A single-lead ECG enhancement algorithm using a regularized data-driven filter

Ensemble empirical mode decomposition based feature enhancement of cardio signals

This paper presents an application of ensemble empirical mode decomposition method for enhancement of specific biological signal features. The application for two types of cardiological signals is presented in this article. Detection of fiducial points is a routine task for analyzing these signals. In a clinical situation, cardiological signals are usually corrupted by artifacts and finding exact time instances of various fiducial points is a challenge. Filtering approach for signal to noise ratio enhancing is traditionally and widely used in clinical practice. Methods, based on filtering, however, have serious limitations when it is necessary to find compromise between noise suppression and preservation of signal features. The proposed method uses ensemble empirical mode decomposition in order to suppress noise or enhance specific waves in the signal. Performance of the method was estimated by using clinical electrocardiogram and impedance cardiogram signals with synthetic baseline-wander, power-line and added Gaussian noise. In electrocardiogram application, an average estimation error of QRS complex length was 2.06-4.47%, the smallest in comparison to the reference methods. In impedance cardiogram application, the proposed method provided the highest cross-correlation coefficient between original and de-noised signal in comparison to reference methods. When the signal to noise ratio of the input signal was -12 dB, the method provided signal to error ratio of 33 dB in this case. The proposed method is adaptive to template and signal itself and thus could be applied to other non-stationary biological signals.

Electrocardiogram data mining based on frame classification by dynamic time warping matching

This paper presents an electrocardiogram (ECG) data mining scheme based on the ECG frame classification realised by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar non-stationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DTW matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-min ECG recording.

A robust approach toward recognizing valid arterial-blood-pressure pulses

We propose a projection method based on singular value decomposition (SVD) to validate arterial blood pressure (ABP) signal in order to avoid artifacts and noise in subsequent processing. The projection has been done on 567 validated ABP beats collected from 51 patients hospitalized in University of California, Los Angeles Medical Center. Then, we compare the performance of the proposed projection method with that of a previously developed algorithm, signal abnormality index (SAI), which is a value- and trend-based approach, and has shown to be effective in cleaning the ABP waveforms. The testing dataset consists of 1336 ten-second ABP segments (18 472 ABP beats) of both valid and invalid pulses selected randomly from multiparameter intelligent monitoring for intensive care II database. The proposed projection approach that validates the signal based on the shape of the waveform achieves a true positive rate (TPR) of 99.06%, 5.43% higher than that of the SAI, and a false positive rate (FPR) of 7.69%, 17.38% lower than that of SAI. Integration of some of the SAI-value-based abnormality conditions to the validation process of SVD-based method can further improve the performance by reducing the FPR to 3.92%, while keeping the TPR at the high rate of 99.05%.

A subspace decomposition approach toward recognizing valid pulsatile signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for the prediction of critical intracranial and cerebrovascular pathophysiological variations during the management of many neurological disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is capable of enhancing the quality of ICP signals, recognizing valid (not contaminated with noise or artifacts) ICP pulses and designating the locations of the three ICP sub-peaks in a pulse. This paper extends the algorithm by proposing a singular value decomposition (SVD) technique to replace the correlation-based approach originally utilized in recognizing valid ICP pulses. The validation of the proposed method is conducted on a large database of ICP signals built from 700 h of recordings from 67 neurosurgical patients. A comparative analysis of the valid ICP recognition using the proposed SVD technique and the correlation-based method demonstrates a significant improvement in terms of (1) accuracy (61.96% reduction in the false positive rate while keeping the true positive rate as high as 99.08%) and (2) computational time (91.14% less time consumption), all in favor of the proposed method. Finally, this SVD-based valid pulse recognition can be potentially applied to process pulsatile signals other than ICP because no proprietary ICP features are incorporated in the algorithm.

Robust projective filtering of time-warped ECG beats

In this paper a new version, more immune to noise, of the nonlinear projective filtering is presented. The method employs an algorithm of robust principal component analysis to signal subspaces construction and as a result it achieves high performance in real electromyographic noise environment. Two aspects of the method's action are investigated: its ability to suppress noise and its influence on the precision of the QT interval measurement. Then the method influence on evaluation of the beat-to-beat variability of the repolarization duration is presented. A comparison to the previous versions of the nonlinear projective filtering and to the classical linear one is carried out.

Pulse onset detection using neighbor pulse-based signal enhancement

Detecting onsets of cardiovascular pulse wave signals is an important prerequisite for successfully conducting various analysis tasks involving the concept of pulse wave velocity. However, pulse onsets are frequently influenced by inherent noise and artifacts in signals continuously acquired in a clinical environment. The present work proposed and validated a neighbor pulse-based signal enhancement algorithm for reducing error in the detected pulse onset locations from noise-contaminated pulsatile signals. Pulse onset was proposed to be detected using the first principal component extracted from three adjacent pulses. This algorithm was evaluated using test signals constructed by mixing arterial blood pressure, cerebral blood flow velocity and intracranial pressure pulses recorded from neurosurgical patients with white noise of various levels. The results showed that the proposed pulse enhancement algorithm improved (p<0.05) pulse onset detection according to all three different onset definitions and for all three types of pulsatile signals as compared to results without using the pulse enhancement. These results suggested that the proposed algorithm could help achieve robustness in pulse onset detection and facilitate pulse wave analysis using clinical recordings.