency components with in the Ramanujan subspace This is due to the basis function (Ramanujan sum) used in RPT To overcome this, a new mixed basis representation is proposed which uses both the sequences from Ramanujan subspace and complex exponentials as basis and both the basis are orthogonal to each other Using this mixed basis representation, the problem of removing Power Line Interference (PLI) in ECG data is addressed The methodology is tested on the MIT-BIH Arrhythmia database and the obtained results are competitive when compared with other techniques

Basheeruddin Shah Shaik

Papers

A Method for QRS Delineation Based on STFT Using Adaptive Threshold

Removal of narrowband interference (PLI in ECG signal) using Ramanujan periodic transform (RPT)

A novel approach for QRS delineation in ECG signal based on chirplet transform

Joint reduction of baseline wander, PLI and its harmonics in ECG signal using Ramanujan Periodic Transform

Ramanujan and DFT mixed basis representation for removal of PLI in ECG signal